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Review

Molecular Markers to Predict Prognosis and Treatment Response in Uterine Cervical Cancer

by
Maximilian Fleischmann
1,*,
Georgios Chatzikonstantinou
1,
Emmanouil Fokas
1,2,3,4,
Jörn Wichmann
5,
Hans Christiansen
5,
Klaus Strebhardt
3,6,
Claus Rödel
1,2,3,4,
Nikolaos Tselis
1,† and
Franz Rödel
1,2,3,4,†
1
Department of Radiation Oncology, University Hospital Johann Wolfgang Goethe University, 60590 Frankfurt, Germany
2
German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany
3
German Cancer Consortium (DKTK), Partner Site Frankfurt am Main, 60590 Frankfurt, Germany
4
Frankfurt Cancer Institute, 60590 Frankfurt, Germany
5
Institute for Radiation Therapy and Special Oncology, Hannover Medical School, 30625 Hannover, Germany
6
Department of Gynecology and Obstetrics, University Hospital, 60590 Frankfurt, Germany
*
Author to whom correspondence should be addressed.
Both authors contributed equally.
Cancers 2021, 13(22), 5748; https://doi.org/10.3390/cancers13225748
Submission received: 27 October 2021 / Revised: 12 November 2021 / Accepted: 14 November 2021 / Published: 17 November 2021
(This article belongs to the Special Issue Molecular Pathogenesis of Cervical Cancer)

Abstract

:

Simple Summary

Despite the implementation of efficient screening and vaccination programs, uterine cervical cancer remains a leading cause of cancer-related mortality in women worldwide. New therapeutic approaches have so far failed to improve treatment response and prognosis significantly, especially in patients with recurrent disease or metastases. Further, robust molecular markers to predict therapy response and survival are scarce and their routine use is limited in clinical practice. Accordingly, there is an urgent need to identify and establish molecular markers to predict therapy response and clinical outcome to improve treatment and survival in cervical cancer.

Abstract

Uterine cervical cancer is one of the leading causes of cancer-related mortality in women worldwide. Each year, over half a million new cases are estimated, resulting in more than 300,000 deaths. While less-invasive, fertility-preserving surgical procedures can be offered to women in early stages, treatment for locally advanced disease may include radical hysterectomy, primary chemoradiotherapy (CRT) or a combination of these modalities. Concurrent platinum-based chemoradiotherapy regimens remain the first-line treatments for locally advanced cervical cancer. Despite achievements such as the introduction of angiogenesis inhibitors, and more recently immunotherapies, the overall survival of women with persistent, recurrent or metastatic disease has not been extended significantly in the last decades. Furthermore, a broad spectrum of molecular markers to predict therapy response and survival and to identify patients with high- and low-risk constellations is missing. Implementation of these markers, however, may help to further improve treatment and to develop new targeted therapies. This review aims to provide comprehensive insights into the complex mechanisms of cervical cancer pathogenesis within the context of molecular markers for predicting treatment response and prognosis.

1. Introduction

Transient infections with human papillomavirus (HPV) are common, with a lifetime probability ranging between 50% and 95% [1]. Persistent infections with oncogenic high-risk HPV16/18 subtypes frequently progress to cervical intraepithelial neoplasia (CIN), a precursor lesion to invasive uterine cervical cancer [2,3]. As a consequence of effective screening and vaccination programs, cervical cancer is largely preventable, although vaccination rates remain low and only a minority of women have access to efficient health care services [4]. Accordingly, more than half a million women worldwide are diagnosed with cervical cancer each year, with the majority of deaths occurring in low- and middle-income countries, indicating substantial global disparities [5,6]. The treatment of cervical cancer can be roughly divided into two central therapy options. For localized disease, without the detection of lymph node metastases or the presence of risk factors, surgery is the preferred treatment modality. The scope of these curatively intended interventions ranges from fertility preserving conization to radical hysterectomy or total mesometrial resection (TMMR), including extensive pelvic and paraaortic lymphadenectomy. In cases of locally advanced cervical cancer (Fédération Internationale de Gynécologie et d’Obstétrique (FIGO) ≥ IB3), definitive chemoradiotherapy (CRT) followed by brachytherapy (BT) is the standard of care [7]. BT is an integral part of definitive treatment regimens for patients with locally advanced cervical cancer [8]. It delivers a highly conformal dose distribution of radiation, maximizes local control and minimizes toxicities by adequately covering the remaining extent of disease and optimally sparing adjacent organs at risk [9,10,11]. Concurrent chemotherapy has significantly improved the survival of cervical cancer patients compared to radiotherapy alone [12,13]. Platinum-based chemotherapy regimens are, therefore, generally preferred for patients who are suitable for this as first-line treatment [14,15]. Further, a combination of cisplatin, paclitaxel or topotecan plus the VEGF (vascular endothelial growth factor) inhibitor bevacizumab showed a significantly improved the survival rate, but was also associated with increased toxicity [16,17]. More recently, immunotherapies have transformed the management of many solid tumors and is constantly evolving. In 2018, the American Food and Drug Administration (FDA) approved programmed death 1 (PD-1) inhibitor pembrolizumab for patients with recurrent or metastatic cervical cancer whose tumors express PD-L1 (combined positive score (CPS) ≥1) and with disease progression after chemotherapy [18]. Pending new evidence, numerous immune checkpoint inhibitors are currently being investigated in various settings for the treatment of cervical cancer, including the CALLA (durvalumab), NCT02635360 (pembrolizumab), and NRG-GY017 (atezolizumab) trials. Moreover, therapeutic vaccination strategies (e.g., NCT02853604) and adoptive cell therapies (e.g., NCT03108495) are currently being tested [19]. In this regard, the FDA recently approved the antibody-drug conjugate (ADC) tisotumab vedotin against tissue factor and monomethyl auristatin E (MMAE) for recurrent or metastatic cervical cancer after efficacy, and safety was demonstrated in the NCT03438396 multicenter, open-label, single arm, phase 2 trial (innovaTV 204/GOG-3023/ENGOT-cx6) [20]. Despite these advances, survival rates have not improved significantly over the past two decades and especially the prognosis for women with persistent, recurrent or metastatic disease remains poor. In addition, valid and easily accessible molecular markers for risk stratification and to predict treatment response and survival remain elusive. These markers, however, may cover a prerequisite to unravel the molecular pathogenesis and heterogeneity of cervical cancer and will be crucial to further develop novel therapeutic targets and treatment approaches.

2. Clinical Parameters to Predict Outcome and Response

Surgical procedures are the standard of care for women with early-stage disease and low-risk features [21]. For those patients, clinical characteristics and postoperative surgicopathologic risk factors were identified to predict local and distant control. Tumor size, stromal invasion, lymphovascular space involvement (LVSI), pathologically confirmed lymph node metastases, extension into parametrial tissue or positive surgical margins are considered to predict an intermediate or high risk of recurrence after primary surgery (Figure 1) [22,23]. According to these clearly interrelated characteristics, Sedlis’ respectively Peters’ criteria (Table 1) were developed to decide whether postoperative radiotherapy with or without systemic therapy is required to reduce the risk of recurrence [24,25]. For intermediate risk patients, adjuvant RT was associated with a 46% reduction of recurrence and a significantly lower risk of disease progression at 5 years, although the improvement in overall survival did not reach statistical significance [26,27]. Patients with more than three positive lymph nodes after surgery are more likely to have extra-pelvic recurrence than patients without lymph node involvement or less than three positive lymph nodes [28]. Therefore, cisplatin-based CRT following radical hysterectomy was associated with significantly improved progression-free survival (63% vs. 80%, HR 2.01, p = 0.003) and overall survival (71% vs. 81%, HR 1.96, p = 0.007) compared to RT alone, as demonstrated by the GOG 109 landmark trial in a cohort of 268 women with high-risk cervical cancer [24].
Importantly, prognostic factors evaluated for women with locally advanced cervical cancer treated with concurrent CRT were similar to those after primary surgery. Tumor size, FIGO stage, pelvic or paraaortic lymph node involvement, non-squamous-cell carcinoma, performance status and ethnical race were significantly correlated with survival [29,30,31,32,33]. Total treatment time should not be longer than eight weeks [34,35]. Moreover, the omission of brachytherapy has a stronger negative effect on survival than the exclusion of chemotherapy [36]. To achieve a maximum of pelvic control, the combined prescription EQD2 of external beam radiotherapy (EBRT) and brachytherapy should be greater than 80 Gy, while higher doses can be considered for tumors with poor response to EBRT or extensive disease [37,38,39]. Meanwhile, the use of intensity modulated radiotherapy (IMRT) generated lower rates of acute and late toxicity and favorable disease-specific survival [40,41,42].

Hematological Parameters

Multiple hematological markers are reported to predict response to CRT as easily accessible and feasible biomarkers. In particular, lower pre-treatment and nadir hemoglobin levels were discussed to negatively impact on treatment response and survival [43]. In this regard, anemia is linked to tumor hypoxia and neo-angiogenesis, which is associated with an impaired local tumor control [44]. However, in a retrospective analysis of 2454 cervical cancer patients by Bishop et al., the impact of anemia was relativized. Anemia was not confirmed as an independent prognostic factor and only hemoglobin levels below 10 g/dL during RT were associated with an inferior disease-specific survival [45]. Until now, data about the predictive value of anemia remain inconclusive.
In addition, further cell lines of the blood count were evaluated. Cancer-related systemic inflammation is associated with impaired outcomes in patients with malignant diseases [46]. Tumor-induced neutrophilia and thrombocytosis, which may be considered a sign of systemic inflammation, and relative lymphocytopenia, which occurs as part of the antitumor immune response, are frequently observed [47]. In a meta-analysis, an elevated pretreatment neutrophil to lymphocyte ratio was confirmed as an independent predictor of a decreased OS and PFS, regardless of stage or primary treatment [48]. In addition, a systemic immune inflammation index based on peripheral neutrophil, lymphocyte and platelet counts was established in cervical cancer patients after primary surgery. An increased immune-inflammation index was significantly correlated with poorer OS regardless of FIGO stage [49].
Moreover, C-reactive acute phase protein (CRP), an inflammatory marker, and serum albumin levels indicative for liver function and malnutrition were associated with impaired immune response in cervix cancer patients [50]. The pretreatment CRP/albumin ratio was further identified as a predictor of survival in two studies. He et al. further concluded an increased predictive value from a combination of CRP/albumin ratio and neutrophil to lymphocyte ratio [51,52]. Low serum albumin levels were associated with a low nutritional index and predicted a poor prognosis in a cohort of 131 cervical cancer patients treated with RT or CRT [53]. The negative impact of a low nutritional index was further confirmed in a larger and more recent study [54]. Sarcopenia resulting from malnutrition, however, does not affect survival before initiation of treatment, whereas significant loss of skeletal muscle during CRT is associated with an impaired outcome [55,56,57].
Finally, elevated pretreatment levels of serum squamous cell carcinoma antigen (SCC Ag) were associated with a larger tumor size, the involvement of lymph nodes, LVSI and deep stromal invasion [58,59,60,61] and were confirmed to significantly correlate with an inferior survival [62].

3. Human Papillomavirus in Cervical Cancer

Cervical cancer is a prime example of a malignancy caused by infection with human papillomaviruses (HPV). Of at least 13 oncogenic high-risk subtypes from the same phylogenetic lineage, HPV16 and HPV18 are the predominant subtypes associated with disease onset and progression [63].

3.1. HPV-DNA Integration

The integration of HPV into the host genome causes a linearization of the viral genome and results in a partial or complete deletion of the viral genes E1 and E2. This further suggests that both integrated viral DNA and its episomal form can be present [64]. The status of the viral E2 gene was evaluated for its influence on therapy response. As a regulator of viral genome replication and RNA transcription, loss of E2 function after viral integration leads to a more radioresistant phenotype by potentiating E6 and E7 expression and reducing pro-apoptotic signaling [65]. In line with this, there is evidence suggesting a favorable prognosis in patients with HPV 16+ tumors and an intact E2 gene [66,67], while episomal expression of E2, E4 and E5 results in enhanced proliferation and increased susceptibility to induction of cancer [68]. In a prospective cohort of 272 HPV+ cervical cancer patients enrolled in the BioRAIDs study, several integration signatures of the HPV genome were identified. The most frequent integration site was the MACROD2 (mono-ADP Ribosylhydrolase 2) gene locus, followed by TP63 and MIPOL1/TTC6 loci. Although there is a lack of evidence for the impact of the integration within MACROD2, MACROD2 deletions are associated with impaired PARP1 (poly(ADP-ribose)polymerase 1) activity and chromosomal instability in other entities. Notably, the integration of HPV DNA was much more frequent in cervical cancer compared with anal cancer and was associated with a low HPV copy number (<4, ratio of the number of HPV reads over the control human kallikrein-3 (KLK3) gene) and worse progression-free survival. Episomal forms of HPV DNA were associated with a high HPV number and phosphatidylinositol-4,5-bisphosphat-3-kinase (PIK3) mutations [69].

3.2. Prognostic Impact of HPV Viral Load

Determining the predictive value of the HPV viral load was the objective of several studies. In locally advanced cervical cancer, a low HPV viral load was associated with an impaired distant metastasis free (DMFS) and overall survival (OS), while the combined analysis HPV viral load and tumor size were superior for predicting OS [70]. These findings are consistent with the findings by Kim et al., who reported worse disease-free survival (DFS) in 169 women with low HPV viral load treated with CRT [71]. A low and persistent HPV viral load was further correlated significantly with poor local recurrence-free survival (LRFS) in another study covering 156 cervical cancer patients treated with CRT or radiotherapy (RT) alone [72]. Furthermore, in a cohort of 520 woman treated with surgery or CRT, a low HPV viral load predicted poor prognosis irrespective of treatment [73]. In contrast, at the tissue and cellular levels, Cao et al. hypothesized that a high HPV viral load influences the tumor microenvironment (TME) towards a more immunosuppressive milieu with an increased proportion of FOXP3-positive tumor-infiltrating regulatory T-lymphocytes (TILs), contributing to an impaired survival rate [74].

3.3. Genetic Alterations, Immune Responses and Angiogenesis in HPV+ Cervical Cancer

Nevertheless, a persistent HPV infection is not sufficient to induce carcinogenesis per se. Further genetic and epigenetic alterations and an interplay between infected cells, the host immune response and the TME are required to promote malignant transformation and propagation [75,76]. A recent genome-wide association study provided new evidence for a genetic disposition and susceptibility of cervical cancer. Bowden et al. identified six variants in the PAX8 (paired-box-protein 8), CLPTM1L and HLA (human leukocyte antigen) genes that were associated with increased risk of CIN and cervical cancer. These variants are associated with disruptions in multiple pro-apoptotic and immune function pathways [77] and are consistent with another genome-wide association study, which identified multiple loci affecting immune pathways of antigen presentation and immune checkpoints associated with increased risk of cervical cancer development [78]. Furthermore, variants of major histocompatibility complex (MHC) alleles are associated with increased risk of persistent HPV infection and cervical cancer [79].
The innate and adaptive immune response is crucial in clearing HPV infections and in surveillance of carcinogenesis and tumor progression [80,81]. Accordingly, there are several immune evasion mechanisms and immunomodulatory effects exploited by HPV oncoproteins that disrupt pivotal anticancer immune response pathways. In vitro and in vivo analyses of oncoproteins E6 and E7 revealed Toll-like receptor 9 (TLR9) downregulation and suppression of C-X-C motif chemokine ligand CXCL14, which in turn reduced the local immune response and the recruitment of antigen-presenting cells, natural killer (NK) and T cells [82,83]. A local immune response is further compromised by E7 targeting the stimulator of interferon gene (STING) pathway, which senses against intracellular DNA and activates the production of pro-inflammatory cytokines, such as interferon gamma (IFNγ) [84,85]. E6 and E7 interact with multiple IFN-receptor pathways and impede the phosphorylation of signal transducer and activators of transcription 1 (STAT1), STAT2 and other JAK/STAT pathways, which are critical for IFN signaling as well as proliferation and invasion [86]. Furthermore, activation and recruitment of immune components such as Langerhans cells, dendritic cells, tumor-associated macrophages, CD4+ and CD8+ lymphocytes are enhanced by HPV fostering of an immunosuppressive tumor microenvironment (TME) by a plethora of signaling pathways, including nuclear factor kappa B (NF-κB) signaling, antigen presentation and the aforementioned IFN signaling pathways [81].
Finally, HPV infection is linked to angiogenesis. In a TP53-dependent manner, the expression of thrombospondin 1, maspin and VEGF is altered by HPV E6 and E7 oncoproteins, enhancing angiogenesis [87]. Concerning TP53 independent pathways, Wang et al. showed an upregulation of ribonucleotide reductase small subunit M2 (RRM2) by E7 and enhanced angiogenesis mediated by the ROS-ERK1/2-HIF1α-VEGF axis [88].

3.4. Oncoproteins E6 and E7 Increase Radiosensitivity of HPV-Positive Cervical Cancer Cells

Importantly, HPV-associated malignancies are linked to an intrinsic radiation sensitivity, resulting in a favorable response to radiotherapy [89,90,91,92]. As summarized in Figure 2, following the integration of the HPV genome, expression of viral oncoproteins E6 and E7 impact on pivotal cellular pathways by inactivating the tumor suppressor protein TP53 and retinoblastoma (Rb) protein, respectively. In brief, E6-mediated inactivation of TP53 requires the ubiquitin ligase E6AP, subsequently influencing cell cycle, apoptosis, cellular stress response and genomic stability [93]. The degradation of pRb by E7 activates the transcription factor E2F1, which promotes the upregulation of several S-phase genes, including the cyclin-dependent kinase inhibitor p16ink4a. In turn, p16ink4a is directly involved in radiation response by interfering with mechanisms of DNA damage response [94,95]. For instance, p16ink4a suppresses nuclear RAD51 foci, a marker of homologous recombination functionality, via downregulation of cyclin D1 [96]. Moreover, a PCR (polymerase chain reaction) array of DNA damage response genes revealed significantly lower RAD51 and breast cancer type 1 susceptibility protein (BRCA1) expression, indicating an impaired DNA damage response repair capacity [97]. Targeting poly(ADP-ribose) polymerase (PARP) is an effective treatment option in treating BRCA1 or BRCA2 negative cancers [98]. PARP1 is thereby involved in non-homologous end-joining (NHEJ) pathway and single-strand DNA repair. Ijff et al. recently showed that PARP1 inhibition sensitizes cervical cancer cell lines for CRT [99]. In addition, DNA single-strand break repair is directly impaired by E6 binding X-ray repair cross-complementing protein 1 (XRCC1), while double-strand break repair fidelity is compromised in both aTP53-dependent and -independent manner [100,101]. Notably, HPV 18+ tumors are considered to be more aggressive and display an impaired prognosis following radiotherapy [102,103,104]. As a potential mechanism, expression patterns of four DNA repair genes in HPV 18+ tumors were identified. TP52BP1, MCM9 (minichromosome maintenance 9 homologous recombination repair factor), POLR2F and SIRT6 were associated with higher activity of NHEJ and homologous recombination (HR) pathway and increased DNA repair capacity [105].

4. Molecular Protein Markers

The Cancer Genome Atlas (TCGA) database has expanded the view of the genomic landscape of cancer by characterizing molecular patterns of many malignancies. Based on this database, subgroups of keratin-low and keratin-high squamous cell carcinomas and adenocarcinoma-rich, endometrial-like cervical cancer were identified. The endometrial-like subgroup, for instance, is associated with a high frequency of KRAS, ARID1A (AT-rich interaction domain 1A) and phosphatase and tensin homolog (PTEN) mutations and is predominantly HPV-negative. PIK3CA (phosphatidylinositol-4,5-bisphosphat-3-kinase, catalytic subunit alpha), PTEN and MPK1 (mitogen-activated protein kinase 1) were confirmed as significantly mutated genes (SMG), while ERBB3, CASP8, HLA-A and TGFBR were identified as novel SMGs in cervical cancer. Amplifications in programmed death 1 (PD-L1) and PD-L2 and a relatively high number of gene fusions in BCAR4 are providing possible targets for novel therapeutic approaches [106].
Genomic alterations in phosphoinositide 3-kinase (PI3K) are common in many different entities, including cervical cancer [107,108]. Activating mutations of PI3K and its catalytic subunit alpha (PIK3CA) are associated with co-alterations of PTEN, MAPK and AKT1, which are sufficient to foster tumorigenesis, tumor growth, migration, protein synthesis and glucose metabolism in pre-clinical models [109,110,111,112,113]. Tumor progression via downstream activation of the PI3K/AKT/mammalian target of rapamycin (mTOR) signaling cascade is thereby induced and further promoted by viral oncoproteins E5, E6 and E7 in HPV-associated malignancies [114]. Arjumand et al. indicated that cervical cancer cells with PIK3CA-E545K mutation are more resistant to cisplatin or cisplatin-based CRT than cells with PIK3CA wild type [115]. Although these aspects seem to influence the treatment response and survival of cervical cancer patients, the predictive value of PIK3CA mutations remains inconclusive. In a cohort of 89 patients with high-risk early-stage cervical cancer who underwent post-operative radiotherapy, a PIK3CA mutation was the most frequently detected aberration (29.2%) but displayed no prognostic impact [116]. In contrast, another study detected PIK3CA mutations in 37.3% of 161 pre-treatment specimens from cervical cancer patients treated with CRT, which was associated with worse OS in univariate analysis (p = 0.037) [117]. These discordant findings are consistent with a systemic review by Pergialiotis and colleagues that included twelve research articles and a total of 2196 cervical cancer patients and demonstrated no impact of PIK3CA mutations on treatment response and survival [118].
In a comprehensive review, Noordhuis et al. described in 2011 potential markers to predict response and survival of cervical cancer patients treated with CRT. Based on 42 eligible studies the authors identified 27 out of 82 markers that were independently associated with survival [119]. Some of these markers have been investigated in detail in other studies and will also be mentioned in the following paragraphs.
A hypoxic TME and a dysregulated neo-angiogenesis is a common feature in solid tumors. Activation of hypoxia-inducible factor 1 α (HIF-1α) upregulates VEGF, which in turn promotes angiogenesis, although also modulates TP53 activity and protects cells from hypoxia-induced apoptosis [120]. In cervical cancer, a high expression level of HIF-1α and its downstream target, the tumor-associated cell-surface glycoprotein carbonic anhydrase 9 (CA9), is linked with poor prognosis and therapy resistance [121,122,123]. In addition, overexpression of VEGF was correlated with a significantly worse OS and DFS/PFS with pooled hazard ratios of 2.29 and 2.77, respectively, in a meta-analysis including 1306 patients performed by Zhang et al. [124]. Notably, assessment of VEGF expression was performed with a variety of methods, meaning the overall results must be evaluated in a limited way considering the heterogeneity of the studies included. Additionally, women treated with (chemo)radiotherapy were under-represented.
As further described by Noordhuis et al., cyclooxygenase-2 (COX-2) and the epidermal growth factor receptor (EGFR) pathway could be associated with therapy response and outcome in patients with cervical cancer treated with (C)RT. COX-2 is one of two isoenzymes that catalyze the synthesis of prostaglandins from arachidonic acid and drive inflammation [125]. In cancer, COX-2 is frequently overexpressed and associated with angiogenesis, disease progression, metastatic behavior and therapy resistance [126,127,128]. Several groups have reported a negative prognostic and predictive impact of COX-2 expression in cervical cancer patients treated with (C)RT, while others did not [129,130]. In a recent retrospective analysis, COX-2 expression was significantly associated with LVSI but not with any survival endpoint [131]. Data from the RTOG 0128 trial, a phase I-II study testing the COX-2 inhibitor celecoxib and CRT in patients with locally advanced cervical cancer, revealed that patients with low COX-2 expression treated with celecoxib plus CRT had a worse OS compared to patients with a high COX-2 expression [132]. Nevertheless, this approach was not pursued further due to excessively high rates of acute toxicities [133].
As upstream modulators of COX-2, members of the mitogen-activated protein kinase (MAPK), extracellular-signal regulated kinase (ERK) family and EGFR may be indicators of outcome and survival in cervical cancer. While data on the impact of the MAPK pathway are limited, numerous studies exist on EGFR signaling in cervical cancer. The EGFR receptor transmits growth signals not only via the RAS-RAF pathway, but also via PIK3 and is linked to multiple signaling cascades [134]. EGFR protein expression and gene amplifications have been correlated with unfavorable clinical outcomes and poor response to (C)RT in cervical cancer [135,136,137,138]. A meta-analysis on the prognostic impact of EGFR overexpression pooled 22 studies and demonstrated that elevated levels of EGFR were associated with inferior OS and DFS. Moreover, EGFR overexpression was correlated with a higher incidence of lymph node metastases and tumor size [139]. Nevertheless, it is still controversial whether the degree of EGFR overexpression or gene amplification should be used as a reliable and robust marker [140]. At present, the use of EGFR inhibitors has been investigated in phase II studies in recurrent or metastatic cervical cancer patients and has shown minimal or no benefit at all [141,142,143,144].
Other proteins orchestrating EGFR signaling, including ERK, pAKT and PTEN, were not related to survival in patients with cervical cancer [133]. Instead, overexpression of CD24, an upstream of glycosylphosphatidylinositol-anchored protein (GPI) stimulating Akt, ERK, and nuclear factor kappa B (NF-κB), was associated with inferior OS in a small cohort of cervical cancer patients [145]. Additionally, the predictive and prognostic impact of KRAS mutations was evaluated in a cohort of 876 early-stage cervical cancer patients (FIGO IB1-IIA2). KRAS mutations were predominant in non-squamous cell carcinomas (8.2% vs. 2.2%, p < 0.001), associated with HPV18+ tumors (p = 0.003) and significantly correlated with a worse RFS in univariate and multivariate analyses. Notably, these results were not reproducible in squamous cell carcinomas and HPV16+ tumors [146].
Polo-like kinases (PLK) represent five highly conserved serine or threonine protein kinases participating in cell cycle regulation, DNA replication, mitosis and response to various types of cellular stress [147,148,149]. PLK1, the prototype member, is essential during mitosis and closely related to cellular proliferation and tumor growth. In cervical cancer, PLK1 overexpression was detected using RNA sequence datasets extracted from 290 cervical cancer tissue specimens. A high level of PLK1 expression had negative impact on survival, consistent with previous findings [150,151]. Yang et al. further demonstrated that phosphorylation of PLK1 by c-ABL regulates its activity, while knockdown of c-ABL inhibits downstream activation of Aurora A, a key kinase in mitotic progression. Again, c-ABL mediated phosphorylation of PLK1 was associated with a worse 5-year survival rate, while PLK1 mutant Y425 partially inhibits tumor growth in mice [152]. Upon ligand stimulation of CD95 (Fas receptor), PLK3 phosphorylates pro-caspase-8 on residue threonine 273 and promotes apoptosis in vitro [153]. Against this background, the prognostic impact of both PLK3 and pT273 Caspase-8 was investigated in patients with cervical cancer. High levels of PLK3 and pT273 Caspase-8 were associated with improved survival rates after definitive CRT [154]. In general, PLK3 is associated with cellular stress response, linking DNA damage to DNA damage repair, cell cycle arrest, apoptosis and cellular adhesion [148,149,155] (Figure 3).
The impacts of DNA damage response (DDR) proteins were investigated by a Canadian group. In a set of pre-treatment samples from 117 cervical cancer patients treated with CRT, the proteins ATM, PARP-1, DNA-PKcs, Ku70 and Ku86 were quantified using fluorescence immunohistochemistry. Low expression of ATM and PARP-1 was significantly associated with a worse 5-year PFS. Moreover, expression of ATM, PARP-1, DNA-PKcs and Ku86 was associated with a shorter OS, while multivariate analysis confirmed ATM and PARP-1 as independent predictors for PFS and OS. Accordingly, the authors hypothesized that the assessment of ATM status may also be a predictive biomarker of PARP-1 inhibitor treatment efficacy [156].
In conclusion, protein biomarkers reflect on the profound and complex genomic and cellular alterations of cervical cancer, although single markers are less likely to predict prognosis and treatment response at present. Small sample sizes, lack of reproducibility and the heterogeneity of biochemical methods further complicate their practical applicability in clinical practice. Combined marker analysis, however, may cover a possible approach to predict treatment response and survival more precisely. Consequently, Choi et al. assessed the expression levels of 22 proteins from pre-treatment biopsies of 181 locally advanced cervical cancer specimens and identified a panel of BCL2, HER2, CD133, CA9 and ERCC1 as an independent predictor of survival after CRT [157].

5. MicroRNA, Long-Non-Coding RNA and Circular RNA

MicroRNA (miRNA), long-non-coding RNA (lncRNA) and circular RNA (circRNA) encompass a variety of RNA molecules that regulate gene expression and a multitude of cellular signaling pathways. RNA molecules have emerged within a growing body of evidence as potential dynamic biomarkers to predict therapeutic response and survival, and may be useful for monitoring and detecting persistent or recurrent disease [158,159,160,161].

5.1. MicroRNAs

MicroRNAs can be classified into oncogenic and tumor suppressive miRNAs, which affect a variety of specific target genes and impact on cell growth, malignant transformation, cell migration, invasion and therapy resistance. Target genes are regulated by binding specific sequence motifs within the 3’ untranslated region (UTR) of mRNAs [162,163,164]. In cervical cancer, there are several types of endogenous and viral forms of RNA derived from the HPV genome that participate in carcinogenesis and tumor progression [165]. To identify consistent signatures of miRNA profiles, He at al. performed a comprehensive analysis of 85 studies including 2099 non-cancerous tissue samples, 827 CIN samples and 3095 samples from cervical cancer tissues. First, they identified 42 upregulated and 21 downregulated miRNAs from CIN to cervical cancer. In the following meta-analysis, five upregulated miRNAs (hsa-miR-10a-5p, −16-5p, −25-5p, −92a-3p and −196a-5p) and seven downregulated miRNAs (hsa-miR-29a, −34a, −99a-5p, −100-5p, 199a-3p, −203 and −218-5p) were confirmed to be dysregulated in CIN and cervical cancer. Inverse miRNA and mRNA expression levels revealed that the identified miRNA signature is involved in numerous cancer-associated pathways, such as MAPK-, WNT-, TGF-β- and p53-pathways, as well as cytokine receptor and extracellular matrix receptor pathways, regulating cell cycle, proliferation and apoptosis [166]. In another systematic review of 24 studies, some overlap (miR-29a and miR-21 were reported to be most frequently down and upregulated miRNAs in cervical cancer) with the analysis of He et al. was reported, although in conclusion different miRNAs were used to create a diagnostic and prognostic panel, indicating difficulties in validating a single miRNA signature [165]. Nevertheless, overexpression of miR-21, miR-34a, miR-196a, miR-27a and miR-221 was identified as a unique and specific miRNA signature in HPV+ squamous cell cervical cancer [167].
HPV oncoproteins E6 and E7 thereby influence miRNA expression itself. Via the degradation of TP53 by E6, HPV downregulates the tumor suppressor miRNA miR-34a [168,169]. The suppression of miR-424 by E6 and E7 further results in CHK1 induction targeting DNA damage repair [170], while low miR-424 expression is inversely correlated with CHK1 and p-CHK1 levels and associated with advanced FIGO stage, poor tumor differentiation, lymph node metastasis and LVSI [171]. Elevated levels of miR-31 were further identified as an independent prognostic factor associated with advanced FIGO stage, lymph node metastases and LVSI. In this study, Wang et al. further identified ARID1A as a direct target of miR-31 [172]. Notably, ARID1A loss is associated with trastuzumab resistance in breast cancer [173]. In addition, upregulation of miR-20a/b was correlated with increased proliferation, tumor growth, migration and invasion via AKT/p38 pathway activation and inhibition of tissue inhibitor of metalloproteinase 2 (TIMP-2), fostering epithelial–mesenchymal transition (EMT) [174,175]. Finally, overexpression of miR-375 increases TP53, TP21, Survivin and Bax expression in line with caspase-3 and -9 activity in HPV18+ cervical cancer cells. The suppression of the insulin-like growth factor-1 receptor (IGF-1R) could be another tumor suppressive mechanism exerted by miR-375 expression [176].

5.2. Long-Non-Coding RNAs

LncRNAs are encoded by a vast proportion of the human genome covering regulatory transcripts exceeding 200 nucleotides that are associated with disease and cancer development and progression by interacting with proteins, mRNAs and miRNAs [177,178,179]. Gene expression is regulated by cis-acting lncRNA or trans-acting lncRNAs, respectively. Cis-acting lncRNAs recruit regulatory factors to specific gene loci, influence transcription or splicing of genes or regulate adjacent genes and chromatin, while trans-acting lncRNAs regulate distant gene expression via the interaction with promoters, RNA-polymerases and other proteins [180].
HPV directly affects lncRNAs such as HOTAIR (HOX transcript antisense intergenic RNA) and MALAT1. HOTAIR is an lncRNA encoded by the antisense strand of the HOXC gene and controlled by HPV 16 E7. By recruiting the chromatin-remodeling complex PRC2, HOTAIR is regulating the expression of cancer-related pathway genes [181]. Moreover, HOTAIR sponges miRNAs such as miR-23b and miR143-3p, thereby modulating MAPK1 expression and the BCL2 axis, facilitating tumor cell proliferation and favoring metastatic behavior [182]. In several cervical cancer cell lines, HOTAIR overexpression was shown to have regulatory influence on additional signaling cascades such as the mTOR and the Notch-Wnt pathway [183,184]. Additionally, HOTAIR fosters the malignant potential via upregulation of VEGF, MMP-9 and other EMT-related genes. Therefore, HOTAIR overexpression is associated with the presence of lymph node metastases and inferior overall survival [185]. Remarkably, higher HOTAIR serum levels were also associated with advanced T-stage, lymph node metastasis and LVSI, thereby impairing survival [184].
The regulation of EMT-related genes is accomplished by lncRNAs such as metastasis-associated lung adenocarcinoma transcript 1 (MALAT1) [186]. The latter is overexpressed in cervical cancer cell lines and in HPV-associated cervical cancer tissues sponging several miRNAs [187]. Based on samples from 50 high-risk HPV+ cervical cancer patients, Lu et al. demonstrated that MALAT1 expression is significantly elevated in radiation-resistant tumors. Following radiation exposure, an inversely changed expression of MALAT1 and the tumor suppressive miRNA miR-145 further suggests their interaction and substantiates the hypothesis of the regulatory effect of miR-145 on cell cycle regulators CDK6, CDK2 and Cyclin D1 [188]. MALAT1 overexpression is further correlated with advanced tumors size and FIGO stage, LVSI, lymph node metastases and impaired OS [189].
The lncRNA cervical carcinoma high-expressed 1 (CCHE1) is seen as a regulator of the ERK/MAPK pathway and is considered a negative prognostic factor in cervical cancer [190,191]. Correlated with advanced FIGO stages and other clinicopathological risk factors, colon cancer-associated transcript 2 (CCAT2) is linked to a worse survival rate [192], while growth arrest-specific transcript 5 (GAS5) has been identified as a tumor-suppressive lncRNA in several entities including cervical cancer. On a mechanistic level, GAS5 may increase radiosensitivity in cervical cancer cells via miR-106b and upregulation of forkhead box O1 (FOXO1) and PTEN by sponging miR-196a and miR-205 [193,194,195].
Finally, Chen et al. established a set of six immune-related lncRNAs (AC009065.8, LINC01871, MIR210HG, GEMIN7-AS1, GAS5-AS1 and DLEU1) to define a risk score, which covers an independent prognostic signature in cervical cancer patients. Associated with the Wnt and TGFβ pathways, the group hypothesized that the lncRNA prognostic signature could help to identify subgroups of patients, which may benefit from immunotherapeutic interventions [196]. Despite all limitations, this work, therefore, represents an interesting approach towards personalized medicine.

5.3. Circular RNAs

CircRNAs are highly conserved, covalently closed RNA species that act in a regulatory network with miRNA and other inhibitory proteins, regulating and influencing gene expression, cancer progression, EMT and the tumor microenvironment (TME) [197]. Characteristic expression patterns in different types of cancer suggest circRNAs as tool to predict and monitor disease-specific survival endpoints [198,199]. Indeed, a variety of circRNAs are overexpressed and can function as oncogenes or tumor suppressors in cervical cancer [200]. In a microarray analysis of 35 cervical cancer tissues, 45 circRNAs were shown to be significantly dysregulated in cervical cancer. Most prominent hsa_circ_0018289 was upregulated in the cervical cancer tissues analyzed. In vitro and in vivo knockdown of hsa_circ_0018289 lead to decreased proliferation, migration and invasion of cervical cancer cells via sponging miR-497 [201]. Moreover, hsa_circ_0023404 overexpression was associated with a poor prognosis in cervical cancer and its knockdown was shown to suppress proliferation, cell cycle progression and migration in a report by Zhang et al. [202]. Other reports classified hsa_circ_0000263 as oncogenic circRNA affecting p53 in a regulatory network in cervical cancer, while hsa_circ_0001445 may act as a tumor suppressor due to its ability to sponge miR-620 [203,204]. The sequestration of miR-136 by hsa_circ_0023404 further promotes the yes-associated protein 1 (YAP) signaling pathway via transcription factor CP2 (TFCP2) and cancer progression, resulting in an impaired prognosis [202]. Hsa_circ_0023404 has also been associated with VEGF induction in cervical cancer tissues, which is related to increased metastasis rates and resistance to therapy [205]. Furthermore, a positive correlation with TNM stage, tumor size and lymph node metastases and a negative prognostic impact on survival is reported for hsa_circRNA_101996 expression [206]. Similar results are reported for hsa_ circ_0067934 sequesting miR-545 in a regulatory network affecting the EGFR axis [207]. Finally, in addition to endogenous circRNA, high-risk HPVs encode oncogenic viral circRNA. CircE7 was shown to translate for E7 oncoprotein in a CaSki cell line. Interestingly, knockdown of circE7 reduces tumor growth and malignant potential in vitro and in vivo [208].

6. Circulating Tumor Cells (CTC), Circulating Cell-Free DNA (cfDNA) and miRNA in Cervical Cancer

Advances in biotechnology techniques have enabled the detection of small amounts of RNA and DNA as well as tumor cells in a patient’s peripheral blood. Circulating molecules and markers represent a promising diagnostic, prognostic and dynamic tool and can be easily performed as a liquid biopsy [209,210].

6.1. Circulating Tumor Cells (CTCs)

The non-invasive, real-time collection of CTCs provides information about disease status and progression and may help identify potential biomarkers for predicting outcome and survival [211]. Analyzing blood samples (3.2 mL) from a cohort of 107 cervical cancer patients, Du et al. detected one to 27 CTCs in 86 patients. Those patients had a significantly shortened PFS. The negative impact of CTC detection on PFS was next confirmed in the multivariate analysis [212]. In another study, a combination of CTC count and serum SCC Ag levels was significantly associated with DFS in a cohort of 99 patients with locally advanced cervical cancer treated with (C)RT and remained significant in multivariate analyses [213]. Within the translational research of the randomized and prospective GOG 240 trial, the impact of the CTC count was evaluated in 176 patients with recurrent or metastatic cervical cancer. The findings indicated that a more pronounced decrease in the CTC count to be associated with a lower risk of death, while patients with a CTC count ≥ median revealed a significant survival benefit due to the addition of bevacizumab. The authors consequently speculated that a high (≥median) pretreatment CTC count might reflect increased neovascularization and vulnerability to VEGF inhibition [214].
Nevertheless, the impact of CTCs on cervical cancer remains inconclusive. This is primarily due to the various isolation methods used for the detection of CTCs, making reproducibility considerably more difficult, while large series for the validation of individual methods are not available at present.

6.2. Circulating Cell-Free Tumor DNA (ctDNA)

The isolation of circulating tumor DNA (ctDNA) from the patient’s peripheral blood allows a molecular characterization of the tumor and the identification of potential genetic alterations [215]. In general, higher ctDNAs levels are associated with advanced FIGO stage, tumor size, grading and lymph node metastases in small cohorts of patients with cervical cancer [210,216]. Comparable to an extensive molecular characterization of cervical cancer tissue, ctDNA provides information regarding mutational burden, amplifications and alterations. Several groups have identified common target gene mutations in PIK3CA, ALK, EGFR, ATM, BRCA2, ERBB2, APC, KRAS, BRAF, ABL1 and PTEN based on genomic analyses of ctDNA [217,218,219,220]. The prognostic relevance of these signatures was further evaluated by matching them with clinical data. For instance, Tian et al. identified a broad spectrum of mutated genes in a cohort of metastatic cervical cancer patients. The presence of any of these alterations was associated with an impaired PFS and OS. Patients with more than two metastases had a significantly higher mutational burden than patients with less than two metastases. In a serial ctDNA analysis, a decrease in the detected genetic alterations was associated with treatment response, while disease progression was inversely associated with a higher mutational burden [217].

6.3. Circulating HPV DNA

In a small cohort of 19 patients with metastatic cervical cancer, Kang et al. were able to detect HPV cfDNA in all samples, while other authors reported the detection of HPV related DNA from 6.9% up to 30% of invasive cervical cancer cases [221,222]. In sequential samples from patients with complete response to CRT, a complete clearance of HPV cfDNA became evident, while baseline HPV cfDNA levels had no impact on treatment response [223]. This assumption was further corroborated by a meta-analysis by Gu et al., who investigated the prognostic impact of HPV cfDNA in 684 patients from ten studies. All studies included had a high specificity in common, whereas sensitivity increased only in recent years. In summary, the meta-analysis revealed that HPV cfDNA levels in liquid biopsies are suitable for diagnostic and monitoring purposes. The relatively small sample size and the heterogeneity of the sampling methods, however, remains a problem in assessing the prognostic value of HPV cfDNA serum levels [224].

6.4. Circulating miRNAs

Several groups investigated singular circulating miRNAs or miRNA profiles in liquid biopsies. Low levels of miR-218 in the serum of 90 cervical cancer patients were associated with a higher incidence of lymph node metastases, a higher tumor volume and adenocarcinomas [225]. High levels of miR-205 were associated with large and less differentiated tumors and lymph node metastases resulting in worse OS [226,227]. Similar results were shown in small cohorts for serum levels of miR-20a and miR-101 [228,229]. Moreover, miR-21 detection in 89 cervical cancer patients was also associated with a significantly higher incidence of lymph node metastases. Qui et al. confirmed a significant correlation of miR-21 levels and lymph nodes metastases in a more recent study with 112 early-stage cervical cancer patients and 45 women diagnosed with CIN. Furthermore, high serum levels of miR-21 were associated with impaired RFS, while similar findings were described for low serum levels of miR-125b and miR-370 [230]. Jia et al. collected blood samples from 213 women with early-stage disease and identified five altered circulating miRNAs (miR-21, miR-29a, miR25, miR200a and miR486-5p) with miR-29a and miR-200a levels to correlate with advanced tumor stages and poor differentiation [231]. Interestingly, these results were consistent with the results from other groups investigating tissue samples from cervical cancer patients for miRNA expression [232].

7. Tumor Microenvironment in Cervical Cancer

A network of immune and endothelial cells, fibroblasts, signaling proteins and extracellular matrix molecules within the tumor and the surrounding tissue constitutes the TME [233]. In that context, immunosuppressive and immunogenic tumor-infiltrating immune cells influence the prognostic landscape in cervical cancer, which is highly related to and modulated by HPV [234] (Figure 4).
Tumor-infiltrating lymphocytes (TILs) interfering with immune checkpoint inhibitors targeting PD-L1/PD-1 quickly became of interest to predict therapy response and survival [235]. HPV-related malignancies are associated with a high density of TILs. TILs (CD3+) can be differentiated by specific markers that define corresponding subpopulations such as helper (CD4+), cytotoxic (CD8+) and regulatory T-cells (FoxP3+, Tregs). In a cohort of 120 patients with locally advanced cervical cancer treated with CRT, Enwere et al. were among the first to assess the prognostic impacts of intratumoral CD8+ lymphocyte density and PD-L1 expression. PD-L1 expression (≥1%) was recorded in 88% of patients. Neither the presence of CD8+ lymphocytes nor PD-L1 expression was associated with survival endpoints after primary CRT [236]. Conversely, immunohistochemical detection of CD3 (pan T-cell marker), CD4, CD8, CD20 (B-cells) CD206 (macrophages) and Tregs in a smaller cohort of cervical cancer patients treated with CRT revealed an association of CD8+ TILS with pelvic lymph node metastases, while elevated levels of CD3+, CD4+, CD8+, CD206+ and FoxP3+ lymphocytes were associated with improved PFS and OS [237]. Using flow cytometry, Fan and colleagues investigated the prognostic impacts of PD-1 expression and density of CD4+ and CD8+ lymphocytes in 47 cervical cancer patients. Here, a high PD-1 expression on CD8+ lymphocytes was significantly correlated with a higher incidence of relapse. In addition, a low CD8/CD4-ratio was associated with poor OS in univariate and multivariate analyses [238]. These contradictory results indicate the difficulties in estimating the prognostic and predictive value of immune parameters, which underlie circadian fluxes and other factors. In a meta-analysis including 783 patients from seven studies, high PD-L1 expression was associated with impaired OS (hazard ratio 2.52, p = 0.031). Notably, in this meta-analysis only one study investigated PD-L1 expression in patients treated with CRT [239]. Data on the impacts of TILs, as assessed in another meta-analysis, revealed a shift from an immunosuppressive towards a highly immunogenic TME, with high levels of cytotoxic and helper T-cells during carcinogenesis [240]. In this context, Tregs were associated with shortened survival, suggesting their immune-inhibitory potential [241]. Another approach was pursued by Someya et al. Based on CD8, FoxP3, HLA-1, PD-L1, and XRCC4 expression, they classified a set of 100 cervical cancers treated with CRT in “inflamed”, “excluded” and “cold” tumors. “Cold” malignancies were defined by a lack of CD8+ lymphocytes in the tumor and stroma and were associated with a significantly larger tumor volume and a worse DFS, while “inflamed” and “excluded” tumors showed no statistical differences. The authors proposed that antitumor immunity is influenced by radiotherapy, which may overcome the immunosuppressive TME and turn “cold” into “hot” tumors [242]. A sufficient antitumor T-cell response requires an intact antigen presentation mediated by antigen-presenting cells (APCs) and the tumor cell itself. HPV infection modulates the innate immune system by changing human leukocyte antigen (HLA) expression and activates natural killer cell (NK cell) inhibitory receptors [243]. In addition, HPV oncoproteins E5, E6 and E7 promote immune suppression and evasion. For instance, E5 inhibits the transport of major histocompatibility complex (MHC) molecules to the cell surface, which present either viral or tumor-associated antigens [244,245]. Moreover, E6 and E7 interact with type I IFN pathways, mitigating antiviral programs of immune response or upregulating PD-L1 expression (E7) to facilitate lymphocyte dysfunction [234,246].
Other immune components in the TME in cervical cancer are less well understood. Tumor-associated macrophages (TAMs) are a major component of the TME with a pronounced ability to adapt their phenotype and function in dependence of different circumstances within the TME [247,248]. Polarization of TAMs in proinflammatory, classically activated (M1) and immuno-suppressive (M2) subtypes determines their function. The role of macrophages in tumorigenesis and treatment response remains controversial and Janus-faced properties are considered a target for tumor-promoting and antitumor activities [249]. In cervical cancer, high counts of CD68+ (M1) and CD163+ (M2) macrophages are significantly correlated to high-risk HPV infection and carcinogenesis. In transition from CIN to cervical cancer, the density of CD163+ macrophages increases, indicating a polarization towards a M2 subtype. High CD163+ counts were associated with higher FIGO stages and the development of lymph node metastases [250]. Conducting RNA sequencing in 90 cervical cancer patients and a comparative analysis with TCGA data from 286 patients, Qiu et al. investigated the landscape of infiltrating immune cells. In addition to ethnical disparities, they indicated a consistent immune infiltration with high numbers of activated dendritic cells and macrophages in HPV+ tumors. Interestingly, patients with higher Tregs levels tend to have impaired survival, similar to M2 and resting macrophages, which are associated with a poor prognosis [251]. Finally, a low M1/M2 ratio was an independent predictor of poor response and worse survival in a cohort of 84 Italian woman with locally advanced cervical cancer treated with CRT and surgery [252].
Less is known about cancer-associated fibroblasts (CAFs) in cervical cancer. In general, CAFs are associated with malignant progression and an impaired outcome in many different entities [253]. CAFs are known to secrete cyclooxygenase 2 (COX-2) and induce cancer stem cell (CSC)-like activity, promoting apoptotic resistance, proliferation, angiogenesis, inflammation, invasion and metastasis behavior of cancer cells [128]. CAFs derived from five cervical cancer patients were evaluated in vitro for effects in co-culture with Hela cells. Overexpression of transforming growth factor β1 (TGF-β1) and stromal cell-derived factor 1 (SDF-1) was associated with increased cell proliferation, migration, invasion, colony formation and cell cycle progression, while apoptosis was decreased in Hela cells [254]. Furthermore, stromal remodeling processes mediated by CAFs are associated with higher laminin-1 secretion, promoting invasion of CSCC7 cells in vitro [255]. In line with this, Wei et al. very recently identified a pro-metastatic periostin+ subset of CAFs that is correlated with impaired survival. These CAFs activate an integrin–FAK/Src-VE–cadherin signaling pathway to foster metastatic spread [256]. Regarding radiation response, Chu et al. revealed a protective crosstalk between CAFs and cervical cancer cells mediated by different growth factors and radiation response genes, including PDGF, VEGF, EGF, GADD45 and BTG2 [257].

8. Microbiota in Cervical Cancer

The increasing knowledge of specific alterations in the composition of the intestinal and cervicovaginal microbiota and its metabolites as well as interactions with neoplastic, epithelial and immune cells in the TME demonstrates the critical role of the microbiome in tumorigenesis and therapy [258]. Notably, global disparities in cervical cancer incidence revealed an association between cervicovaginal infections and the incidence of CINs and cervical cancer [259]. A recent systematic review and network meta-analysis suggested a greater diversity of the cervicovaginal microbiota in HPV+ women, while a greater diversity of cervicovaginal microbiota dominated by non-Lactobacilli species expect of Lactobacillus iners was associated with high-risk HPV, CINs and cervical cancer [260,261]. The rising alpha diversity is frequently associated with bacterial vaginosis, including Gardnerella, Prevotella, Atopobium and Sneathia, as well as the progression from CINs to cervical cancer [262]. Gardnerella and Atopobium could be involved in forming a biofilm, facilitating viral persistence [263,264]. In contrast, Lactobacillus crispatus was associated with HPV clearance [260,261]. Moreover, specific bacterial species such as Fusobacterium are under suspicion of promoting the malignant transformation in cervical cancer and other entities [265,266,267].
During CRT, a significant decrease in the bacterial load of cervical cytobrush samples was observed, while neither alpha- nor beta-diversity significantly changed [268]. Sims et al. succeeded in showing that the diversity of the gut microbiota could act as predictor of the survival of cervical cancer patients after CRT. In fecal specimens of 55 patients, the group observed a significant enrichment of Escherichia, Shigella and Enterobacteriaceae. Enterobacteriales species were dominant in long-term survivors. In addition, a higher diversity of the gut microbiome correlated with an increased tumor infiltration of activated CD4+ lymphocytes in tumor brush samples, indicating an interaction of the microbiome and the TME [269]. Although longitudinal studies are still lacking and are need to draw a firm conclusion, microbiota appear to contribute to persistent HPV infection and progression of invasive forms of cervical cancer.

9. Conclusions

To date, most approaches used to identify and validate robust and feasible biomarkers to predict treatment response and survival for women with cervical cancer in the clinical routine have failed. Regardless, clinical parameters form the basis of treatment decisions and may be used to estimate prognosis. Therefore, HPV infection is an important determinant of response to CRT, impacting a variety of pathways and the immunogenicity of the tumor.
Despite the multitude of dysregulated pathways and extensive research over the last decades, protein biomarkers are not routinely used in predicting therapy response and survival. In contrast, miRNAs and circulating tumor cells or tumor DNA could be used as potential dynamic biomarkers to detect persistent or recurrent disease response in the future. Similarly, this information may help in monitoring and predicting treatment response and outcome, as well as in identifying potential molecular targets.
Immunotherapies have rapidly transformed the treatment of solid tumors over the past decade. In this context, the TME has increasing importance. Tumor development, progression and metastatic behavior are critically influenced by the composition of the TME, resulting in a close association with outcome and survival. Nevertheless, immunotherapy only effects a restricted number of patients and conclusive results of ongoing studies on the implementation of immunotherapy in the primary setting in cervical cancer are still pending.
In summary, accurate prediction of treatment response and survival will help to implement personalized therapies that may improve the treatment of cervical cancer patients. As stated before, the identification of simple, valid and reproducible biomarkers will cover a critical role in this process. At present, however, usage of these markers is neither concise nor clear. Thus, further efforts are needed to implement new approaches and to achieve and expand a holistic view of the multifaceted nature of uterine cervical cancer. Finally, we have to make these achievements accessible to the majority of patients.

Author Contributions

M.F. and F.R. performed the literature search, analyzed the data, and wrote the manuscript. M.F. created the figures. G.C., E.F., J.W., H.C., K.S., C.R. and N.T. contributed to manuscript editing and final approval. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Chesson, H.W.; Dunne, E.F.; Hariri, S.; Markowitz, L.E. The Estimated Lifetime Probability of Acquiring Human Papillomavirus in the United States. Sex. Transm. Dis. 2014, 41, 660–664. [Google Scholar] [CrossRef]
  2. Ho, G.Y.; Bierman, R.; Beardsley, L.; Chang, C.J.; Burk, R.D. Natural History of Cervicovaginal Papillomavirus Infection in Young Women. N. Engl. J. Med. 1998, 338, 423–428. [Google Scholar] [CrossRef]
  3. Walboomers, J.M.; Jacobs, M.V.; Manos, M.M.; Bosch, F.X.; Kummer, J.A.; Shah, K.V.; Snijders, P.J.; Peto, J.; Meijer, C.J.; Muñoz, N. Human Papillomavirus Is a Necessary Cause of Invasive Cervical Cancer Worldwide. J. Pathol. 1999, 189, 12–19. [Google Scholar] [CrossRef]
  4. Rodriguez, N.M. Participatory Innovation for Human Papillomavirus Screening to Accelerate the Elimination of Cervical Cancer. Lancet Glob. Health 2021, 9, e582–e583. [Google Scholar] [CrossRef]
  5. Ferlay, J.; Colombet, M.; Soerjomataram, I.; Mathers, C.; Parkin, D.M.; Piñeros, M.; Znaor, A.; Bray, F. Estimating the Global Cancer Incidence and Mortality in 2018: GLOBOCAN Sources and Methods. Int. J. Cancer 2019, 144, 1941–1953. [Google Scholar] [CrossRef] [Green Version]
  6. Small, W.; Bacon, M.A.; Bajaj, A.; Chuang, L.T.; Fisher, B.J.; Harkenrider, M.M.; Jhingran, A.; Kitchener, H.C.; Mileshkin, L.R.; Viswanathan, A.N.; et al. Cervical Cancer: A Global Health Crisis. Cancer 2017, 123, 2404–2412. [Google Scholar] [CrossRef] [Green Version]
  7. Cohen, P.A.; Jhingran, A.; Oaknin, A.; Denny, L. Cervical Cancer. Lancet 2019, 393, 169–182. [Google Scholar] [CrossRef]
  8. Pötter, R.; Tanderup, K.; Kirisits, C.; de Leeuw, A.; Kirchheiner, K.; Nout, R.; Tan, L.T.; Haie-Meder, C.; Mahantshetty, U.; Segedin, B.; et al. The EMBRACE II Study: The Outcome and Prospect of Two Decades of Evolution within the GEC-ESTRO GYN Working Group and the EMBRACE Studies. Clin. Transl. Radiat. Oncol. 2018, 9, 48–60. [Google Scholar] [CrossRef] [Green Version]
  9. Han, K.; Milosevic, M.; Fyles, A.; Pintilie, M.; Viswanathan, A.N. Trends in the Utilization of Brachytherapy in Cervical Cancer in the United States. Int. J. Radiat. Oncol. Biol. Phys. 2013, 87, 111–119. [Google Scholar] [CrossRef]
  10. Gill, B.S.; Lin, J.F.; Krivak, T.C.; Sukumvanich, P.; Laskey, R.A.; Ross, M.S.; Lesnock, J.L.; Beriwal, S. National Cancer Data Base Analysis of Radiation Therapy Consolidation Modality for Cervical Cancer: The Impact of New Technological Advancements. Int. J. Radiat. Oncol. Biol. Phys. 2014, 90, 1083–1090. [Google Scholar] [CrossRef]
  11. Pötter, R.; Tanderup, K.; Schmid, M.P.; Jürgenliemk-Schulz, I.; Haie-Meder, C.; Fokdal, L.U.; Sturdza, A.E.; Hoskin, P.; Mahantshetty, U.; Segedin, B.; et al. MRI-Guided Adaptive Brachytherapy in Locally Advanced Cervical Cancer (EMBRACE-I): A Multicentre Prospective Cohort Study. Lancet Oncol. 2021, 22, 538–547. [Google Scholar] [CrossRef]
  12. Morris, M.; Eifel, P.J.; Lu, J.; Grigsby, P.W.; Levenback, C.; Stevens, R.E.; Rotman, M.; Gershenson, D.M.; Mutch, D.G. Pelvic Radiation with Concurrent Chemotherapy Compared with Pelvic and Para-Aortic Radiation for High-Risk Cervical Cancer. N. Engl. J. Med. 1999, 340, 1137–1143. [Google Scholar] [CrossRef]
  13. Eifel, P.J.; Winter, K.; Morris, M.; Levenback, C.; Grigsby, P.W.; Cooper, J.; Rotman, M.; Gershenson, D.; Mutch, D.G. Pelvic Irradiation with Concurrent Chemotherapy versus Pelvic and Para-Aortic Irradiation for High-Risk Cervical Cancer: An Update of Radiation Therapy Oncology Group Trial (RTOG) 90-01. J. Clin. Oncol. 2004, 22, 872–880. [Google Scholar] [CrossRef]
  14. Rose, P.G.; Ali, S.; Watkins, E.; Thigpen, J.T.; Deppe, G.; Clarke-Pearson, D.L.; Insalaco, S.; Gynecologic Oncology Group. Long-Term Follow-up of a Randomized Trial Comparing Concurrent Single Agent Cisplatin, Cisplatin-Based Combination Chemotherapy, or Hydroxyurea during Pelvic Irradiation for Locally Advanced Cervical Cancer: A Gynecologic Oncology Group Study. J. Clin. Oncol. 2007, 25, 2804–2810. [Google Scholar] [CrossRef]
  15. Shrivastava, S.; Mahantshetty, U.; Engineer, R.; Chopra, S.; Hawaldar, R.; Hande, V.; Kerkar, R.A.; Maheshwari, A.; Shylasree, T.S.; Ghosh, J.; et al. Cisplatin Chemoradiotherapy vs. Radiotherapy in FIGO Stage IIIB Squamous Cell Carcinoma of the Uterine Cervix: A Randomized Clinical Trial. JAMA Oncol. 2018, 4, 506–513. [Google Scholar] [CrossRef]
  16. Tewari, K.S.; Sill, M.W.; Long, H.J.; Penson, R.T.; Huang, H.; Ramondetta, L.M.; Landrum, L.M.; Oaknin, A.; Reid, T.J.; Leitao, M.M.; et al. Improved Survival with Bevacizumab in Advanced Cervical Cancer. N. Engl. J. Med. 2014, 370, 734–743. [Google Scholar] [CrossRef] [Green Version]
  17. Tewari, K.S.; Sill, M.W.; Penson, R.T.; Huang, H.; Ramondetta, L.M.; Landrum, L.M.; Oaknin, A.; Reid, T.J.; Leitao, M.M.; Michael, H.E.; et al. Bevacizumab for Advanced Cervical Cancer: Final Overall Survival and Adverse Event Analysis of a Randomised, Controlled, Open-Label, Phase 3 Trial (Gynecologic Oncology Group 240). Lancet 2017, 390, 1654–1663. [Google Scholar] [CrossRef] [Green Version]
  18. Chung, H.C.; Ros, W.; Delord, J.-P.; Perets, R.; Italiano, A.; Shapira-Frommer, R.; Manzuk, L.; Piha-Paul, S.A.; Xu, L.; Zeigenfuss, S.; et al. Efficacy and Safety of Pembrolizumab in Previously Treated Advanced Cervical Cancer: Results From the Phase II KEYNOTE-158 Study. J. Clin. Oncol. 2019, 37, 1470–1478. [Google Scholar] [CrossRef]
  19. De Felice, F.; Marchetti, C.; Fagotti, A.; Scambia, G. Immunotherapy in Cervical Cancer: The Advent of Precision Medicine. Ann. Transl. Med. 2020, 8, 773. [Google Scholar] [CrossRef]
  20. Coleman, R.L.; Lorusso, D.; Gennigens, C.; González-Martín, A.; Randall, L.; Cibula, D.; Lund, B.; Woelber, L.; Pignata, S.; Forget, F.; et al. Efficacy and safety of tisotumab vedotin in previously treated recurrent or metastatic cervical cancer (innovaTV 204/GOG-3023/ENGOT-cx6): A multicentre, open-label, single-arm, phase 2 study. Lancet Oncol. 2021, 22, 609–619. [Google Scholar] [CrossRef]
  21. Koh, W.-J.; Abu-Rustum, N.R.; Bean, S.; Bradley, K.; Campos, S.M.; Cho, K.R.; Chon, H.S.; Chu, C.; Clark, R.; Cohn, D.; et al. Cervical Cancer, Version 3.2019, NCCN Clinical Practice Guidelines in Oncology. J. Natl. Compr. Cancer Netw. 2019, 17, 64–84. [Google Scholar] [CrossRef] [Green Version]
  22. Pickel, H.; Haas, J.; Lahousen, M. Prognostic Factors in Cervical Cancer. Eur. J. Obstet. Gynecol. Reprod. Biol. 1997, 71, 209–213. [Google Scholar] [CrossRef]
  23. Ho, C.-M.; Chien, T.-Y.; Huang, S.-H.; Wu, C.-J.; Shih, B.-Y.; Chang, S.-C. Multivariate Analysis of the Prognostic Factors and Outcomes in Early Cervical Cancer Patients Undergoing Radical Hysterectomy. Gynecol. Oncol. 2004, 93, 458–464. [Google Scholar] [CrossRef]
  24. Sedlis, A.; Bundy, B.N.; Rotman, M.Z.; Lentz, S.S.; Muderspach, L.I.; Zaino, R.J. A Randomized Trial of Pelvic Radiation Therapy versus No Further Therapy in Selected Patients with Stage IB Carcinoma of the Cervix after Radical Hysterectomy and Pelvic Lymphadenectomy: A Gynecologic Oncology Group Study. Gynecol. Oncol. 1999, 73, 177–183. [Google Scholar] [CrossRef]
  25. Peters, W.A.; Liu, P.Y.; Barrett, R.J.; Stock, R.J.; Monk, B.J.; Berek, J.S.; Souhami, L.; Grigsby, P.; Gordon, W.; Alberts, D.S. Concurrent Chemotherapy and Pelvic Radiation Therapy Compared with Pelvic Radiation Therapy Alone as Adjuvant Therapy after Radical Surgery in High-Risk Early-Stage Cancer of the Cervix. J. Clin. Oncol. 2000, 18, 1606–1613. [Google Scholar] [CrossRef]
  26. Rotman, M.; Sedlis, A.; Piedmonte, M.R.; Bundy, B.; Lentz, S.S.; Muderspach, L.I.; Zaino, R.J. A Phase III Randomized Trial of Postoperative Pelvic Irradiation in Stage IB Cervical Carcinoma with Poor Prognostic Features: Follow-up of a Gynecologic Oncology Group Study. Int. J. Radiat. Oncol. Biol. Phys. 2006, 65, 169–176. [Google Scholar] [CrossRef]
  27. Rogers, L.; Siu, S.S.N.; Luesley, D.; Bryant, A.; Dickinson, H.O. Radiotherapy and Chemoradiation after Surgery for Early Cervical Cancer. Cochrane Database Syst. Rev. 2012, 5, CD007583. [Google Scholar] [CrossRef] [Green Version]
  28. Kasuya, G.; Ogawa, K.; Iraha, S.; Nagai, Y.; Hirakawa, M.; Toita, T.; Kakinohana, Y.; Kudaka, W.; Inamine, M.; Ariga, T.; et al. Postoperative Radiotherapy for Uterine Cervical Cancer: Impact of Lymph Node and Histological Type on Survival. Anticancer Res. 2013, 33, 2199–2204. [Google Scholar]
  29. Nugent, E.K.; Case, A.S.; Hoff, J.T.; Zighelboim, I.; DeWitt, L.L.; Trinkhaus, K.; Mutch, D.G.; Thaker, P.H.; Massad, L.S.; Rader, J.S. Chemoradiation in Locally Advanced Cervical Carcinoma: An Analysis of Cisplatin Dosing and Other Clinical Prognostic Factors. Gynecol. Oncol. 2010, 116, 438–441. [Google Scholar] [CrossRef]
  30. Teh, J.; Yap, S.P.; Tham, I.; Sethi, V.K.; Chua, E.J.; Yeo, R.; Ho, T.H.; Tay, E.H.; Chia, Y.N.; Soh, L.T.; et al. Concurrent Chemoradiotherapy Incorporating High-Dose Rate Brachytherapy for Locally Advanced Cervical Carcinoma: Survival Outcomes, Patterns of Failure, and Prognostic Factors. Int. J. Gynecol. Cancer 2010, 20, 428–433. [Google Scholar] [CrossRef]
  31. Kim, T.-E.; Park, B.-J.; Kwack, H.-S.; Kwon, J.-Y.; Kim, J.-H.; Yoon, S.-C. Outcomes and Prognostic Factors of Cervical Cancer after Concurrent Chemoradiation. J. Obstet. Gynaecol. Res. 2012, 38, 1315–1320. [Google Scholar] [CrossRef] [Green Version]
  32. Rose, P.G.; Java, J.; Whitney, C.W.; Stehman, F.B.; Lanciano, R.; Thomas, G.M.; DiSilvestro, P.A. Nomograms Predicting Progression-Free Survival, Overall Survival, and Pelvic Recurrence in Locally Advanced Cervical Cancer Developed From an Analysis of Identifiable Prognostic Factors in Patients From NRG Oncology/Gynecologic Oncology Group Randomized Trials of Chemoradiotherapy. J. Clin. Oncol. 2015, 33, 2136–2142. [Google Scholar] [CrossRef] [Green Version]
  33. Chen, C.-C.; Wang, L.; Lin, J.-C.; Jan, J.-S. The Prognostic Factors for Locally Advanced Cervical Cancer Patients Treated by Intensity-Modulated Radiation Therapy with Concurrent Chemotherapy. J. Formos. Med. Assoc. 2015, 114, 231–237. [Google Scholar] [CrossRef] [Green Version]
  34. Perez, C.A.; Grigsby, P.W.; Castro-Vita, H.; Lockett, M.A. Carcinoma of the Uterine Cervix. I. Impact of Prolongation of Overall Treatment Time and Timing of Brachytherapy on Outcome of Radiation Therapy. Int. J. Radiat. Oncol. Biol. Phys. 1995, 32, 1275–1288. [Google Scholar] [CrossRef]
  35. Tanderup, K.; Fokdal, L.U.; Sturdza, A.; Haie-Meder, C.; Mazeron, R.; van Limbergen, E.; Jürgenliemk-Schulz, I.; Petric, P.; Hoskin, P.; Dörr, W.; et al. Effect of Tumor Dose, Volume and Overall Treatment Time on Local Control after Radiochemotherapy Including MRI Guided Brachytherapy of Locally Advanced Cervical Cancer. Radiother. Oncol. 2016, 120, 441–446. [Google Scholar] [CrossRef]
  36. Fields, E.C.; Hazell, S.; Morcos, M.; Schmidt, E.J.; Chargari, C.; Viswanathan, A.N. Image-Guided Gynecologic Brachytherapy for Cervical Cancer. Semin. Radiat. Oncol. 2020, 30, 16–28. [Google Scholar] [CrossRef]
  37. Pötter, R.; Dimopoulos, J.; Georg, P.; Lang, S.; Waldhäusl, C.; Wachter-Gerstner, N.; Weitmann, H.; Reinthaller, A.; Knocke, T.H.; Wachter, S.; et al. Clinical Impact of MRI Assisted Dose Volume Adaptation and Dose Escalation in Brachytherapy of Locally Advanced Cervix Cancer. Radiother. Oncol. 2007, 83, 148–155. [Google Scholar] [CrossRef]
  38. Schernberg, A.; Bockel, S.; Annede, P.; Fumagalli, I.; Escande, A.; Mignot, F.; Kissel, M.; Morice, P.; Bentivegna, E.; Gouy, S.; et al. Tumor Shrinkage During Chemoradiation in Locally Advanced Cervical Cancer Patients: Prognostic Significance, and Impact for Image-Guided Adaptive Brachytherapy. Int. J. Radiat. Oncol. Biol. Phys. 2018, 102, 362–372. [Google Scholar] [CrossRef]
  39. Tang, X.; Mu, X.; Zhao, Z.; Zhao, H.; Mao, Z. Dose-Effect Response in Image-Guided Adaptive Brachytherapy for Cervical Cancer: A Systematic Review and Meta-Regression Analysis. Brachytherapy 2020, 19, 438–446. [Google Scholar] [CrossRef]
  40. Gandhi, A.K.; Sharma, D.N.; Rath, G.K.; Julka, P.K.; Subramani, V.; Sharma, S.; Manigandan, D.; Laviraj, M.A.; Kumar, S.; Thulkar, S. Early Clinical Outcomes and Toxicity of Intensity Modulated versus Conventional Pelvic Radiation Therapy for Locally Advanced Cervix Carcinoma: A Prospective Randomized Study. Int. J. Radiat. Oncol. Biol. Phys. 2013, 87, 542–548. [Google Scholar] [CrossRef]
  41. Naik, A.; Gurjar, O.P.; Gupta, K.L.; Singh, K.; Nag, P.; Bhandari, V. Comparison of Dosimetric Parameters and Acute Toxicity of Intensity-Modulated and Three-Dimensional Radiotherapy in Patients with Cervix Carcinoma: A Randomized Prospective Study. Cancer Radiother. 2016, 20, 370–376. [Google Scholar] [CrossRef]
  42. Yu, C.; Zhu, W.; Ji, Y.; Guo, J.; Pan, P.; Han, J.; Zhou, X. A Comparative Study of Intensity-Modulated Radiotherapy and Standard Radiation Field with Concurrent Chemotherapy for Local Advanced Cervical Cancer. Eur. J. Gynaecol. Oncol. 2015, 36, 278–282. [Google Scholar]
  43. Koulis, T.A.; Kornaga, E.N.; Banerjee, R.; Phan, T.; Ghatage, P.; Magliocco, A.M.; Lees-Miller, S.P.; Doll, C.M. Anemia, Leukocytosis and Thrombocytosis as Prognostic Factors in Patients with Cervical Cancer Treated with Radical Chemoradiotherapy: A Retrospective Cohort Study. Clin. Transl. Radiat. Oncol. 2017, 4, 51–56. [Google Scholar] [CrossRef] [Green Version]
  44. Dunst, J.; Kuhnt, T.; Strauss, H.G.; Krause, U.; Pelz, T.; Koelbl, H.; Haensgen, G. Anemia in Cervical Cancers: Impact on Survival, Patterns of Relapse, and Association with Hypoxia and Angiogenesis. Int. J. Radiat. Oncol. Biol. Phys. 2003, 56, 778–787. [Google Scholar] [CrossRef]
  45. Bishop, A.J.; Allen, P.K.; Klopp, A.H.; Meyer, L.A.; Eifel, P.J. Relationship between Low Hemoglobin Levels and Outcomes after Treatment with Radiation or Chemoradiation in Patients with Cervical Cancer: Has the Impact of Anemia Been Overstated? Int. J. Radiat. Oncol. Biol. Phys. 2015, 91, 196–205. [Google Scholar] [CrossRef]
  46. Shinko, D.; Diakos, C.I.; Clarke, S.J.; Charles, K.A. Cancer-Related Systemic Inflammation: The Challenges and Therapeutic Opportunities for Personalized Medicine. Clin. Pharmacol. Ther. 2017, 102, 599–610. [Google Scholar] [CrossRef]
  47. Singh, N.; Baby, D.; Rajguru, J.P.; Patil, P.B.; Thakkannavar, S.S.; Pujari, V.B. Inflammation and Cancer. Ann. Afr. Med. 2019, 18, 121–126. [Google Scholar] [CrossRef]
  48. Zou, P.; Yang, E.; Li, Z. Neutrophil-to-Lymphocyte Ratio Is an Independent Predictor for Survival Outcomes in Cervical Cancer: A Systematic Review and Meta-Analysis. Sci. Rep. 2020, 10, 21917. [Google Scholar] [CrossRef]
  49. Huang, H.; Liu, Q.; Zhu, L.; Zhang, Y.; Lu, X.; Wu, Y.; Liu, L. Prognostic Value of Preoperative Systemic Immune-Inflammation Index in Patients with Cervical Cancer. Sci. Rep. 2019, 9, 3284. [Google Scholar] [CrossRef] [Green Version]
  50. Yu, J.M.; Yang, M.; Xu, H.X.; Li, W.; Fu, Z.M.; Lin, Y.; Shi, Y.Y.; Song, C.H.; Shi, H.P.; Guo, Z.Q.; et al. Association between Serum C-Reactive Protein Concentration and Nutritional Status of Malignant Tumor Patients. Nutr. Cancer 2019, 71, 240–245. [Google Scholar] [CrossRef]
  51. Zhang, W.; Liu, K.; Ye, B.; Liang, W.; Ren, Y. Pretreatment C-Reactive Protein/Albumin Ratio Is Associated with Poor Survival in Patients with Stage IB-IIA Cervical Cancer. Cancer Med. 2018, 7, 105–113. [Google Scholar] [CrossRef] [Green Version]
  52. He, X.; Li, J.-P.; Liu, X.-H.; Zhang, J.-P.; Zeng, Q.-Y.; Chen, H.; Chen, S.-L. Prognostic Value of C-Reactive Protein/Albumin Ratio in Predicting Overall Survival of Chinese Cervical Cancer Patients Overall Survival: Comparison among Various Inflammation Based Factors. J. Cancer 2018, 9, 1877–1884. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  53. Haraga, J.; Nakamura, K.; Omichi, C.; Nishida, T.; Haruma, T.; Kusumoto, T.; Seki, N.; Masuyama, H.; Katayama, N.; Kanazawa, S.; et al. Pretreatment Prognostic Nutritional Index Is a Significant Predictor of Prognosis in Patients with Cervical Cancer Treated with Concurrent Chemoradiotherapy. Mol. Clin. Oncol. 2016, 5, 567–574. [Google Scholar] [CrossRef] [PubMed]
  54. Gangopadhyay, A. Prognostic Nutritional Index and Clinical Response in Locally Advanced Cervical Cancer. Nutr. Cancer 2020, 72, 1438–1442. [Google Scholar] [CrossRef] [PubMed]
  55. Matsuoka, H.; Nakamura, K.; Matsubara, Y.; Ida, N.; Nishida, T.; Ogawa, C.; Katsi, K.; Kanazawa, S.; Masuyama, H. Sarcopenia Is Not a Prognostic Factor of Outcome in Patients with Cervical Cancer Undergoing Concurrent Chemoradiotherapy or Radiotherapy. Anticancer Res. 2019, 39, 933–939. [Google Scholar] [CrossRef] [PubMed]
  56. Kiyotoki, T.; Nakamura, K.; Haraga, J.; Omichi, C.; Ida, N.; Saijo, M.; Nishida, T.; Kusumoto, T.; Masuyama, H. Sarcopenia Is an Important Prognostic Factor in Patients With Cervical Cancer Undergoing Concurrent Chemoradiotherapy. Int. J. Gynecol. Cancer 2018, 28, 168–175. [Google Scholar] [CrossRef]
  57. Lee, J.; Chang, C.-L.; Lin, J.-B.; Wu, M.-H.; Sun, F.-J.; Jan, Y.-T.; Hsu, S.-M.; Chen, Y.-J. Skeletal Muscle Loss Is an Imaging Biomarker of Outcome after Definitive Chemoradiotherapy for Locally Advanced Cervical Cancer. Clin. Cancer Res. 2018, 24, 5028–5036. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  58. Ryu, H.K.; Baek, J.S.; Kang, W.D.; Kim, S.M. The Prognostic Value of Squamous Cell Carcinoma Antigen for Predicting Tumor Recurrence in Cervical Squamous Cell Carcinoma Patients. Obstet. Gynecol. Sci. 2015, 58, 368–376. [Google Scholar] [CrossRef] [Green Version]
  59. Xu, D.; Wang, D.; Wang, S.; Tian, Y.; Long, Z.; Ren, X. Correlation Between Squamous Cell Carcinoma Antigen Level and the Clinicopathological Features of Early-Stage Cervical Squamous Cell Carcinoma and the Predictive Value of Squamous Cell Carcinoma Antigen Combined With Computed Tomography Scan for Lymph Node Metastasis. Int. J. Gynecol. Cancer 2017, 27, 1935–1942. [Google Scholar] [CrossRef] [PubMed]
  60. Choi, K.H.; Lee, S.W.; Yu, M.; Jeong, S.; Lee, J.W.; Lee, J.H. Significance of Elevated SCC-Ag Level on Tumor Recurrence and Patient Survival in Patients with Squamous-Cell Carcinoma of Uterine Cervix Following Definitive Chemoradiotherapy: A Multi-Institutional Analysis. J. Gynecol. Oncol. 2019, 30, e1. [Google Scholar] [CrossRef]
  61. Fu, J.; Wang, W.; Wang, Y.; Liu, C.; Wang, P. The Role of Squamous Cell Carcinoma Antigen (SCC Ag) in Outcome Prediction after Concurrent Chemoradiotherapy and Treatment Decisions for Patients with Cervical Cancer. Radiat. Oncol. 2019, 14, 146. [Google Scholar] [CrossRef] [Green Version]
  62. Liu, Z.; Shi, H. Prognostic Role of Squamous Cell Carcinoma Antigen in Cervical Cancer: A Meta-Analysis. Dis. Markers 2019, 2019, 6710352. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  63. Schiffman, M.; Doorbar, J.; Wentzensen, N.; de Sanjosé, S.; Fakhry, C.; Monk, B.J.; Stanley, M.A.; Franceschi, S. Carcinogenic Human Papillomavirus Infection. Nat. Rev. Dis. Primers 2016, 2, 16086. [Google Scholar] [CrossRef] [PubMed]
  64. Oyervides-Muñoz, M.A.; Pérez-Maya, A.A.; Rodríguez-Gutiérrez, H.F.; Gómez-Macias, G.S.; Fajardo-Ramírez, O.R.; Treviño, V.; Barrera-Saldaña, H.A.; Garza-Rodríguez, M.L. Understanding the HPV Integration and Its Progression to Cervical Cancer. Infect. Genet. Evol. 2018, 61, 134–144. [Google Scholar] [CrossRef] [PubMed]
  65. Lindel, K.; Rieken, S.; Daffinger, S.; Weber, K.J.; de Villiers, E.-M.; Debus, J. The Transcriptional Regulator Gene E2 of the Human Papillomavirus (HPV) 16 Influences the Radiosensitivity of Cervical Keratinocytes. Radiat. Oncol. 2012, 7, 187. [Google Scholar] [CrossRef] [Green Version]
  66. Vernon, S.D.; Unger, E.R.; Miller, D.L.; Lee, D.R.; Reeves, W.C. Association of Human Papillomavirus Type 16 Integration in the E2 Gene with Poor Disease-Free Survival from Cervical Cancer. Int. J. Cancer 1997, 74, 50–56. [Google Scholar] [CrossRef]
  67. Lindel, K.; de Villiers, E.-M.; Burri, P.; Studer, U.; Altermatt, H.J.; Greiner, R.H.; Gruber, G. Impact of Viral E2-Gene Status on Outcome after Radiotherapy for Patients with Human Papillomavirus 16-Positive Cancer of the Uterine Cervix. Int. J. Radiat. Oncol. Biol. Phys. 2006, 65, 760–765. [Google Scholar] [CrossRef]
  68. Ren, S.; Gaykalova, D.A.; Guo, T.; Favorov, A.V.; Fertig, E.J.; Tamayo, P.; Callejas-Valera, J.L.; Allevato, M.; Gilardi, M.; Santos, J.; et al. HPV E2, E4, E5 Drive Alternative Carcinogenic Pathways in HPV Positive Cancers. Oncogene 2020, 39, 6327–6339. [Google Scholar] [CrossRef]
  69. Kamal, M.; Lameiras, S.; Deloger, M.; Morel, A.; Vacher, S.; Lecerf, C.; Dupain, C.; Jeannot, E.; Girard, E.; Baulande, S.; et al. Human Papilloma Virus (HPV) Integration Signature in Cervical Cancer: Identification of MACROD2 Gene as HPV Hot Spot Integration Site. Br. J. Cancer 2021, 124, 777–785. [Google Scholar] [CrossRef]
  70. Huang, Y.; He, Q.; Xu, K.; Zhou, J.; Yin, J.; Li, F.; Feng, M.; Lang, J. A New Marker Based on Risk Stratification of Human Papillomavirus DNA and Tumor Size to Predict Survival of Locally Advanced Cervical Cancer. Int. J. Gynecol. Cancer 2019, 29, 459–465. [Google Scholar] [CrossRef]
  71. Kim, J.-Y.; Park, S.; Nam, B.-H.; Roh, J.-W.; Lee, C.H.; Kim, Y.-H.; Shin, H.-J.; Lee, S.-K.; Kong, S.-Y.; Seong, M.-W.; et al. Low Initial Human Papilloma Viral Load Implicates Worse Prognosis in Patients with Uterine Cervical Cancer Treated with Radiotherapy. J. Clin. Oncol. 2009, 27, 5088–5093. [Google Scholar] [CrossRef] [PubMed]
  72. Song, Y.J.; Kim, J.-Y.; Lee, S.-K.; Lim, H.-S.; Lim, M.C.; Seo, S.-S.; Kang, S.; Lee, D.O.; Park, S.-Y. Persistent Human Papillomavirus DNA Is Associated with Local Recurrence after Radiotherapy of Uterine Cervical Cancer. Int. J. Cancer 2011, 129, 896–902. [Google Scholar] [CrossRef] [PubMed]
  73. Zuo, J.; Huang, Y.; An, J.; Yang, X.; Li, N.; Huang, M.; Wu, L. Nomograms Based on HPV Load for Predicting Survival in Cervical Squamous Cell Carcinoma: An Observational Study with a Long-Term Follow-Up. Chin. J. Cancer Res. 2019, 31, 389–399. [Google Scholar] [CrossRef] [PubMed]
  74. Cao, M.; Wang, Y.; Wang, D.; Duan, Y.; Hong, W.; Zhang, N.; Shah, W.; Wang, Y.; Chen, H. Increased High-Risk Human Papillomavirus Viral Load Is Associated With Immunosuppressed Microenvironment and Predicts a Worse Long-Term Survival in Cervical Cancer Patients. Am. J. Clin. Pathol. 2020, 153, 502–512. [Google Scholar] [CrossRef]
  75. Narisawa-Saito, M.; Kiyono, T. Basic Mechanisms of High-Risk Human Papillomavirus-Induced Carcinogenesis: Roles of E6 and E7 Proteins. Cancer Sci 2007, 98, 1505–1511. [Google Scholar] [CrossRef]
  76. Yuan, Y.; Cai, X.; Shen, F.; Ma, F. HPV Post-Infection Microenvironment and Cervical Cancer. Cancer Lett. 2021, 497, 243–254. [Google Scholar] [CrossRef]
  77. Bowden, S.J.; Bodinier, B.; Kalliala, I.; Zuber, V.; Vuckovic, D.; Doulgeraki, T.; Whitaker, M.D.; Wielscher, M.; Cartwright, R.; Tsilidis, K.K.; et al. Genetic Variation in Cervical Preinvasive and Invasive Disease: A Genome-Wide Association Study. Lancet Oncol. 2021, 22, 548–557. [Google Scholar] [CrossRef]
  78. Chen, D.; Juko-Pecirep, I.; Hammer, J.; Ivansson, E.; Enroth, S.; Gustavsson, I.; Feuk, L.; Magnusson, P.K.E.; McKay, J.D.; Wilander, E.; et al. Genome-Wide Association Study of Susceptibility Loci for Cervical Cancer. J. Natl. Cancer Inst. 2013, 105, 624–633. [Google Scholar] [CrossRef] [Green Version]
  79. Leo, P.J.; Madeleine, M.M.; Wang, S.; Schwartz, S.M.; Newell, F.; Pettersson-Kymmer, U.; Hemminki, K.; Hallmans, G.; Tiews, S.; Steinberg, W.; et al. Defining the Genetic Susceptibility to Cervical Neoplasia-A Genome-Wide Association Study. PLoS Genet. 2017, 13, e1006866. [Google Scholar] [CrossRef] [Green Version]
  80. Nunes, R.A.L.; Morale, M.G.; Silva, G.Á.F.; Villa, L.L.; Termini, L. Innate Immunity and HPV: Friends or Foes. Clinics 2018, 73 (Suppl. 1). [Google Scholar] [CrossRef]
  81. Zhou, C.; Tuong, Z.K.; Frazer, I.H. Papillomavirus Immune Evasion Strategies Target the Infected Cell and the Local Immune System. Front. Oncol. 2019, 9, 682. [Google Scholar] [CrossRef] [Green Version]
  82. Hasan, U.A.; Zannetti, C.; Parroche, P.; Goutagny, N.; Malfroy, M.; Roblot, G.; Carreira, C.; Hussain, I.; Müller, M.; Taylor-Papadimitriou, J.; et al. The Human Papillomavirus Type 16 E7 Oncoprotein Induces a Transcriptional Repressor Complex on the Toll-like Receptor 9 Promoter. J. Exp. Med. 2013, 210, 1369–1387. [Google Scholar] [CrossRef] [PubMed]
  83. Starnes, T.; Rasila, K.K.; Robertson, M.J.; Brahmi, Z.; Dahl, R.; Christopherson, K.; Hromas, R. The Chemokine CXCL14 (BRAK) Stimulates Activated NK Cell Migration: Implications for the Downregulation of CXCL14 in Malignancy. Exp. Hematol. 2006, 34, 1101–1105. [Google Scholar] [CrossRef] [PubMed]
  84. Shaikh, M.H.; Bortnik, V.; McMillan, N.A.; Idris, A. CGAS-STING Responses Are Dampened in High-Risk HPV Type 16 Positive Head and Neck Squamous Cell Carcinoma Cells. Microb. Pathog. 2019, 132, 162–165. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  85. Bortnik, V.; Wu, M.; Julcher, B.; Salinas, A.; Nikolic, I.; Simpson, K.J.; McMillan, N.A.; Idris, A. Loss of HPV Type 16 E7 Restores CGAS-STING Responses in Human Papilloma Virus-Positive Oropharyngeal Squamous Cell Carcinomas Cells. J. Microbiol. Immunol. Infect. 2021, 54, 733–739. [Google Scholar] [CrossRef]
  86. Gutiérrez-Hoya, A.; Soto-Cruz, I. Role of the JAK/STAT Pathway in Cervical Cancer: Its Relationship with HPV E6/E7 Oncoproteins. Cells 2020, 9, 2297. [Google Scholar] [CrossRef]
  87. Toussaint-Smith, E.; Donner, D.B.; Roman, A. Expression of Human Papillomavirus Type 16 E6 and E7 Oncoproteins in Primary Foreskin Keratinocytes Is Sufficient to Alter the Expression of Angiogenic Factors. Oncogene 2004, 23, 2988–2995. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  88. Wang, N.; Zhan, T.; Ke, T.; Huang, X.; Ke, D.; Wang, Q.; Li, H. Increased Expression of RRM2 by Human Papillomavirus E7 Oncoprotein Promotes Angiogenesis in Cervical Cancer. Br. J. Cancer 2014, 110, 1034–1044. [Google Scholar] [CrossRef] [Green Version]
  89. Lindel, K.; Burri, P.; Studer, H.U.; Altermatt, H.J.; Greiner, R.H.; Gruber, G. Human Papillomavirus Status in Advanced Cervical Cancer: Predictive and Prognostic Significance for Curative Radiation Treatment. Int. J. Gynecol. Cancer 2005, 15, 278–284. [Google Scholar] [CrossRef]
  90. Li, P.; Tan, Y.; Zhu, L.-X.; Zhou, L.-N.; Zeng, P.; Liu, Q.; Chen, M.-B.; Tian, Y. Prognostic Value of HPV DNA Status in Cervical Cancer before Treatment: A Systematic Review and Meta-Analysis. Oncotarget 2017, 8, 66352–66359. [Google Scholar] [CrossRef] [Green Version]
  91. Fu, H.-C.; Chuang, I.-C.; Yang, Y.-C.; Chuang, P.-C.; Lin, H.; Ou, Y.-C.; Chang Chien, C.-C.; Huang, H.-S.; Kang, H.-Y. Low P16INK4A Expression Associated with High Expression of Cancer Stem Cell Markers Predicts Poor Prognosis in Cervical Cancer after Radiotherapy. Int. J. Mol. Sci. 2018, 19, 2541. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  92. Rödel, F.; Martin, D.; Balermpas, P.; Wieland, U.; Winkelmann, R.; Riekmann, T.; Falk, S.; Rödel, C.; Fokas, E. Modulation of Radiation Sensitivity and Antitumor Immunity by Viral Pathogenic Factors: Implications for Radio-Immunotherapy. Biochim. Biophys. Acta Rev. Cancer 2019, 1871, 126–137. [Google Scholar] [CrossRef] [PubMed]
  93. Scheffner, M.; Werness, B.A.; Huibregtse, J.M.; Levine, A.J.; Howley, P.M. The E6 Oncoprotein Encoded by Human Papillomavirus Types 16 and 18 Promotes the Degradation of P53. Cell 1990, 63, 1129–1136. [Google Scholar] [CrossRef]
  94. Song, S.; Liem, A.; Miller, J.A.; Lambert, P.F. Human Papillomavirus Types 16 E6 and E7 Contribute Differently to Carcinogenesis. Virology 2000, 267, 141–150. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  95. Bergeron, C.; Ronco, G.; Reuschenbach, M.; Wentzensen, N.; Arbyn, M.; Stoler, M.; von Knebel Doeberitz, M. The Clinical Impact of Using P16(INK4a) Immunochemistry in Cervical Histopathology and Cytology: An Update of Recent Developments. Int. J. Cancer 2015, 136, 2741–2751. [Google Scholar] [CrossRef]
  96. Dok, R.; Kalev, P.; Van Limbergen, E.J.; Asbagh, L.A.; Vázquez, I.; Hauben, E.; Sablina, A.; Nuyts, S. P16INK4a Impairs Homologous Recombination-Mediated DNA Repair in Human Papillomavirus-Positive Head and Neck Tumors. Cancer Res. 2014, 74, 1739–1751. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  97. Molkentine, J.M.; Molkentine, D.P.; Bridges, K.A.; Xie, T.; Yang, L.; Sheth, A.; Heffernan, T.P.; Clump, D.A.; Faust, A.Z.; Ferris, R.L.; et al. Targeting DNA Damage Response in Head and Neck Cancers through Abrogation of Cell Cycle Checkpoints. Int. J. Radiat. Biol. 2021, 97, 1121–1128. [Google Scholar] [CrossRef]
  98. Lightfoot, M.; Montemorano, L.; Bixel, K. PARP Inhibitors in Gynecologic Cancers: What Is the Next Big Development? Curr. Oncol. Rep. 2020, 22, 29. [Google Scholar] [CrossRef] [PubMed]
  99. IJff, M.; van Bochove, G.G.W.; Whitton, D.; Winiarczyk, R.; Honhoff, C.; Rodermond, H.; Crezee, J.; Stalpers, L.J.A.; Franken, N.A.P.; Oei, A.L. PARP1-Inhibition Sensitizes Cervical Cancer Cell Lines for Chemoradiation and Thermoradiation. Cancers 2021, 13, 2092. [Google Scholar] [CrossRef]
  100. Iftner, T.; Elbel, M.; Schopp, B.; Hiller, T.; Loizou, J.I.; Caldecott, K.W.; Stubenrauch, F. Interference of Papillomavirus E6 Protein with Single-Strand Break Repair by Interaction with XRCC1. EMBO J. 2002, 21, 4741–4748. [Google Scholar] [CrossRef] [Green Version]
  101. Shin, K.-H.; Ahn, J.H.; Kang, M.K.; Lim, P.K.; Yip, F.K.; Baluda, M.A.; Park, N.-H. HPV-16 E6 Oncoprotein Impairs the Fidelity of DNA End-Joining via P53-Dependent and -Independent Pathways. Int. J. Oncol. 2006, 28, 209–215. [Google Scholar] [CrossRef] [PubMed]
  102. Lombard, I.; Vincent-Salomon, A.; Validire, P.; Zafrani, B.; de la Rochefordière, A.; Clough, K.; Favre, M.; Pouillart, P.; Sastre-Garau, X. Human Papillomavirus Genotype as a Major Determinant of the Course of Cervical Cancer. J. Clin. Oncol. 1998, 16, 2613–2619. [Google Scholar] [CrossRef]
  103. Okonogi, N.; Kobayashi, D.; Suga, T.; Imai, T.; Wakatsuki, M.; Ohno, T.; Kato, S.; Nakano, T.; Kamada, T. Human Papillomavirus Genotype Affects Metastatic Rate Following Radiotherapy in Patients with Uterine Cervical Cancer. Oncol. Lett. 2018, 15, 459–466. [Google Scholar] [CrossRef] [Green Version]
  104. Onuki, M.; Matsumoto, K.; Tenjimbayashi, Y.; Tasaka, N.; Akiyama, A.; Sakurai, M.; Minaguchi, T.; Oki, A.; Satoh, T.; Yoshikawa, H. Human Papillomavirus Genotype and Prognosis of Cervical Cancer: Favorable Survival of Patients with HPV16-Positive Tumors. Papillomavirus Res. 2018, 6, 41–45. [Google Scholar] [CrossRef] [PubMed]
  105. Sample, K.M. DNA Repair Gene Expression Is Associated with Differential Prognosis between HPV16 and HPV18 Positive Cervical Cancer Patients Following Radiation Therapy. Sci. Rep. 2020, 10, 2774. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  106. Cancer Genome Atlas Research Network; Albert Einstein College of Medicine; Analytical Biological Services; Barretos Cancer Hospital; Baylor College of Medicine; Beckman Research Institute of City of Hope; Buck Institute for Research on Aging; Canada’s Michael Smith Genome Sciences Centre; Harvard Medical School; Helen F. Graham Cancer Center; et al. Integrated Genomic and Molecular Characterization of Cervical Cancer. Nature 2017, 543, 378–384. [Google Scholar] [CrossRef]
  107. Ojesina, A.I.; Lichtenstein, L.; Freeman, S.S.; Pedamallu, C.S.; Imaz-Rosshandler, I.; Pugh, T.J.; Cherniack, A.D.; Ambrogio, L.; Cibulskis, K.; Bertelsen, B.; et al. Landscape of Genomic Alterations in Cervical Carcinomas. Nature 2014, 506, 371–375. [Google Scholar] [CrossRef]
  108. Scholl, S.; Popovic, M.; de la Rochefordiere, A.; Girard, E.; Dureau, S.; Mandic, A.; Koprivsek, K.; Samet, N.; Craina, M.; Margan, M.; et al. Clinical and Genetic Landscape of Treatment Naive Cervical Cancer: Alterations in PIK3CA and in Epigenetic Modulators Associated with Sub-Optimal Outcome. EBioMedicine 2019, 43, 253–260. [Google Scholar] [CrossRef] [Green Version]
  109. Vivanco, I.; Sawyers, C.L. The Phosphatidylinositol 3-Kinase AKT Pathway in Human Cancer. Nat. Rev. Cancer 2002, 2, 489–501. [Google Scholar] [CrossRef]
  110. Samuels, Y.; Wang, Z.; Bardelli, A.; Silliman, N.; Ptak, J.; Szabo, S.; Yan, H.; Gazdar, A.; Powell, S.M.; Riggins, G.J.; et al. High Frequency of Mutations of the PIK3CA Gene in Human Cancers. Science 2004, 304, 554. [Google Scholar] [CrossRef] [Green Version]
  111. Schuurbiers, O.C.J.; Kaanders, J.H.A.M.; van der Heijden, H.F.M.; Dekhuijzen, R.P.N.; Oyen, W.J.G.; Bussink, J. The PI3-K/AKT-Pathway and Radiation Resistance Mechanisms in Non-Small Cell Lung Cancer. J. Thorac. Oncol. 2009, 4, 761–767. [Google Scholar] [CrossRef]
  112. Millis, S.Z.; Ikeda, S.; Reddy, S.; Gatalica, Z.; Kurzrock, R. Landscape of Phosphatidylinositol-3-Kinase Pathway Alterations Across 19 784 Diverse Solid Tumors. JAMA Oncol. 2016, 2, 1565–1573. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  113. Millis, S.Z.; Jardim, D.L.; Albacker, L.; Ross, J.S.; Miller, V.A.; Ali, S.M.; Kurzrock, R. Phosphatidylinositol 3-Kinase Pathway Genomic Alterations in 60,991 Diverse Solid Tumors Informs Targeted Therapy Opportunities. Cancer 2019, 125, 1185–1199. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  114. Zhang, L.; Wu, J.; Ling, M.T.; Zhao, L.; Zhao, K.-N. The Role of the PI3K/Akt/MTOR Signalling Pathway in Human Cancers Induced by Infection with Human Papillomaviruses. Mol. Cancer 2015, 14, 87. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  115. Arjumand, W.; Merry, C.D.; Wang, C.; Saba, E.; McIntyre, J.B.; Fang, S.; Kornaga, E.; Ghatage, P.; Doll, C.M.; Lees-Miller, S.P. Phosphatidyl Inositol-3 Kinase (PIK3CA) E545K Mutation Confers Cisplatin Resistance and a Migratory Phenotype in Cervical Cancer Cells. Oncotarget 2016, 7, 82424–82439. [Google Scholar] [CrossRef] [Green Version]
  116. Murakami, N.; Asami, Y.; Yoshida, H.; Takayanagi, D.; Hirose, S.; Kuno, I.; Takahashi, K.; Matsuda, M.; Shimada, Y.; Yamano, S.; et al. Distribution of Genetic Alterations in High-Risk Early-Stage Cervical Cancer Patients Treated with Postoperative Radiation Therapy. Sci. Rep. 2021, 11, 10567. [Google Scholar] [CrossRef]
  117. Martell, K.; McIntyre, J.B.; Kornaga, E.N.; Chan, A.M.Y.; Phan, T.; Köbel, M.; Enwere, E.K.; Dean, M.L.; Ghatage, P.; Lees-Miller, S.P.; et al. PIK3CA Mutation and CNV Status and Post-Chemoradiotherapy Survival in Patients with Cervical Cancer. Gynecol. Oncol. 2020, 158, 776–784. [Google Scholar] [CrossRef]
  118. Pergialiotis, V.; Nikolaou, C.; Haidopoulos, D.; Frountzas, M.; Thomakos, N.; Bellos, I.; Papapanagiotou, A.; Rodolakis, A. PIK3CA Mutations and Their Impact on Survival Outcomes of Patients with Cervical Cancer: A Systematic Review. Acta Cytol. 2020, 64, 547–555. [Google Scholar] [CrossRef]
  119. Noordhuis, M.G.; Eijsink, J.J.H.; Roossink, F.; de Graeff, P.; Pras, E.; Schuuring, E.; Wisman, G.B.A.; de Bock, G.H.; van der Zee, A.G.J. Prognostic Cell Biological Markers in Cervical Cancer Patients Primarily Treated with (Chemo)Radiation: A Systematic Review. Int. J. Radiat. Oncol. Biol. Phys. 2011, 79, 325–334. [Google Scholar] [CrossRef]
  120. Pezzuto, A.; Carico, E. Role of HIF-1 in Cancer Progression: Novel Insights. A Review. Curr. Mol. Med. 2018, 18, 343–351. [Google Scholar] [CrossRef]
  121. Lee, S.; Shin, H.-J.; Han, I.-O.; Hong, E.-K.; Park, S.-Y.; Roh, J.-W.; Shin, K.H.; Kim, T.H.; Kim, J.-Y. Tumor Carbonic Anhydrase 9 Expression Is Associated with the Presence of Lymph Node Metastases in Uterine Cervical Cancer. Cancer Sci. 2007, 98, 329–333. [Google Scholar] [CrossRef] [PubMed]
  122. Huang, M.; Chen, Q.; Xiao, J.; Yao, T.; Bian, L.; Liu, C.; Lin, Z. Overexpression of Hypoxia-Inducible Factor-1α Is a Predictor of Poor Prognosis in Cervical Cancer: A Clinicopathologic Study and a Meta-Analysis. Int. J. Gynecol. Cancer 2014, 24, 1054–1064. [Google Scholar] [CrossRef] [PubMed]
  123. Fu, Z.; Chen, D.; Cheng, H.; Wang, F. Hypoxia-Inducible Factor-1α Protects Cervical Carcinoma Cells from Apoptosis Induced by Radiation via Modulation of Vascular Endothelial Growth Factor and P53 under Hypoxia. Med. Sci. Monit. 2015, 21, 318–325. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  124. Zhang, J.; Liu, J.; Zhu, C.; He, J.; Chen, J.; Liang, Y.; Yang, F.; Wu, X.; Ma, X. Prognostic Role of Vascular Endothelial Growth Factor in Cervical Cancer: A Meta-Analysis. Oncotarget 2017, 8, 24797–24803. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  125. Mitchell, J.A.; Larkin, S.; Williams, T.J. Cyclooxygenase-2: Regulation and Relevance in Inflammation. Biochem. Pharmacol. 1995, 50, 1535–1542. [Google Scholar] [CrossRef]
  126. Kulkarni, S.; Rader, J.S.; Zhang, F.; Liapis, H.; Koki, A.T.; Masferrer, J.L.; Subbaramaiah, K.; Dannenberg, A.J. Cyclooxygenase-2 Is Overexpressed in Human Cervical Cancer. Clin. Cancer Res. 2001, 7, 429–434. [Google Scholar]
  127. Masunaga, R.; Kohno, H.; Dhar, D.K.; Ohno, S.; Shibakita, M.; Kinugasa, S.; Yoshimura, H.; Tachibana, M.; Kubota, H.; Nagasue, N. Cyclooxygenase-2 Expression Correlates with Tumor Neovascularization and Prognosis in Human Colorectal Carcinoma Patients. Clin. Cancer Res. 2000, 6, 4064–4068. [Google Scholar]
  128. Hashemi Goradel, N.; Najafi, M.; Salehi, E.; Farhood, B.; Mortezaee, K. Cyclooxygenase-2 in Cancer: A Review. J. Cell. Physiol. 2019, 234, 5683–5699. [Google Scholar] [CrossRef]
  129. Chen, H.H.W.; Su, W.-C.; Chou, C.-Y.; Guo, H.-R.; Ho, S.-Y.; Que, J.; Lee, W.-Y. Increased Expression of Nitric Oxide Synthase and Cyclooxygenase-2 Is Associated with Poor Survival in Cervical Cancer Treated with Radiotherapy. Int. J. Radiat. Oncol. Biol. Phys. 2005, 63, 1093–1100. [Google Scholar] [CrossRef]
  130. Kim, G.E.; Kim, Y.B.; Cho, N.H.; Chung, H.-C.; Pyo, H.R.; Lee, J.D.; Park, T.K.; Koom, W.S.; Chun, M.; Suh, C.O. Synchronous Coexpression of Epidermal Growth Factor Receptor and Cyclooxygenase-2 in Carcinomas of the Uterine Cervix: A Potential Predictor of Poor Survival. Clin. Cancer Res. 2004, 10, 1366–1374. [Google Scholar] [CrossRef] [Green Version]
  131. Hoellen, F.; Waldmann, A.; Banz-Jansen, C.; Rody, A.; Heide, M.; Köster, F.; Ribbat-Idel, J.; Thorns, C.; Gebhard, M.; Oberländer, M.; et al. Expression of Cyclooxygenase-2 in Cervical Cancer Is Associated with Lymphovascular Invasion. Oncol. Lett. 2016, 12, 2351–2356. [Google Scholar] [CrossRef] [Green Version]
  132. Doll, C.M.; Winter, K.; Gaffney, D.K.; Ryu, J.K.; Jhingran, A.; Dicker, A.P.; Weidhaas, J.B.; Miller, B.E.; Magliocco, A.M. COX-2 Expression and Survival in Patients with Locally Advanced Cervical Cancer Treated with Chemoradiotherapy and Celecoxib: A Quantitative Immunohistochemical Analysis of RTOG C0128. Int. J. Gynecol. Cancer 2013, 23, 176–183. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  133. Gaffney, D.K.; Winter, K.; Dicker, A.P.; Miller, B.; Eifel, P.J.; Ryu, J.; Avizonis, V.; Fromm, M.; Greven, K. A Phase II Study of Acute Toxicity for Celebrex (Celecoxib) and Chemoradiation in Patients with Locally Advanced Cervical Cancer: Primary Endpoint Analysis of RTOG 0128. Int. J. Radiat. Oncol. Biol. Phys. 2007, 67, 104–109. [Google Scholar] [CrossRef] [Green Version]
  134. Lemmon, M.A.; Schlessinger, J. Cell Signaling by Receptor Tyrosine Kinases. Cell 2010, 141, 1117–1134. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  135. Noordhuis, M.G.; Eijsink, J.J.H.; Ten Hoor, K.A.; Roossink, F.; Hollema, H.; Arts, H.J.G.; Pras, E.; Maduro, J.H.; Reyners, A.K.L.; de Bock, G.H.; et al. Expression of Epidermal Growth Factor Receptor (EGFR) and Activated EGFR Predict Poor Response to (Chemo)Radiation and Survival in Cervical Cancer. Clin. Cancer Res. 2009, 15, 7389–7397. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  136. Iida, K.; Nakayama, K.; Rahman, M.T.; Rahman, M.; Ishikawa, M.; Katagiri, A.; Yeasmin, S.; Otsuki, Y.; Kobayashi, H.; Nakayama, S.; et al. EGFR Gene Amplification Is Related to Adverse Clinical Outcomes in Cervical Squamous Cell Carcinoma, Making the EGFR Pathway a Novel Therapeutic Target. Br. J. Cancer 2011, 105, 420–427. [Google Scholar] [CrossRef]
  137. Soonthornthum, T.; Arias-Pulido, H.; Joste, N.; Lomo, L.; Muller, C.; Rutledge, T.; Verschraegen, C. Epidermal Growth Factor Receptor as a Biomarker for Cervical Cancer. Ann. Oncol. 2011, 22, 2166–2178. [Google Scholar] [CrossRef] [PubMed]
  138. Li, Q.; Tang, Y.; Cheng, X.; Ji, J.; Zhang, J.; Zhou, X. EGFR Protein Expression and Gene Amplification in Squamous Intraepithelial Lesions and Squamous Cell Carcinomas of the Cervix. Int. J. Clin. Exp. Pathol. 2014, 7, 733–741. [Google Scholar]
  139. Tian, W.-J.; Huang, M.-L.; Qin, Q.-F.; Chen, Q.; Fang, K.; Wang, P.-L. Prognostic Impact of Epidermal Growth Factor Receptor Overexpression in Patients with Cervical Cancer: A Meta-Analysis. PLoS ONE 2016, 11, e0158787. [Google Scholar] [CrossRef]
  140. Kato, S.; Okamura, R.; Mareboina, M.; Lee, S.; Goodman, A.; Patel, S.P.; Fanta, P.T.; Schwab, R.B.; Vu, P.; Raymond, V.M.; et al. Revisiting Epidermal Growth Factor Receptor (EGFR) Amplification as a Target for Anti-EGFR Therapy: Analysis of Cell-Free Circulating Tumor DNA in Patients with Advanced Malignancies. JCO Precis. Oncol. 2019, 3. [Google Scholar] [CrossRef]
  141. Goncalves, A.; Fabbro, M.; Lhommé, C.; Gladieff, L.; Extra, J.-M.; Floquet, A.; Chaigneau, L.; Carrasco, A.T.; Viens, P. A Phase II Trial to Evaluate Gefitinib as Second- or Third-Line Treatment in Patients with Recurring Locoregionally Advanced or Metastatic Cervical Cancer. Gynecol. Oncol. 2008, 108, 42–46. [Google Scholar] [CrossRef]
  142. Schilder, R.J.; Sill, M.W.; Lee, Y.-C.; Mannel, R. A Phase II Trial of Erlotinib in Recurrent Squamous Cell Carcinoma of the Cervix: A Gynecologic Oncology Group Study. Int. J. Gynecol. Cancer 2009, 19, 929–933. [Google Scholar] [CrossRef] [Green Version]
  143. Farley, J.; Sill, M.W.; Birrer, M.; Walker, J.; Schilder, R.J.; Thigpen, J.T.; Coleman, R.L.; Miller, B.E.; Rose, P.G.; Lankes, H.A. Phase II Study of Cisplatin plus Cetuximab in Advanced, Recurrent, and Previously Treated Cancers of the Cervix and Evaluation of Epidermal Growth Factor Receptor Immunohistochemical Expression: A Gynecologic Oncology Group Study. Gynecol. Oncol. 2011, 121, 303–308. [Google Scholar] [CrossRef] [PubMed]
  144. Santin, A.D.; Sill, M.W.; McMeekin, D.S.; Leitao, M.M.; Brown, J.; Sutton, G.P.; Van Le, L.; Griffin, P.; Boardman, C.H. Phase II Trial of Cetuximab in the Treatment of Persistent or Recurrent Squamous or Non-Squamous Cell Carcinoma of the Cervix: A Gynecologic Oncology Group Study. Gynecol. Oncol. 2011, 122, 495–500. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  145. Tanaka, T.; Terai, Y.; Kogata, Y.; Ashihara, K.; Maeda, K.; Fujiwara, S.; Yoo, S.; Tanaka, Y.; Tsunetoh, S.; Sasaki, H.; et al. CD24 Expression as a Marker for Predicting Clinical Outcome and Invasive Activity in Uterine Cervical Cancer. Oncol. Rep. 2015, 34, 2282–2288. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  146. Jiang, W.; Xiang, L.; Pei, X.; He, T.; Shen, X.; Wu, X.; Yang, H. Mutational Analysis of KRAS and Its Clinical Implications in Cervical Cancer Patients. J. Gynecol. Oncol. 2018, 29, e4. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  147. Strebhardt, K. Multifaceted Polo-like Kinases: Drug Targets and Antitargets for Cancer Therapy. Nat. Rev. Drug Discov. 2010, 9, 643–660. [Google Scholar] [CrossRef] [PubMed]
  148. Barton, O.; Naumann, S.C.; Diemer-Biehs, R.; Künzel, J.; Steinlage, M.; Conrad, S.; Makharashvili, N.; Wang, J.; Feng, L.; Lopez, B.S.; et al. Polo-like Kinase 3 Regulates CtIP during DNA Double-Strand Break Repair in G1. J. Cell Biol. 2014, 206, 877–894. [Google Scholar] [CrossRef]
  149. Helmke, C.; Becker, S.; Strebhardt, K. The Role of Plk3 in Oncogenesis. Oncogene 2016, 35, 135–147. [Google Scholar] [CrossRef]
  150. Gao, L.; Pang, Y.-Y.; Guo, X.-Y.; Zeng, J.-J.; Tang, Z.-Q.; Xiong, D.-D.; Yang, X.; Li, Y.; Ma, F.-C.; Pan, L.-J.; et al. Polo like Kinase 1 Expression in Cervical Cancer Tissues Generated from Multiple Detection Methods. PeerJ 2020, 8, e10458. [Google Scholar] [CrossRef]
  151. Chhavi; Saxena, M.; Singh, S.; Negi, M.P.S.; Srivastava, A.K.; Trivedi, R.; Singh, U.; Pant, M.C.; Bhatt, M.L.B. Expression Profiling of G2/M Phase Regulatory Proteins in Normal, Premalignant and Malignant Uterine Cervix and Their Correlation with Survival of Patients. J. Cancer Res. Ther. 2010, 6, 167–171. [Google Scholar] [CrossRef]
  152. Yang, X.; Chen, G.; Li, W.; Peng, C.; Zhu, Y.; Yang, X.; Li, T.; Cao, C.; Pei, H. Cervical Cancer Growth Is Regulated by a C-ABL-PLK1 Signaling Axis. Cancer Res. 2017, 77, 1142–1154. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  153. Helmke, C.; Raab, M.; Rödel, F.; Matthess, Y.; Oellerich, T.; Mandal, R.; Sanhaji, M.; Urlaub, H.; Rödel, C.; Becker, S.; et al. Ligand Stimulation of CD95 Induces Activation of Plk3 Followed by Phosphorylation of Caspase-8. Cell Res. 2016, 26, 914–934. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  154. Fleischmann, M.; Martin, D.; Peña-Llopis, S.; Oppermann, J.; von der Grün, J.; Diefenhardt, M.; Chatzikonstantinou, G.; Fokas, E.; Rödel, C.; Strebhardt, K.; et al. Association of Polo-Like Kinase 3 and PhosphoT273 Caspase 8 Levels With Disease-Related Outcomes Among Cervical Squamous Cell Carcinoma Patients Treated with Chemoradiation and Brachytherapy. Front. Oncol. 2019, 9, 742. [Google Scholar] [CrossRef] [Green Version]
  155. Holtrich, U.; Wolf, G.; Yuan, J.; Bereiter-Hahn, J.; Karn, T.; Weiler, M.; Kauselmann, G.; Rehli, M.; Andreesen, R.; Kaufmann, M.; et al. Adhesion Induced Expression of the Serine/Threonine Kinase Fnk in Human Macrophages. Oncogene 2000, 19, 4832–4839. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  156. Ho, C.K.; Kornaga, E.N.; Klimowicz, A.C.; Enwere, E.K.; Dean, M.; Bebb, G.D.; Phan, T.; Ghatage, P.; Magliocco, A.M.; Lees-Miller, S.P.; et al. Expression of DNA Damage Response Proteins in Cervical Cancer Patients Treated with Radical Chemoradiotherapy. Gynecol. Oncol. 2017, 145, 176–184. [Google Scholar] [CrossRef]
  157. Choi, C.H.; Chung, J.-Y.; Kang, J.H.; Paik, E.S.; Lee, Y.-Y.; Park, W.; Byeon, S.-J.; Chung, E.J.; Kim, B.-G.; Hewitt, S.M.; et al. Chemoradiotherapy Response Prediction Model by Proteomic Expressional Profiling in Patients with Locally Advanced Cervical Cancer. Gynecol. Oncol. 2020, 157, 437–443. [Google Scholar] [CrossRef]
  158. Lu, Y.; Govindan, R.; Wang, L.; Liu, P.; Goodgame, B.; Wen, W.; Sezhiyan, A.; Pfeifer, J.; Li, Y.; Hua, X.; et al. MicroRNA Profiling and Prediction of Recurrence/Relapse-Free Survival in Stage I Lung Cancer. Carcinogenesis 2012, 33, 1046–1054. [Google Scholar] [CrossRef]
  159. Jacob, H.; Stanisavljevic, L.; Storli, K.E.; Hestetun, K.E.; Dahl, O.; Myklebust, M.P. A Four-MicroRNA Classifier as a Novel Prognostic Marker for Tumor Recurrence in Stage II Colon Cancer. Sci. Rep. 2018, 8, 6157. [Google Scholar] [CrossRef]
  160. Akazawa, Y.; Mizuno, S.; Fujinami, N.; Suzuki, T.; Yoshioka, Y.; Ochiya, T.; Nakamoto, Y.; Nakatsura, T. Usefulness of Serum MicroRNA as a Predictive Marker of Recurrence and Prognosis in Biliary Tract Cancer after Radical Surgery. Sci. Rep. 2019, 9, 5925. [Google Scholar] [CrossRef]
  161. Wang, J.-Y.; Chen, L.-J. The Role of MiRNAs in the Invasion and Metastasis of Cervical Cancer. Biosci. Rep. 2019, 39, BSR20181377. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  162. Lai, E.C. Micro RNAs Are Complementary to 3′ UTR Sequence Motifs That Mediate Negative Post-Transcriptional Regulation. Nat. Genet. 2002, 30, 363–364. [Google Scholar] [CrossRef] [PubMed]
  163. Jansson, M.D.; Lund, A.H. MicroRNA and Cancer. Mol. Oncol. 2012, 6, 590–610. [Google Scholar] [CrossRef] [PubMed]
  164. Di Leva, G.; Garofalo, M.; Croce, C.M. MicroRNAs in Cancer. Annu. Rev. Pathol. 2014, 9, 287–314. [Google Scholar] [CrossRef] [Green Version]
  165. Pardini, B.; De Maria, D.; Francavilla, A.; Di Gaetano, C.; Ronco, G.; Naccarati, A. MicroRNAs as Markers of Progression in Cervical Cancer: A Systematic Review. BMC Cancer 2018, 18, 696. [Google Scholar] [CrossRef]
  166. He, Y.; Lin, J.; Ding, Y.; Liu, G.; Luo, Y.; Huang, M.; Xu, C.; Kim, T.-K.; Etheridge, A.; Lin, M.; et al. A Systematic Study on Dysregulated MicroRNAs in Cervical Cancer Development. Int. J. Cancer 2016, 138, 1312–1327. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  167. Gocze, K.; Gombos, K.; Juhasz, K.; Kovacs, K.; Kajtar, B.; Benczik, M.; Gocze, P.; Patczai, B.; Arany, I.; Ember, I. Unique MicroRNA Expression Profiles in Cervical Cancer. Anticancer Res. 2013, 33, 2561–2567. [Google Scholar]
  168. Wang, X.; Meyers, C.; Guo, M.; Zheng, Z.-M. Upregulation of P18Ink4c Expression by Oncogenic HPV E6 via P53-MiR-34a Pathway. Int. J. Cancer 2011, 129, 1362–1372. [Google Scholar] [CrossRef] [Green Version]
  169. Navarro, F.; Lieberman, J. MiR-34 and P53: New Insights into a Complex Functional Relationship. PLoS ONE 2015, 10, e0132767. [Google Scholar] [CrossRef]
  170. Hong, S.; Cheng, S.; Songock, W.; Bodily, J.; Laimins, L.A. Suppression of MicroRNA 424 Levels by Human Papillomaviruses Is Necessary for Differentiation-Dependent Genome Amplification. J. Virol. 2017, 91, e01712-17. [Google Scholar] [CrossRef] [Green Version]
  171. Xu, J.; Li, Y.; Wang, F.; Wang, X.; Cheng, B.; Ye, F.; Xie, X.; Zhou, C.; Lu, W. Suppressed MiR-424 Expression via Upregulation of Target Gene Chk1 Contributes to the Progression of Cervical Cancer. Oncogene 2013, 32, 976–987. [Google Scholar] [CrossRef]
  172. Wang, N.; Zhou, Y.; Zheng, L.; Li, H. MiR-31 Is an Independent Prognostic Factor and Functions as an Oncomir in Cervical Cancer via Targeting ARID1A. Gynecol. Oncol. 2014, 134, 129–137. [Google Scholar] [CrossRef]
  173. Berns, K.; Sonnenblick, A.; Gennissen, A.; Brohée, S.; Hijmans, E.M.; Evers, B.; Fumagalli, D.; Desmedt, C.; Loibl, S.; Denkert, C.; et al. Loss of ARID1A Activates ANXA1, Which Serves as a Predictive Biomarker for Trastuzumab Resistance. Clin. Cancer Res. 2016, 22, 5238–5248. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  174. Liu, X. Up-Regulation of MiR-20a by HPV16 E6 Exerts Growth-Promoting Effects by Targeting PDCD6 in Cervical Carcinoma Cells. Biomed. Pharmacother. 2018, 102, 996–1002. [Google Scholar] [CrossRef]
  175. Cheng, Y.; Geng, L.; Zhao, L.; Zuo, P.; Wang, J. Human Papillomavirus E6-Regulated MicroRNA-20b Promotes Invasion in Cervical Cancer by Targeting Tissue Inhibitor of Metalloproteinase 2. Mol. Med. Rep. 2017, 16, 5464–5470. [Google Scholar] [CrossRef] [PubMed]
  176. Wu, S.; Chen, H. Anti-Condyloma Acuminata Mechanism of MicroRNAs-375 Modulates HPV in Cervical Cancer Cells via the UBE3A and IGF-1R Pathway. Oncol. Lett. 2018, 16, 3241–3247. [Google Scholar] [CrossRef]
  177. Ulitsky, I.; Bartel, D.P. LincRNAs: Genomics, Evolution, and Mechanisms. Cell 2013, 154, 26–46. [Google Scholar] [CrossRef] [Green Version]
  178. Sun, Q.; Hao, Q.; Prasanth, K.V. Nuclear Long Noncoding RNAs: Key Regulators of Gene Expression. Trends Genet. 2018, 34, 142–157. [Google Scholar] [CrossRef] [PubMed]
  179. Dong, J.; Su, M.; Chang, W.; Zhang, K.; Wu, S.; Xu, T. Long Non-Coding RNAs on the Stage of Cervical Cancer (Review). Oncol. Rep. 2017, 38, 1923–1931. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  180. Cheng, T.; Huang, S. Roles of Non-Coding RNAs in Cervical Cancer Metastasis. Front. Oncol. 2021, 11, 646192. [Google Scholar] [CrossRef] [PubMed]
  181. Sharma, S.; Mandal, P.; Sadhukhan, T.; Roy Chowdhury, R.; Ranjan Mondal, N.; Chakravarty, B.; Chatterjee, T.; Roy, S.; Sengupta, S. Bridging Links between Long Noncoding RNA HOTAIR and HPV Oncoprotein E7 in Cervical Cancer Pathogenesis. Sci. Rep. 2015, 5, 11724. [Google Scholar] [CrossRef] [Green Version]
  182. Liu, M.; Jia, J.; Wang, X.; Liu, Y.; Wang, C.; Fan, R. Long Non-Coding RNA HOTAIR Promotes Cervical Cancer Progression through Regulating BCL2 via Targeting MiR-143-3p. Cancer Biol. Ther. 2018, 19, 391–399. [Google Scholar] [CrossRef] [Green Version]
  183. Zhang, D.; Zhou, X.; Zhang, J.; Zhou, Y.; Ying, J.; Wu, G.; Qian, J. Propofol Promotes Cell Apoptosis via Inhibiting HOTAIR Mediated MTOR Pathway in Cervical Cancer. Biochem. Biophys. Res. Commun. 2015, 468, 561–567. [Google Scholar] [CrossRef]
  184. Lee, M.; Kim, H.J.; Kim, S.W.; Park, S.-A.; Chun, K.-H.; Cho, N.H.; Song, Y.S.; Kim, Y.T. The Long Non-Coding RNA HOTAIR Increases Tumour Growth and Invasion in Cervical Cancer by Targeting the Notch Pathway. Oncotarget 2016, 7, 44558–44571. [Google Scholar] [CrossRef] [Green Version]
  185. Kim, H.J.; Lee, D.W.; Yim, G.W.; Nam, E.J.; Kim, S.; Kim, S.W.; Kim, Y.T. Long Non-Coding RNA HOTAIR Is Associated with Human Cervical Cancer Progression. Int. J. Oncol. 2015, 46, 521–530. [Google Scholar] [CrossRef] [Green Version]
  186. Sun, R.; Qin, C.; Jiang, B.; Fang, S.; Pan, X.; Peng, L.; Liu, Z.; Li, W.; Li, Y.; Li, G. Down-Regulation of MALAT1 Inhibits Cervical Cancer Cell Invasion and Metastasis by Inhibition of Epithelial-Mesenchymal Transition. Mol. Biosyst. 2016, 12, 952–962. [Google Scholar] [CrossRef]
  187. Jiang, Y.; Li, Y.; Fang, S.; Jiang, B.; Qin, C.; Xie, P.; Zhou, G.; Li, G. The Role of MALAT1 Correlates with HPV in Cervical Cancer. Oncol. Lett. 2014, 7, 2135–2141. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  188. Lu, H.; He, Y.; Lin, L.; Qi, Z.; Ma, L.; Li, L.; Su, Y. Long Non-Coding RNA MALAT1 Modulates Radiosensitivity of HR-HPV+ Cervical Cancer via Sponging MiR-145. Tumour Biol. 2016, 37, 1683–1691. [Google Scholar] [CrossRef] [PubMed]
  189. Yang, L.; Bai, H.-S.; Deng, Y.; Fan, L. High MALAT1 Expression Predicts a Poor Prognosis of Cervical Cancer and Promotes Cancer Cell Growth and Invasion. Eur. Rev. Med. Pharmacol. Sci. 2015, 19, 3187–3193. [Google Scholar] [PubMed]
  190. Peng, W.; Fan, H. Long Noncoding RNA CCHE1 Indicates a Poor Prognosis of Hepatocellular Carcinoma and Promotes Carcinogenesis via Activation of the ERK/MAPK Pathway. Biomed. Pharmacother. 2016, 83, 450–455. [Google Scholar] [CrossRef] [PubMed]
  191. Chen, Y.; Wang, C.-X.; Sun, X.-X.; Wang, C.; Liu, T.-F.; Wang, D.-J. Long Non-Coding RNA CCHE1 Overexpression Predicts a Poor Prognosis for Cervical Cancer. Eur. Rev. Med. Pharmacol. Sci. 2017, 21, 479–483. [Google Scholar]
  192. Wu, L.; Jin, L.; Zhang, W.; Zhang, L. Roles of Long Non-Coding RNA CCAT2 in Cervical Cancer Cell Growth and Apoptosis. Med. Sci. Monit. 2016, 22, 875–879. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  193. Cao, S.; Liu, W.; Li, F.; Zhao, W.; Qin, C. Decreased Expression of LncRNA GAS5 Predicts a Poor Prognosis in Cervical Cancer. Int. J. Clin. Exp. Pathol. 2014, 7, 6776–6783. [Google Scholar] [PubMed]
  194. Yang, W.; Hong, L.; Xu, X.; Wang, Q.; Huang, J.; Jiang, L. LncRNA GAS5 Suppresses the Tumorigenesis of Cervical Cancer by Downregulating MiR-196a and MiR-205. Tumour Biol. 2017, 39, 1010428317711315. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  195. Gao, J.; Liu, L.; Li, G.; Cai, M.; Tan, C.; Han, X.; Han, L. LncRNA GAS5 Confers the Radio Sensitivity of Cervical Cancer Cells via Regulating MiR-106b/IER3 Axis. Int. J. Biol. Macromol. 2019, 126, 994–1001. [Google Scholar] [CrossRef]
  196. Chen, Q.; Hu, L.; Huang, D.; Chen, K.; Qiu, X.; Qiu, B. Six-LncRNA Immune Prognostic Signature for Cervical Cancer. Front. Genet. 2020, 11, 533628. [Google Scholar] [CrossRef]
  197. Kristensen, L.S.; Andersen, M.S.; Stagsted, L.V.W.; Ebbesen, K.K.; Hansen, T.B.; Kjems, J. The Biogenesis, Biology and Characterization of Circular RNAs. Nat. Rev. Genet. 2019, 20, 675–691. [Google Scholar] [CrossRef]
  198. Zhang, H.; Jiang, L.-H.; Sun, D.-W.; Hou, J.-C.; Ji, Z.-L. CircRNA: A Novel Type of Biomarker for Cancer. Breast Cancer 2018, 25, 1–7. [Google Scholar] [CrossRef]
  199. Han, Y.-N.; Xia, S.-Q.; Zhang, Y.-Y.; Zheng, J.-H.; Li, W. Circular RNAs: A Novel Type of Biomarker and Genetic Tools in Cancer. Oncotarget 2017, 8, 64551–64563. [Google Scholar] [CrossRef]
  200. Chaichian, S.; Shafabakhsh, R.; Mirhashemi, S.M.; Moazzami, B.; Asemi, Z. Circular RNAs: A Novel Biomarker for Cervical Cancer. J. Cell. Physiol. 2020, 235, 718–724. [Google Scholar] [CrossRef]
  201. Gao, Y.-L.; Zhang, M.-Y.; Xu, B.; Han, L.-J.; Lan, S.-F.; Chen, J.; Dong, Y.-J.; Cao, L.-L. Circular RNA Expression Profiles Reveal That Hsa_circ_0018289 Is Up-Regulated in Cervical Cancer and Promotes the Tumorigenesis. Oncotarget 2017, 8, 86625–86633. [Google Scholar] [CrossRef] [Green Version]
  202. Zhang, J.; Zhao, X.; Zhang, J.; Zheng, X.; Li, F. Circular RNA Hsa_circ_0023404 Exerts an Oncogenic Role in Cervical Cancer through Regulating MiR-136/TFCP2/YAP Pathway. Biochem. Biophys. Res. Commun. 2018, 501, 428–433. [Google Scholar] [CrossRef] [PubMed]
  203. Cai, H.; Zhang, P.; Xu, M.; Yan, L.; Liu, N.; Wu, X. Circular RNA Hsa_circ_0000263 Participates in Cervical Cancer Development by Regulating Target Gene of MiR-150-5p. J. Cell. Physiol. 2019, 234, 11391–11400. [Google Scholar] [CrossRef] [PubMed]
  204. Tian, J.-J.D.C.; Liang, L. Involvement of Circular RNA SMARCA5/MicroRNA-620 Axis in the Regulation of Cervical Cancer Cell Proliferation, Invasion and Migration. Eur. Rev. Med. Pharmacol. Sci. 2018, 22, 8589–8598. [Google Scholar] [CrossRef] [PubMed]
  205. Guo, J.; Chen, M.; Ai, G.; Mao, W.; Li, H.; Zhou, J. Hsa_circ_0023404 Enhances Cervical Cancer Metastasis and Chemoresistance through VEGFA and Autophagy Signaling by Sponging MiR-5047. Biomed. Pharmacother. 2019, 115, 108957. [Google Scholar] [CrossRef]
  206. Song, T.; Xu, A.; Zhang, Z.; Gao, F.; Zhao, L.; Chen, X.; Gao, J.; Kong, X. CircRNA Hsa_circRNA_101996 Increases Cervical Cancer Proliferation and Invasion through Activating TPX2 Expression by Restraining MiR-8075. J. Cell. Physiol. 2019, 234, 14296–14305. [Google Scholar] [CrossRef]
  207. Hu, C.; Wang, Y.; Li, A.; Zhang, J.; Xue, F.; Zhu, L. Overexpressed Circ_0067934 Acts as an Oncogene to Facilitate Cervical Cancer Progression via the MiR-545/EIF3C Axis. J. Cell. Physiol. 2019, 234, 9225–9232. [Google Scholar] [CrossRef] [PubMed]
  208. Zhao, J.; Lee, E.E.; Kim, J.; Yang, R.; Chamseddin, B.; Ni, C.; Gusho, E.; Xie, Y.; Chiang, C.-M.; Buszczak, M.; et al. Transforming Activity of an Oncoprotein-Encoding Circular RNA from Human Papillomavirus. Nat. Commun. 2019, 10, 2300. [Google Scholar] [CrossRef] [Green Version]
  209. Heidrich, I.; Ačkar, L.; Mossahebi Mohammadi, P.; Pantel, K. Liquid Biopsies: Potential and Challenges. Int. J. Cancer 2021, 148, 528–545. [Google Scholar] [CrossRef] [PubMed]
  210. Cafforio, P.; Palmirotta, R.; Lovero, D.; Cicinelli, E.; Cormio, G.; Silvestris, E.; Porta, C.; D’Oronzo, S. Liquid Biopsy in Cervical Cancer: Hopes and Pitfalls. Cancers 2021, 13, 3968. [Google Scholar] [CrossRef]
  211. Vasseur, A.; Kiavue, N.; Bidard, F.-C.; Pierga, J.-Y.; Cabel, L. Clinical Utility of Circulating Tumor Cells: An Update. Mol. Oncol. 2021, 15, 1647–1666. [Google Scholar] [CrossRef] [PubMed]
  212. Du, K.; Huang, Q.; Bu, J.; Zhou, J.; Huang, Z.; Li, J. Circulating Tumor Cells Counting Act as a Potential Prognostic Factor in Cervical Cancer. Technol. Cancer Res. Treat. 2020, 19, 1533033820957005. [Google Scholar] [CrossRef]
  213. Wen, Y.-F.; Cheng, T.-T.; Chen, X.-L.; Huang, W.-J.; Peng, H.-H.; Zhou, T.-C.; Lin, X.-D.; Zeng, L.-S. Elevated Circulating Tumor Cells and Squamous Cell Carcinoma Antigen Levels Predict Poor Survival for Patients with Locally Advanced Cervical Cancer Treated with Radiotherapy. PLoS ONE 2018, 13, e0204334. [Google Scholar] [CrossRef]
  214. Tewari, K.S.; Sill, M.W.; Monk, B.J.; Penson, R.T.; Moore, D.H.; Lankes, H.A.; Ramondetta, L.M.; Landrum, L.M.; Randall, L.M.; Oaknin, A.; et al. Circulating Tumor Cells In Advanced Cervical Cancer: NRG Oncology-Gynecologic Oncology Group Study 240 (NCT 00803062). Mol. Cancer Ther. 2020, 19, 2363–2370. [Google Scholar] [CrossRef] [PubMed]
  215. Palmirotta, R.; Lovero, D.; Cafforio, P.; Felici, C.; Mannavola, F.; Pellè, E.; Quaresmini, D.; Tucci, M.; Silvestris, F. Liquid Biopsy of Cancer: A Multimodal Diagnostic Tool in Clinical Oncology. Ther. Adv. Med. Oncol. 2018, 10, 1758835918794630. [Google Scholar] [CrossRef] [PubMed]
  216. Liu, K.; Tong, H.; Li, T.; Chen, Y.; Mao, X. Potential Value of Circulating Tumor DNA in Gynecological Tumors. Am. J. Transl. Res. 2020, 12, 3225–3233. [Google Scholar]
  217. Tian, J.; Geng, Y.; Lv, D.; Li, P.; Cordova, M.; Liao, Y.; Tian, X.; Zhang, X.; Zhang, Q.; Zou, K.; et al. Using Plasma Cell-Free DNA to Monitor the Chemoradiotherapy Course of Cervical Cancer. Int. J. Cancer 2019, 145, 2547–2557. [Google Scholar] [CrossRef]
  218. Lee, S.-Y.; Chae, D.-K.; An, J.; Yoo, S.; Jung, S.; Chae, C.H.; Bhak, J.; Kim, B.C.; Cho, D.-H. Combinatory Analysis of Cell-Free and Circulating Tumor Cell DNAs Provides More Variants for Cancer Treatment. Anticancer Res. 2019, 39, 6595–6602. [Google Scholar] [CrossRef]
  219. Lee, S.-Y.; Chae, D.-K.; Lee, S.-H.; Lim, Y.; An, J.; Chae, C.H.; Kim, B.C.; Bhak, J.; Bolser, D.; Cho, D.-H. Efficient Mutation Screening for Cervical Cancers from Circulating Tumor DNA in Blood. BMC Cancer 2020, 20, 694. [Google Scholar] [CrossRef]
  220. Tian, X.; Ge, D.; Zhang, F.; Zhang, B.; Bai, W.; Xu, X.; Li, Z.; Cao, Y.; Li, P.; Zou, K.; et al. Dynamic Analysis of Circulating Tumor DNA to Predict Prognosis and Monitor Therapeutic Response in Metastatic Relapsed Cervical Cancer. Int. J. Cancer 2021, 148, 921–931. [Google Scholar] [CrossRef] [PubMed]
  221. Dong, S.M.; Pai, S.I.; Rha, S.-H.; Hildesheim, A.; Kurman, R.J.; Schwartz, P.E.; Mortel, R.; McGowan, L.; Greenberg, M.D.; Barnes, W.A.; et al. Detection and Quantitation of Human Papillomavirus DNA in the Plasma of Patients with Cervical Carcinoma. Cancer Epidemiol. Biomark. Prev. 2002, 11, 3–6. [Google Scholar]
  222. Shimada, T.; Yamaguchi, N.; Nishida, N.; Yamasaki, K.; Miura, K.; Katamine, S.; Masuzaki, H. Human Papillomavirus DNA in Plasma of Patients with HPV16 DNA-Positive Uterine Cervical Cancer. Jpn. J. Clin. Oncol. 2010, 40, 420–424. [Google Scholar] [CrossRef] [PubMed]
  223. Kang, Z.; Stevanović, S.; Hinrichs, C.S.; Cao, L. Circulating Cell-Free DNA for Metastatic Cervical Cancer Detection, Genotyping, and Monitoring. Clin. Cancer Res. 2017, 23, 6856–6862. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  224. Gu, Y.; Wan, C.; Qiu, J.; Cui, Y.; Jiang, T.; Zhuang, Z. Circulating HPV CDNA in the Blood as a Reliable Biomarker for Cervical Cancer: A Meta-Analysis. PLoS ONE 2020, 15, e0224001. [Google Scholar] [CrossRef] [PubMed]
  225. Yu, J.; Wang, Y.; Dong, R.; Huang, X.; Ding, S.; Qiu, H. Circulating MicroRNA-218 Was Reduced in Cervical Cancer and Correlated with Tumor Invasion. J. Cancer Res. Clin. Oncol. 2012, 138, 671–674. [Google Scholar] [CrossRef]
  226. Ma, Q.; Wan, G.; Wang, S.; Yang, W.; Zhang, J.; Yao, X. Serum MicroRNA-205 as a Novel Biomarker for Cervical Cancer Patients. Cancer Cell Int. 2014, 14, 81. [Google Scholar] [CrossRef] [Green Version]
  227. Farzanehpour, M.; Mozhgani, S.-H.; Jalilvand, S.; Faghihloo, E.; Akhavan, S.; Salimi, V.; Azad, T.M. Serum and Tissue MiRNAs: Potential Biomarkers for the Diagnosis of Cervical Cancer. Virol. J. 2019, 16, 116. [Google Scholar] [CrossRef] [Green Version]
  228. Zhao, S.; Yao, D.; Chen, J.; Ding, N. Circulating MiRNA-20a and MiRNA-203 for Screening Lymph Node Metastasis in Early Stage Cervical Cancer. Genet. Test. Mol. Biomark. 2013, 17, 631–636. [Google Scholar] [CrossRef]
  229. Jiang, W.; Pan, J.J.; Deng, Y.H.; Liang, M.R.; Yao, L.H. Down-Regulated Serum MicroRNA-101 Is Associated with Aggressive Progression and Poor Prognosis of Cervical Cancer. J. Gynecol. Oncol. 2017, 28, e75. [Google Scholar] [CrossRef] [Green Version]
  230. Qiu, H.; Liang, D.; Liu, L.; Xiang, Q.; Yi, Z.; Ji, Y. A Novel Circulating MiRNA-Based Signature for the Diagnosis and Prognosis Prediction of Early-Stage Cervical Cancer. Technol. Cancer Res. Treat. 2020, 19, 1533033820970667. [Google Scholar] [CrossRef]
  231. Jia, W.; Wu, Y.; Zhang, Q.; Gao, G.E.; Zhang, C.; Xiang, Y. Expression Profile of Circulating MicroRNAs as a Promising Fingerprint for Cervical Cancer Diagnosis and Monitoring. Mol. Clin. Oncol. 2015, 3, 851–858. [Google Scholar] [CrossRef] [Green Version]
  232. Hu, X.; Schwarz, J.K.; Lewis, J.S.; Huettner, P.C.; Rader, J.S.; Deasy, J.O.; Grigsby, P.W.; Wang, X. A MicroRNA Expression Signature for Cervical Cancer Prognosis. Cancer Res. 2010, 70, 1441–1448. [Google Scholar] [CrossRef] [Green Version]
  233. Mbeunkui, F.; Johann, D.J. Cancer and the Tumor Microenvironment: A Review of an Essential Relationship. Cancer Chemother. Pharmacol. 2009, 63, 571–582. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  234. Shamseddine, A.A.; Burman, B.; Lee, N.Y.; Zamarin, D.; Riaz, N. Tumor Immunity and Immunotherapy for HPV-Related Cancers. Cancer Discov. 2021, 11, 1896–1912. [Google Scholar] [CrossRef] [PubMed]
  235. Duranti, S.; Pietragalla, A.; Daniele, G.; Nero, C.; Ciccarone, F.; Scambia, G.; Lorusso, D. Role of Immune Checkpoint Inhibitors in Cervical Cancer: From Preclinical to Clinical Data. Cancers 2021, 13, 2089. [Google Scholar] [CrossRef] [PubMed]
  236. Enwere, E.K.; Kornaga, E.N.; Dean, M.; Koulis, T.A.; Phan, T.; Kalantarian, M.; Köbel, M.; Ghatage, P.; Magliocco, A.M.; Lees-Miller, S.P.; et al. Expression of PD-L1 and Presence of CD8-Positive T Cells in Pre-Treatment Specimens of Locally Advanced Cervical Cancer. Mod. Pathol. 2017, 30, 577–586. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  237. Ohno, A.; Iwata, T.; Katoh, Y.; Taniguchi, S.; Tanaka, K.; Nishio, H.; Nakamura, M.; Morisada, T.; Chen, G.; Saito, M.; et al. Tumor-Infiltrating Lymphocytes Predict Survival Outcomes in Patients with Cervical Cancer Treated with Concurrent Chemoradiotherapy. Gynecol. Oncol. 2020, 159, 329–334. [Google Scholar] [CrossRef]
  238. Fan, P.; Li, X.; Feng, Y.; Cai, H.; Dong, D.; Peng, Y.; Yao, X.; Guo, Y.; Ma, M.; Dong, T.; et al. PD-1 Expression Status on CD8+ Tumour Infiltrating Lymphocytes Associates With Survival in Cervical Cancer. Front. Oncol. 2021, 11, 678758. [Google Scholar] [CrossRef]
  239. Gu, X.; Dong, M.; Liu, Z.; Mi, Y.; Yang, J.; Zhang, Z.; Liu, K.; Jiang, L.; Zhang, Y.; Dong, S.; et al. Elevated PD-L1 Expression Predicts Poor Survival Outcomes in Patients with Cervical Cancer. Cancer Cell Int. 2019, 19, 146. [Google Scholar] [CrossRef]
  240. Litwin, T.R.; Irvin, S.R.; Chornock, R.L.; Sahasrabuddhe, V.V.; Stanley, M.; Wentzensen, N. Infiltrating T-Cell Markers in Cervical Carcinogenesis: A Systematic Review and Meta-Analysis. Br. J. Cancer 2021, 124, 831–841. [Google Scholar] [CrossRef]
  241. Shang, B.; Liu, Y.; Jiang, S.; Liu, Y. Prognostic Value of Tumor-Infiltrating FoxP3+ Regulatory T Cells in Cancers: A Systematic Review and Meta-Analysis. Sci. Rep. 2015, 5, 15179. [Google Scholar] [CrossRef] [Green Version]
  242. Someya, M.; Tsuchiya, T.; Fukushima, Y.; Hasegawa, T.; Hori, M.; Kitagawa, M.; Gocho, T.; Mafune, S.; Ikeuchi, Y.; Hirohashi, Y.; et al. Prediction of Treatment Response from the Microenvironment of Tumor Immunity in Cervical Cancer Patients Treated with Chemoradiotherapy. Med. Mol. Morphol. 2021, 54, 245–252. [Google Scholar] [CrossRef]
  243. Ferguson, R.; Ramanakumar, A.V.; Richardson, H.; Tellier, P.-P.; Coutlée, F.; Franco, E.L.; Roger, M. Human Leukocyte Antigen (HLA)-E and HLA-G Polymorphisms in Human Papillomavirus Infection Susceptibility and Persistence. Hum. Immunol. 2011, 72, 337–341. [Google Scholar] [CrossRef] [PubMed]
  244. Hemmat, N.; Baghi, H.B. Human Papillomavirus E5 Protein, the Undercover Culprit of Tumorigenesis. Infect. Agents Cancer 2018, 13, 31. [Google Scholar] [CrossRef] [PubMed]
  245. Ashrafi, G.H.; Haghshenas, M.R.; Marchetti, B.; O’Brien, P.M.; Campo, M.S. E5 Protein of Human Papillomavirus Type 16 Selectively Downregulates Surface HLA Class I. Int. J. Cancer 2005, 113, 276–283. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  246. Liu, C.; Lu, J.; Tian, H.; Du, W.; Zhao, L.; Feng, J.; Yuan, D.; Li, Z. Increased Expression of PD-L1 by the Human Papillomavirus 16 E7 Oncoprotein Inhibits Anticancer Immunity. Mol. Med. Rep. 2017, 15, 1063–1070. [Google Scholar] [CrossRef] [Green Version]
  247. Qian, B.-Z.; Pollard, J.W. Macrophage Diversity Enhances Tumor Progression and Metastasis. Cell 2010, 141, 39–51. [Google Scholar] [CrossRef] [Green Version]
  248. Biswas, S.K.; Allavena, P.; Mantovani, A. Tumor-Associated Macrophages: Functional Diversity, Clinical Significance, and Open Questions. Semin. Immunopathol. 2013, 35, 585–600. [Google Scholar] [CrossRef]
  249. Poh, A.R.; Ernst, M. Targeting Macrophages in Cancer: From Bench to Bedside. Front. Oncol. 2018, 8, 49. [Google Scholar] [CrossRef] [Green Version]
  250. Chen, X.-J.; Han, L.-F.; Wu, X.-G.; Wei, W.-F.; Wu, L.-F.; Yi, H.-Y.; Yan, R.-M.; Bai, X.-Y.; Zhong, M.; Yu, Y.-H.; et al. Clinical Significance of CD163+ and CD68+ Tumor-Associated Macrophages in High-Risk HPV-Related Cervical Cancer. J. Cancer 2017, 8, 3868–3875. [Google Scholar] [CrossRef] [Green Version]
  251. Qiu, Q.; Zhou, Q.; Luo, A.; Li, X.; Li, K.; Li, W.; Yu, M.; Amanullah, M.; Lu, B.; Lu, W.; et al. Integrated Analysis of Virus and Host Transcriptomes in Cervical Cancer in Asian and Western Populations. Genomics 2021, 113, 1554–1564. [Google Scholar] [CrossRef] [PubMed]
  252. Petrillo, M.; Zannoni, G.F.; Martinelli, E.; Pedone Anchora, L.; Ferrandina, G.; Tropeano, G.; Fagotti, A.; Scambia, G. Polarisation of Tumor-Associated Macrophages toward M2 Phenotype Correlates with Poor Response to Chemoradiation and Reduced Survival in Patients with Locally Advanced Cervical Cancer. PLoS ONE 2015, 10, e0136654. [Google Scholar] [CrossRef]
  253. Ping, Q.; Yan, R.; Cheng, X.; Wang, W.; Zhong, Y.; Hou, Z.; Shi, Y.; Wang, C.; Li, R. Cancer-Associated Fibroblasts: Overview, Progress, Challenges, and Directions. Cancer Gene Ther. 2021, 28, 984–999. [Google Scholar] [CrossRef]
  254. Xiao, L.; Zhu, H.; Shu, J.; Gong, D.; Zheng, D.; Gao, J. Overexpression of TGF-Β1 and SDF-1 in Cervical Cancer-Associated Fibroblasts Promotes Cell Growth, Invasion and Migration. Arch. Gynecol. Obstet. 2021, 1–14. [Google Scholar] [CrossRef] [PubMed]
  255. Fullár, A.; Dudás, J.; Oláh, L.; Hollósi, P.; Papp, Z.; Sobel, G.; Karászi, K.; Paku, S.; Baghy, K.; Kovalszky, I. Remodeling of Extracellular Matrix by Normal and Tumor-Associated Fibroblasts Promotes Cervical Cancer Progression. BMC Cancer 2015, 15, 256. [Google Scholar] [CrossRef] [Green Version]
  256. Wei, W.-F.; Chen, X.-J.; Liang, L.-J.; Yu, L.; Wu, X.-G.; Zhou, C.-F.; Wang, Z.-C.; Fan, L.-S.; Hu, Z.; Liang, L.; et al. Periostin+ Cancer-Associated Fibroblasts Promote Lymph Node Metastasis by Impairing the Lymphatic Endothelial Barriers in Cervical Squamous Cell Carcinoma. Mol. Oncol. 2021, 15, 210–227. [Google Scholar] [CrossRef]
  257. Chu, T.-Y.; Yang, J.-T.; Huang, T.-H.; Liu, H.-W. Crosstalk with Cancer-Associated Fibroblasts Increases the Growth and Radiation Survival of Cervical Cancer Cells. Radiat. Res. 2014, 181, 540–547. [Google Scholar] [CrossRef]
  258. Helmink, B.A.; Khan, M.A.W.; Hermann, A.; Gopalakrishnan, V.; Wargo, J.A. The Microbiome, Cancer, and Cancer Therapy. Nat. Med. 2019, 25, 377–388. [Google Scholar] [CrossRef]
  259. Klein, C.; Kahesa, C.; Mwaiselage, J.; West, J.T.; Wood, C.; Angeletti, P.C. How the Cervical Microbiota Contributes to Cervical Cancer Risk in Sub-Saharan Africa. Front. Cell. Infect. Microbiol. 2020, 10, 23. [Google Scholar] [CrossRef] [Green Version]
  260. Norenhag, J.; Du, J.; Olovsson, M.; Verstraelen, H.; Engstrand, L.; Brusselaers, N. The Vaginal Microbiota, Human Papillomavirus and Cervical Dysplasia: A Systematic Review and Network Meta-Analysis. BJOG 2020, 127, 171–180. [Google Scholar] [CrossRef]
  261. Petrova, M.I.; Reid, G.; Vaneechoutte, M.; Lebeer, S. Lactobacillus Iners: Friend or Foe? Trends Microbiol. 2017, 25, 182–191. [Google Scholar] [CrossRef] [PubMed]
  262. Curty, G.; de Carvalho, P.S.; Soares, M.A. The Role of the Cervicovaginal Microbiome on the Genesis and as a Biomarker of Premalignant Cervical Intraepithelial Neoplasia and Invasive Cervical Cancer. Int. J. Mol. Sci. 2019, 21, 222. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  263. Swidsinski, A.; Mendling, W.; Loening-Baucke, V.; Ladhoff, A.; Swidsinski, S.; Hale, L.P.; Lochs, H. Adherent Biofilms in Bacterial Vaginosis. Obstet. Gynecol. 2005, 106, 1013–1023. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  264. Di Paola, M.; Sani, C.; Clemente, A.M.; Iossa, A.; Perissi, E.; Castronovo, G.; Tanturli, M.; Rivero, D.; Cozzolino, F.; Cavalieri, D.; et al. Characterization of Cervico-Vaginal Microbiota in Women Developing Persistent High-Risk Human Papillomavirus Infection. Sci. Rep. 2017, 7, 10200. [Google Scholar] [CrossRef]
  265. Kostic, A.D.; Chun, E.; Robertson, L.; Glickman, J.N.; Gallini, C.A.; Michaud, M.; Clancy, T.E.; Chung, D.C.; Lochhead, P.; Hold, G.L.; et al. Fusobacterium Nucleatum Potentiates Intestinal Tumorigenesis and Modulates the Tumor-Immune Microenvironment. Cell Host Microbe 2013, 14, 207–215. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  266. Mitra, A.; MacIntyre, D.A.; Lee, Y.S.; Smith, A.; Marchesi, J.R.; Lehne, B.; Bhatia, R.; Lyons, D.; Paraskevaidis, E.; Li, J.V.; et al. Cervical Intraepithelial Neoplasia Disease Progression Is Associated with Increased Vaginal Microbiome Diversity. Sci. Rep. 2015, 5, 16865. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  267. Audirac-Chalifour, A.; Torres-Poveda, K.; Bahena-Román, M.; Téllez-Sosa, J.; Martínez-Barnetche, J.; Cortina-Ceballos, B.; López-Estrada, G.; Delgado-Romero, K.; Burguete-García, A.I.; Cantú, D.; et al. Cervical Microbiome and Cytokine Profile at Various Stages of Cervical Cancer: A Pilot Study. PLoS ONE 2016, 11, e0153274. [Google Scholar] [CrossRef] [PubMed]
  268. Tsakmaklis, A.; Vehreschild, M.; Farowski, F.; Trommer, M.; Kohler, C.; Herter, J.; Marnitz, S. Changes in the Cervical Microbiota of Cervical Cancer Patients after Primary Radio-Chemotherapy. Int. J. Gynecol. Cancer 2020, 30, 1326–1330. [Google Scholar] [CrossRef]
  269. Sims, T.T.; El Alam, M.B.; Karpinets, T.V.; Dorta-Estremera, S.; Hegde, V.L.; Nookala, S.; Yoshida-Court, K.; Wu, X.; Biegert, G.W.G.; Delgado Medrano, A.Y.; et al. Gut Microbiome Diversity Is an Independent Predictor of Survival in Cervical Cancer Patients Receiving Chemoradiation. Commun. Biol. 2021, 4, 237. [Google Scholar] [CrossRef]
Figure 1. Clinical risk factors in cervical cancer. After primary, curatively intended surgery, Sedlis’ and Peters’ criteria were established to estimate the risk of pelvic and extra-pelvic recurrence. These risk factors include tumor size, deep stromal invasion, involved parametria, lymphovascular space invasion (LVSI) and the presence of lymph node metastases. Created with BioRender.com (accessed on 5 September 2021).
Figure 1. Clinical risk factors in cervical cancer. After primary, curatively intended surgery, Sedlis’ and Peters’ criteria were established to estimate the risk of pelvic and extra-pelvic recurrence. These risk factors include tumor size, deep stromal invasion, involved parametria, lymphovascular space invasion (LVSI) and the presence of lymph node metastases. Created with BioRender.com (accessed on 5 September 2021).
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Figure 2. E6 and E7 increase radiosensitivity of HPV-positive cervical cancer cells. An increased sensitivity to radiation by HPV-positive malignancies has been linked to several signaling pathways, including regulation of cell cycle progression, apoptosis and chromosomal instability. Shown is a simplified illustration of DNA damage repair signaling pathways impaired by altered TP53 and p16 activity. After the downregulation of retinoblastoma protein (Rb) by viral oncoprotein E7, increasing p16 levels hamper homologous recombination (HR) regulated via cyclin D1 and subsequently the RAD51/BRCA2 axis. Rb loss further diminishes the non-homologous end-joining (NHEJ) repair pathway. Cell cycle progression and G1-S transition are promoted by the release of the transcription factor E2F and the inhibition of cyclin-dependent kinase (CDK) inhibitors p21 and p27, while in contrast frequently triggered G2/M arrest reorders and arrests cancer cells at vulnerable stages of the cell cycle. Abbreviations: ATM: ataxia telangiectasia mutated; CHK2: checkpoint kinase 2; HPV: human papilloma virus. Adapted from “P53 Regulation and Signaling”, by BioRender.com (accessed on 2021). Retrieved from https://app.biorender.com/biorender-templates (accessed on October 2021).
Figure 2. E6 and E7 increase radiosensitivity of HPV-positive cervical cancer cells. An increased sensitivity to radiation by HPV-positive malignancies has been linked to several signaling pathways, including regulation of cell cycle progression, apoptosis and chromosomal instability. Shown is a simplified illustration of DNA damage repair signaling pathways impaired by altered TP53 and p16 activity. After the downregulation of retinoblastoma protein (Rb) by viral oncoprotein E7, increasing p16 levels hamper homologous recombination (HR) regulated via cyclin D1 and subsequently the RAD51/BRCA2 axis. Rb loss further diminishes the non-homologous end-joining (NHEJ) repair pathway. Cell cycle progression and G1-S transition are promoted by the release of the transcription factor E2F and the inhibition of cyclin-dependent kinase (CDK) inhibitors p21 and p27, while in contrast frequently triggered G2/M arrest reorders and arrests cancer cells at vulnerable stages of the cell cycle. Abbreviations: ATM: ataxia telangiectasia mutated; CHK2: checkpoint kinase 2; HPV: human papilloma virus. Adapted from “P53 Regulation and Signaling”, by BioRender.com (accessed on 2021). Retrieved from https://app.biorender.com/biorender-templates (accessed on October 2021).
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Figure 3. Exemplary illustration of dysregulated signaling pathways and their interactions in cervical cancer. The schematic shows the nexus of dysregulated, activated or discontinued signaling pathways related to PLK3. Upon activation by DNA damage, hypoxic or oxidative stress PLK3 is activated and impacts on apoptosis, cell cycle progression, proliferation and survival. Details are given in the text. Abbreviations: Casp8: caspase 8, Casp3: caspase 3; Chk2: checkpoint kinase 2 FADD: Fas-associated protein with death domain; HIF1a: hypoxia-inducible factor 1-alpha; PARP: poly (ADP-ribose) polymerase; PLK3: polo-like kinase 3, PTEN: phosphatase and tensin homolog; VEGF: vascular endothelial growth factor. Adapted from “FAS Activation Pathway Initiates Cancer Cell Apoptosis” and “Novel Pharmacotherapies for Diabetic Macular Edema (DME)”, by BioRender.com (accessed on 2021). Retrieved from https://app.biorender.com/biorender-templates (accessed on October 2021).
Figure 3. Exemplary illustration of dysregulated signaling pathways and their interactions in cervical cancer. The schematic shows the nexus of dysregulated, activated or discontinued signaling pathways related to PLK3. Upon activation by DNA damage, hypoxic or oxidative stress PLK3 is activated and impacts on apoptosis, cell cycle progression, proliferation and survival. Details are given in the text. Abbreviations: Casp8: caspase 8, Casp3: caspase 3; Chk2: checkpoint kinase 2 FADD: Fas-associated protein with death domain; HIF1a: hypoxia-inducible factor 1-alpha; PARP: poly (ADP-ribose) polymerase; PLK3: polo-like kinase 3, PTEN: phosphatase and tensin homolog; VEGF: vascular endothelial growth factor. Adapted from “FAS Activation Pathway Initiates Cancer Cell Apoptosis” and “Novel Pharmacotherapies for Diabetic Macular Edema (DME)”, by BioRender.com (accessed on 2021). Retrieved from https://app.biorender.com/biorender-templates (accessed on October 2021).
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Figure 4. Tumor microenvironment in cervical cancer (1). Immune cells, endothelial cells, fibroblasts, signaling proteins and extracellular matrix molecules within the tumor TME and in lymph nodes impact on tumor biology, therapy response and survival. The adaptive antitumor immune response is mainly mediated by activated CD8+ T cells, while regulatory T cells (Tregs) and myeloid-derived suppressor cells suppress the immune response (2). Further, the polarization status of tumor associated macrophages (TAMs) impact on treatment response and survival, while natural killer (NK) cell activity is modulated and hindered by HPV+ tumor cells (3). Moreover, cancer-associated fibroblasts (CAFs) promote apoptotic resistance, proliferation, angiogenesis, inflammation, invasion and metastatic spread of cancer cells in a paracrine manner (4). Details are given in the text. Abbreviations: CTLA4: cytotoxic T-lymphocyte-associated protein 4; COX2: cyclooxygenase 2; GM-CSF: granulocyte macrophage colony-stimulating factor; PD1: programmed death 1; SDF-1: stromal cell-derived factor 1; TCR: T-cell receptor; TGF-ß1 transforming growth factor beta 1. Adapted from “T Cell Deactivation vs. Activation”, “Tumor-Specific T Cell Induction and Function” and “Cancer Cell Mutations Affect Tumor Microenvironment”, by BioRender.com (accessed on 2021). Retrieved from https://app.biorender.com/biorender-templates (accessed on October 2021).
Figure 4. Tumor microenvironment in cervical cancer (1). Immune cells, endothelial cells, fibroblasts, signaling proteins and extracellular matrix molecules within the tumor TME and in lymph nodes impact on tumor biology, therapy response and survival. The adaptive antitumor immune response is mainly mediated by activated CD8+ T cells, while regulatory T cells (Tregs) and myeloid-derived suppressor cells suppress the immune response (2). Further, the polarization status of tumor associated macrophages (TAMs) impact on treatment response and survival, while natural killer (NK) cell activity is modulated and hindered by HPV+ tumor cells (3). Moreover, cancer-associated fibroblasts (CAFs) promote apoptotic resistance, proliferation, angiogenesis, inflammation, invasion and metastatic spread of cancer cells in a paracrine manner (4). Details are given in the text. Abbreviations: CTLA4: cytotoxic T-lymphocyte-associated protein 4; COX2: cyclooxygenase 2; GM-CSF: granulocyte macrophage colony-stimulating factor; PD1: programmed death 1; SDF-1: stromal cell-derived factor 1; TCR: T-cell receptor; TGF-ß1 transforming growth factor beta 1. Adapted from “T Cell Deactivation vs. Activation”, “Tumor-Specific T Cell Induction and Function” and “Cancer Cell Mutations Affect Tumor Microenvironment”, by BioRender.com (accessed on 2021). Retrieved from https://app.biorender.com/biorender-templates (accessed on October 2021).
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Table 1. Clinical features and postoperative surgical and pathologic risk factors to assess the risk of recurrence in cervical cancer.
Table 1. Clinical features and postoperative surgical and pathologic risk factors to assess the risk of recurrence in cervical cancer.
Intermediate Risk (Sedlis’ Criteria)High Risk (Peters’ Criteria)
LVSI plus deep stromal invasion (outer third)Positive surgical margins
LVSI plus middle stromal invasion (one-third) and tumor size ≥ 2 cm Detection of pathologically-confirmed lymph node metastases
LVSI plus superficial stromal invasion (inner third) and tumor size ≥ 5 cmExtension into the parametrial tissue
No LVSI but deep or middle stromal invasion and tumor size ≥ 4 cm
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Fleischmann, M.; Chatzikonstantinou, G.; Fokas, E.; Wichmann, J.; Christiansen, H.; Strebhardt, K.; Rödel, C.; Tselis, N.; Rödel, F. Molecular Markers to Predict Prognosis and Treatment Response in Uterine Cervical Cancer. Cancers 2021, 13, 5748. https://doi.org/10.3390/cancers13225748

AMA Style

Fleischmann M, Chatzikonstantinou G, Fokas E, Wichmann J, Christiansen H, Strebhardt K, Rödel C, Tselis N, Rödel F. Molecular Markers to Predict Prognosis and Treatment Response in Uterine Cervical Cancer. Cancers. 2021; 13(22):5748. https://doi.org/10.3390/cancers13225748

Chicago/Turabian Style

Fleischmann, Maximilian, Georgios Chatzikonstantinou, Emmanouil Fokas, Jörn Wichmann, Hans Christiansen, Klaus Strebhardt, Claus Rödel, Nikolaos Tselis, and Franz Rödel. 2021. "Molecular Markers to Predict Prognosis and Treatment Response in Uterine Cervical Cancer" Cancers 13, no. 22: 5748. https://doi.org/10.3390/cancers13225748

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