Next Article in Journal
Bardet–Biedl Syndrome—Multiple Kaleidoscope Images: Insight into Mechanisms of Genotype–Phenotype Correlations
Previous Article in Journal
Thousands of CpGs Show DNA Methylation Differences in ACPA-Positive Individuals
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Genes
Volume 12
Issue 9
10.3390/genes12091350
genes-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Multi-Dimensional Scaling Analysis of Key Regulatory Genes in Prostate Cancer Using the TCGA Database

by
Laura Boldrini
1,*,
Pinuccia Faviana
1,
Luca Galli
2,
Federico Paolieri
2,
Paola Anna Erba
2 and
Massimo Bardi
3
1
Department of Surgical, Medical, Molecular Pathology and Critical Area, University of Pisa, 56126 Pisa, Italy
2
Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, 56126 Pisa, Italy
3
Department of Psychology & Behavioral Neuroscience, Randolph-Macon College, Ashland, VA 23005, USA
*
Author to whom correspondence should be addressed.
Genes 2021, 12(9), 1350; https://doi.org/10.3390/genes12091350
Submission received: 31 May 2021 / Revised: 20 August 2021 / Accepted: 26 August 2021 / Published: 29 August 2021
(This article belongs to the Section Human Genomics and Genetic Diseases)
Browse Figures
Versions Notes

Abstract

:
Prostate cancer (PC) is a polygenic disease with multiple gene interactions. Therefore, a detailed analysis of its epidemiology and evaluation of risk factors can help to identify more accurate predictors of aggressive disease. We used the transcriptome data from a cohort of 243 patients from the Cancer Genome Atlas (TCGA) database. Key regulatory genes involved in proliferation activity, in the regulation of stress, and in the regulation of inflammation processes of the tumor microenvironment were selected to test a priori multi-dimensional scaling (MDS) models and create a combined score to better predict the patients’ survival and disease-free intervals. Survival was positively correlated with cortisol expression and negatively with Mini-Chromosome Maintenance 7 (MCM7) and Breast-Related Cancer Antigen2 (BRCA2) expression. The disease-free interval was negatively related to the expression of enhancer of zeste homolog 2 (EZH2), MCM7, BRCA2, and programmed cell death 1 ligand 1 (PD-L1). MDS suggested two separate pathways of activation in PC. Within these two dimensions three separate clusters emerged: (1) cortisol and brain-derived neurotrophic factor BDNF, (2) PD-L1 and cytotoxic-T-lymphocyte-associated protein 4 (CTL4); (3) and finally EZH2, MCM7, BRCA2, and c-Myc. We entered the three clusters of association shown in the MDS in several Kaplan–Meier analyses. It was found that only Cluster 3 was significantly related to the interval-disease free, indicating that patients with an overall higher activity of regulatory genes of proliferation and DNA repair had a lower probability to have a longer disease-free time. In conclusion, our data study provided initial evidence that selecting patients with a high grade of proliferation and DNA repair activity could lead to an early identification of an aggressive PC with a potentials for metastatic development.

1. Introduction

Prostate cancer (PC) is the second most frequent malignancy (after lung cancer) in men worldwide [1]. Global cancer statistics indicated over one million new cases per year causing about 350,000 deaths just in 2018 [2]. Mortality varies dramatically with age and with the stage of detection. When PC is detected later in older patients, mortality increases exponentially, considering that almost 60% of all deaths occur after 65 years of age [3,4]. Therefore, more precise tools to identify early signs of PC or even being able to assess the risk factors in healthy people are essential to reduce the most dramatic consequences of PC. The main problem in detecting PC is that it is often asymptomatic at the early stage; actually, early PC may require minimal or even no treatment [5]. The most frequent complaint is difficulty with urination, increased frequency, and nocturia, all symptoms that may also arise from prostatic hypertrophy.
When further assessments are warranted, the serum prostate-specific antigen (PSA) test is often used to screen for PC [6]. PSA is a glycoprotein normally expressed by prostate tissue. An increasing serum PSA level is usually associated with an advanced stage of the disease and, potentially, poor outcomes [7,8]. However, because men without cancer have also been found with elevated PSA, a tissue biopsy is the standard of care to confirm the presence of cancer. Therefore, Gleason score (GS) is also used in predicting patient outcomes: high GS implies increased tumor aggressiveness and an increased an risk of local and distant tumor spread with a worse prognosis [9,10,11]. However, GS evaluation can be inaccurate for risk stratification considering that sometimes low Gleason tumors may develop into an aggressive disease [12]. Therefore, besides increased PSA serum levels and high GS, other biologic features, such as identifying patterns with extravasated mucin in cases with more complex epithelial bridges, may confer a better prognosis [13].
Considering that PC is a polygenic disease with many gene–gene interactions, several studies have attempted to identify hub genes involved in PC [14]. Some of these works also tried to identify mutations occurring in abnormal prostatic development [15]. It is not unusual to find hundreds of genes highly related to PC progression, often including hubs whose involvement was previously completely unknown [16] Therefore, a detailed analysis of the molecular risk factors can help to understand the connection between genetic mutations and the role of the environment in triggering PC progression [17]. Recently, Seibert and colleagues described the development of a new polygenic hazard score for personalized genetic assessment of individual age associated risk of PC [18] based on single nucleotide polymorphisms. Other researchers used a weighted co-expression network analysis to identify clusters of highly correlated genes as a raking method to find specific monograms able to predict possible outcome for PC [14,19]. Even when studies revealed a large preponderance of specific clusters, such as a molecular taxonomy in which 74% of tumors falling into one of seven subtypes defined by specific gene fusions or mutations [20], patient stratification and outcome remain elusive [21]. Many challenges remain to integrate epidemiological studies with molecular investigations and clinical analyses to gain fundamental insights into how environmental, dietary, and lifestyle influences contribute to the development of PC, and thus a priori models selecting target genes involved in macroscopic functions could shed new light on this complex disease, as previous studies on different types of cancers have revealed [22].
The role of life events, such as dietary conditions and stress, appears to gain traction in recent studies [23]. Intriguingly, previous studies have tested the relationship between stressful life events and PC onset [24]. Cortisol has been showed to have a role in the life events correlated with PSA levels, suggesting an interaction between stress and the hypothalamic-pituitary-adrenal (HPA) axis in relation to cancer [25]. Fabre et al. then reinforced the idea that cortisol may have a role in PC [26]. More recently, the role of BDNF pathway has been investigated in prostate cancer, suggesting the activation of its pathway as a crucial step in disease progression [27].
The role of the tumor microenvironment (TME) as a mediator of metastatic activity has been also extensively evaluated in PC [19,28]. The main hypothesis is that inflammatory injury could prompt carcinogenesis by causing cellular stress and repeated genomic damage [29,30]. However, the role of inflammation in prostate carcinogenesis is still controversial, considering that some evidence showed that prostatic inflammation may confer a protective effect and decrease the rate of subsequent metastasis [31]. Immune checkpoint genes such as CTLA4 or PD-L1 have been extensively studied for therapeutic approach, even if with modest efficacy in PC [32]. Nevertheless, including data on the prostate immune microenvironment in a multi-factorial index of cancer aggressiveness could provide essential information in the effort to increase early detection.
One of the extensively studied prognostic markers in PC is cell proliferation activity. It has been shown that the expression of polycomb group protein EZH2 is strongly associated with short progression-free survival and with poor prognosis in many malignancies, including PC [33,34]. Another suggested molecular marker of PC aggressiveness is MCM7, a member of a DNA helicase complex involved in the initiation of DNA replication. MCM7, as well as the other five proteins of the family, is overexpressed in several human cancers, such as laryngeal [35], esophageal squamous cell carcinoma [36], and hepatocellular carcinoma [37]. MCM7 and EZH2 are prognostic markers in prostatectomy treated patients [38,39]. Recent studies described the role of EZH2 in DNA repair by regulating the cancer cell fate in response to DNA damage, contributing to DNA double-strand break repair [40,41]. Moreover, a recent work showed that EZH2 expression predicts outcome in patients with BRCA2-mutant ovarian tumors by regulating genomic stability at stalled replication forks [42], suggesting a possible connection between EZH2 and BRCA2. Finally, the Myc oncogene has a key role in cancer initiation and progression in several cancer types, including PC [43,44]. The regulation of c-Myc may be an ideal effective therapeutic target in this disease [45].
In this study, we used a priori approach to select a very specific set of genes involved in the processes detailed above. The advantage of this approach over a more traditional search for cluster of highly related genes [46,47] is related to the ability to look for connections with the behavioral output associated with the disease onset and progression. Therefore, the main aim of this study was to develop an innovative model based on the expression of regulatory genes associated with the stress and immune responses influencing cell proliferation. We used public data from The Cancer Genome Atlas to create a map of association of key regulatory genes involved in proliferation activity, such as EZH2, MCM7, BRCA2, and c-MYC, in stressful pathway (BDNF and cortisol), and in inflammation microenvironment (CTLA4 and PD-L1) to test several multi-dimensional scaling models and create a combined gene-cluster to better predict PC. The main weakness of this approach is that we inevitably cannot select all possible combinations of the many genes and cluster of genes that are most certainly involved in this complex disease and its progression.

2. Materials and Methods

2.1. TCGA Database

This study used data from the public domain and did not require the approval from an ethics committee. The gene expression profiles of patients with prostate cancer were obtained from the TCGA data portal (https://tcga-data.nci.nih.gov/tcga/, accessed on 14 January 2021). Clinical characteristics such as age, latest values of hematic PSA, disease-free interval, survival time, and cancer outcome were also obtained from TCGA data portal. IlluminaHiSeq expression data for 243 patients affects by PC were downloaded and processed.
The Gleason score for each patient was classified in five groups based on their primary and secondary GS (see Table 1). The age at the diagnosis was given in years, whereas time of survival and disease-free interval were in months. Last PSA values were given in ng/mL and ranged from 0 to 323 with a mean of 2.45 ± 22.27 SD.

2.2. Statistical Analysis

General Liner Models (GLM) with Tukey’s test as a post hoc test were used for multiple comparisons prior of entering the data into the multivariate models. Pearson’s r was used to calculate the bivariate correlations among clinical output and target gene expression. Survival analyses were performed using the Kaplan–Meier method with the log-rank test for statistical significance. All analyses were considered significant at the α-level = 0.05.
To identify the independent association of all variables (both clinical output and gene expression) a Multi-Dimensional Scaling analysis was run. MDS is a data reduction technique used to reveal the similarities among variables and individual cases in a set of data. Distances between variables were derived looking at partial correlations (i.e., proximities) among variables, which were subsequently used to create a matrix of distance could be displayed graphically. The closer two or more variables are on the map, the more highly correlated they are, while the farther apart they are, the less correlated they are. To arrange the variable into a map sensitive to each individual contribution, a limited lack of fit between the data and the model is inevitable. This lack of fit is known as the s-stress. The values of s-stress range from 0 (perfect fit) to 1 (worst possible fit). Thus, the aim of MDS is to find a map of the variables that minimizes the s-stress. The number of dimensions in a map is linked to the number of latent underlying factors in the dataset, similarly to other procedures like factor analysis. Therefore, the optimal number of dimensions to represent the data is dependent on several factors: the number of variables in the model; the lack of fit (s-stress value), given the number of dimensions; an index of fit of the model (r2-value); and interpretability of the dimensions. Typically, r2-values of 0.8 or higher are considered acceptable.
Dimensions revealed by the MDS were entered in a multivariate regression (MR) model to determine if clinical output could be predicted by a linear combination of the target gene expression.
All analyses were performed using SPSS 27.0 (IBM, Armonk, NY, USA).

3. Results

3.1. TCGA Database

Patients affected by PC (n = 243) were extracted from the TCGA database. The average age was 61.36 ± 6.77 SD years, reporting a disease-free interval (DFI) of 34.47 ± 25.90 SD months and overall survival (OS) of 38.12 ± 26.57 SD months. The age of diagnosis was not related to neither disease-free interval (p = 0.11) and survival (p = 0.11), whereas survival was significantly related to the disease-free interval (p = 0.001).
Table 2 showed that neither survival (F4,238 = 0.248; p = 0.91) nor disease-free interval (F4,235 = 1.79; p = 0.13) were related to the Gleason score. PSA values were negatively correlated related with the DFI (r = −0.18, p = 0.007), but it was not related to OS (p = 0.09). PSA values was not significantly related to the GS (F4,211 = 1.30; p = 0.27).

3.2. Target Genes

Pearson’s correlations between PSA, disease-free interval, and survival and several target genes are shown in Table 3. Survival was positively correlated with cortisol expression (p = 0.048) and negatively with MCM7 (p = 0.040) and BRCA2 (p = 0.039). The disease-free interval was negatively related to the expression of EZH2 (p = 0.035), MCM7 (p = 0.017) and BRCA2 (p = 0.049), and PD-L1 (p = 0.039). PSA values were positively related to c-Myc (p < 0.001), EZH2 (p < 0.001), and MCM7 (p = 0.048).
The expression of EZH2 (F4,238 = 6.88; p < 0.001), MCM7 (F4,238 =4.99; p < 0.001), and BRCA2 (F4,238 =3.76; p = 0.028) varied significantly by the GS (Figure 1), whereas the other genes did not—although the expression of BDNF was close to the significance level (p = 0.057).

3.3. MDS Analysis

MDS analysis was run to identify the independent association among the target genes and the survival data. S-stress (0.12) and r2 (0.83) were acceptable. The resulting map clearly identified two dimensions, named Survivability and Proliferation/Inflammation axis (Figure 2). The first dimension indicated that higher expression of cortisol and BDNF were related to a higher probability to stay free form the disease and, hence, survive; on the other side of the map, higher expressions of BRCA2 and MCM7 were related to higher PSA values and, thus, a higher probability of the reappearance of the prostate cancer. The second dimension showed a separation of cluster of proliferation genes (EZH2, c-Myc, BRCA2, and MCM7) on one side and inflammation genes (PD-L1/CTL4) on the opposite side. This could indicate two separate pathways of activation in PC. Within these two dimensions, three separate clusters emerged: in the top-left quadrant an association between cortisol and BDNF expression was revealed; in the top-right quadrant the association between PD-L1 and CTL4 emerged; finally, in the bottom-right quadrant the target genes EZH2, c-Myc, BRCA2, and MCM7 clustered together.

3.4. Stepwise Multiple Regression

To identify the best predictors of the patient survival, several stepwise multiple regression (SMR) models were run. Firstly, we ran a SMR using only the original values of gene expression. The best model retained the following gene expression: BRCA2, cortisol, and BDNF (Table 4a). Although it was very significant (F3,239 = 4.57; p = 0.004), the predictive value of this model was quite poor (r2 = 0.05) thus confirming the complexity of the phenomenon and that no single gene can explain the variability in survival data among patients of PC.
Then, we used the composite indices derived from the MDS. All variables were standardized to take into account the difference in the scale of expression, and each cluster was calculated using the sum of all variables included, so Cluster 1 = BDNF + Cortisol, Cluster 2 = PD-L1 + CTL4, and Cluster 3 = EZH2 + BRCA2 + cMyc + MCM7. The best SMR model retained only the last cluster, the composite index derived by Cluster 3 (F1,241 = 6.103; p = 0.014 in Table 4b). This model had also a much better predictive value than the original one, being able to explain almost 16% of the variance (r2 = 0.157).

3.5. Survival Curve

We entered the three clusters of association shown in the MDS in several Kaplan–Mayer analyses, using the disease-free interval as the time variable. It was found that only Cluster 3 was significantly related to the interval-disease free (Mantel-Cox = 3.69, p = 0.05) indicating that patients with an overall higher activity of these 3 key genes (EZH2, BRCA2, c-Myc, and MCM7) has a lower probability to have a longer disease-free time (Estimate High: 38.32, 95% CI 33.79 to 42. 85; versus Low: 30.55, 95% CI 25.9 to 35.11 in Figure 3). None of the original gene expression was significantly related to the disease-free interval by its own (all p-values > 0.10).

4. Discussion

The main aim of this study was to develop a priori model based on the expression of regulatory genes involved in proliferation activity, TME inflammation, and live events related to stress. We found that patients with concurred increased mRNA expression of genes involved in proliferation and DNA repair pathways, such as EZH2, MCM7, BRCA2, and c-MYC, showed to have a more aggressive PC phenotype. We included these genes in our model since several studies based on weighted gene co-expression found that patient outcome and disease progression were related to genes involved in proliferation and DHAN repair [14,19]. While each of these genes has been independently studied in PC progression [37,39], they have never been analyzed together as a combined factor for prognostic use. Our study provided novel evidence that selecting patients with a high grade of proliferation and DNA repair activity could lead to an early identification of an aggressive disease with a potential for metastatic development. Our data specifically linked this cluster of genes with a better prospective for longer disease-free intervals, but not with the overall survival. Although this result seems difficult to explain, since progression-free survival or OS is often used as the endpoint in PC due to its clinical relevance [48,49,50], it is important to remember that in some oncologic disease characterized by a slow growth rate, such as PC, an improvement in DFI appears to be more relevant in the effort of the early identification of aggressive forms of PC. In our study we also merged higher GS, whereas some authors recommend an even higher level of differentiation among the different groups [51], which could influence the overall conclusions.
Several studies have clearly indicated that c-Myc is overexpressed at early stages of PC and acts as a key driver of tumorigenesis and disease progression [43]. c-Myc overexpression is observed in up to 37% of metastatic PC patients [52] and significantly associated with poor survival [53]. Although the role of c-Myc in PC has been intensely studied, little is known concerning the impact of c-Myc overexpression in combination with other key regulators in PC progression. Dardenne and colleagues [54] suggested an interaction between c-Myc and EZH2; they observed high levels of EZH2 activity in mouse models over-expressing N-Myc and in human PC cells, concluding that the histone methyltransferase EZH2 seems to cooperate with Myc in regulating its target genes in neuroendocrine prostate cancer. More recently, Neves Filho and colleagues [55] demonstrated that EZH2 was up-regulated by Myc and associated with high proliferation tumors in diffuse large-B-cell lymphoma. This a priori knowledge was used to include the above genes in our model. The results confirmed that a cluster of genes including c-Myc, EZH2, MCM7, and BRCA2 could be part of the same regulatory pathway in PC progression. More specifically, Myc-mediated stimulation of cell cycle can occur using several parallel mechanisms, also liked to key biological functions such as DNA replication and repair [56].
MCM7 is one of the heterohexamer MCM helicase complex recruited in initiation of DNA replication. Induced overexpression of MCM7 is involved in tumor formation and progression in a variety of human malignancies [37,57,58,59,60,61], including PC [38], and its down-regulation results in growth inhibition [62], indicating that MCM7 is particularly important as a potential biomarker. Here we confirmed that MCM7 could be a survival predictor for prostate cancer, considering that a shorter disease-free interval was associated with higher MCM7 and other key proliferation factors expression.
The gene BRCA2 is involved, together with BRCA1, in DNA repair, cell cycle checkpoint regulation and transcription [63]. Several studies have identified DNA repair gene signatures as effective predictors of PC progression and outcome [47,64] It is currently understood that the normal protein product of BRCA2 gene is important in double-strand DNA repair by maintaining genomic integrity, and that once this gene is mutated or altered, DNA damage may not be repaired properly, likely leading to the occurrence of cancer. How DNA repair is affected by homeostatic processes involving life habits and stress responses is not sufficiently understood. It is known that cells undergo a constant burden of damage that can be increased dramatically by environmental agents and social events, generating many of the same DNA lesions that are a hallmark of cancer progression [65]. Our results appear to indicate that life-base and cellular-base DNA damages contribute to PC cancer following two separate pathways. Accumulated evidence has demonstrated that the expression level of BRCA2 is altered in breast and ovarian cancer, offering a potentially important tool for use in cancer management [66]. BRCA2 mutations are well recognized in PC [67,68], associated with a poor prognosis [69,70,71], and with a high response rate to PARP inhibitors in patients not responding to standard treatments [72]. However, no data exist regarding BRCA2 expression level, and its crosstalk within a network of proteins in PC.
Intriguingly, BRCA2 and other DNA repair genes seem to be influenced by circadian modulation [73], and diet habits [74], probably in connection with diurnal hormonal modulation. Since PC is also a hormone-dependent tumor, nutrients potentially affect tumor pathogenesis and progression [75,76] through various mechanisms including inflammation, cortisol-mediated stress effects, and the action of sex hormones [77]. Recently, Li and colleagues [27] investigated the role of BDNF in prostate cancer, demonstrating that the BDNF pathway is crucial for disease progression. Our integrated data confirmed that a higher expression of BDNF and cortisol were related to a greater probability of survival. Chronic stress is known to promote tumor progression in several cancer models [78], and a growing body of evidence suggests that the stress response machinery is an important mediator during tumorigenesis and metastasis [79,80,81,82,83,84]. Steroid hormones are critical factors in mediating the stress-cancer relationship; stress-related hormones have been shown to be involved in accelerating cell proliferation and tumor growth in PC [85]. However, our understanding of the mechanisms through which stress contributes to cancer development and progression is incomplete. Our data of elevated cortisol mRNA levels confirmed a previous study reporting increased circulating levels of cortisol in PC patients [26], suggesting a possible relationship between stress and cancer.
Cancer is a multistep process that requires cells to acquire specific characteristics in order to evolve into a malignant phenotype; it is unlikely that stress alone can provide cells with such traits, however, stress can influence a wide variety of cellular functions, facilitating deregulation of pro- and anti-proliferative cellular processes. Without doubts, the regulation of mRNA expression by stress signals (both internal and external) represents an emerging and promising field of study in PC and it needs further analysis. Another route of action, which has been extensively studied in recent years, is focused on immune function and its well-established relationship with stress [86,87]. Reprogramming innate immune cells is one of the main ways by which the tumor controls the surrounding microenvironment in order to promote its growth [88]. A complex series of interactions between immune cell types and non-tumor cells within the TME heavily impact tumor progression, invasion, and metastasis. A chronic inflammatory prostate microenvironment seems to drive prostate carcinogenesis and progression [89]. A growing number of trials are ongoing with the immune checkpoint antibodies in prostate cancer. PC grows slowly compared to other types of malignancies, which allows it to be an ideal candidate for immunotherapy. However, various clinical trials by immunotherapy (anti-PD1 and anti-CTLA4) have only shown modest clinical outcomes in comparison to other cancers. Particularly, TME in prostate lesions may be unsuitable for tumor infiltrating immune cells with anti-tumor activities, leading to limited efficacy of immunotherapy [90]. Bishop et al. [91] showed variations in PD-L1 expression in prostate tumors, suggesting that the levels of immune checkpoint molecule expression vary in different stages of PC progression and our data of PD-L1 and CTLA4 mRNA expression confirmed this mechanism. These variations in immune checkpoint molecules expression in prostate tumors need more studies in order to assess their putative clinical use as potential indicator of checkpoint immunotherapy and tumors progression.
Until recently, Gleason score and serum PSA levels represented the most important predictive factors during PC management [92]. The recommended grade grouping based on GS is routinely adopted. However, issues remain regarding quantification of high-grade patterns and the mostly indolent behavior of these tumors. Moreover, the PSA level is not able to discriminate aggressive or advanced prostate cancer, particularly at PSA levels below 20 ng/mL [93,94], and it has a high false-positive rate. Considering that the currently available methods cannot provide accurate parameters for the prediction of aggressive potential in prostate cancer, tissue-based biomarkers could be used as prognostic markers [95], and hence the need of a more sophisticated level of analysis as exemplified in our study.
Although the results in the current study are promising and confirm the necessity of integrated genetic markers to enhance early detection of aggressive forms of PC, it would be important to validate our conclusions in experimental settings that go beyond the scope of using the TCGA database. For starter, multiple pathways of a priori selected genes should be tested to find the best combination as a predictive tool for PC. Although the gene selected in this work are clearly implicated in the development of PC, many other key regulatory genes could be involved in the functional pathway of PC, alternative models would be certainly improved by including different alternative pathways.

5. Conclusions

The novelty of our approach warrants further investigation on the role of proliferation and DNA repair genes in the role of PC, and demonstrates that the role of the TME and life events, such as stress, can be key factors in detecting PC progression with a high clinical impact.

Author Contributions

Conceptualization, L.B. and M.B.; Methodology, M.B.; Software, M.B.; Validation, L.B., M.B. and P.F.; Formal Analysis, L.G., F.P. and P.A.E.; Investigation, L.B., P.F., L.G., F.P., P.A.E. and M.B.; Data Curation, M.B.; Writing—Original Draft Preparation, L.B. and M.B.; Writing—Review and Editing, L.B., P.F., L.G., F.P., P.A.E. and M.B.; Supervision, L.B. and M.B. All authors have read and agreed to the published version of the manuscript.

Funding

No specific funding has been received.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Gronberg, H. Prostate cancer epidemiology. Lancet 2003, 361, 859–864. [Google Scholar] [CrossRef]
  2. Bray, F.; Ferlay, J.; Soerjomataram, I.; Siegel, R.L.; Torre, L.A.; Jemal, A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J. Clin. 2018, 68, 394–424. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  3. Quinn, M.; Babb, P. Patterns and trends in prostate cancer incidence, survival, prevalence and mortality. Part I: International comparisons. BJU 2002, 90, 162–173. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  4. Ferlay, J.; Ervik, M.; Lam, F.; Colombet, M.; Mery, L.; Piñeros, M.; Znaor, A.; Soerjomataram, I. Global Cancer Observatory: Cancer Today; International Agency for Research on Cancer: Lyon, France, 2021; Available online: https://gco.iarc.fr/today (accessed on 14 January 2021).
  5. Draisma, G.; Etzioni, R.; Tsodikov, A.; Mariotto, A.; Wever, E.; Gulati, R.; Feuer, E.; De Koning, H. Lead time and overdiagnosis in prostate-specific antigen screening: Importance of methods and context. J. Natl. Cancer Inst. 2009, 101, 374–383. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  6. Grossman, D.C.; Curry, S.J.; Owens, D.K.; Bibbins-Domingo, K.; Caughey, A.B.; Davidson, K.W.; Doubeni, C.A.; Ebell, M.; Epling, J.W., Jr.; Kemper, A.R.; et al. Screening for Prostate Cancer: US Preventive Services Task Force Recommendation Statement. US Preventive Services Task Force. JAMA 2018, 319, 1901–1913. [Google Scholar] [CrossRef]
  7. D’Amico, A.V.; Whittington, R.; Malkowicz, S.B.; Fondurulia, J.; Chen, M.-H.; Kaplan, I.; Beard, C.J.; Tomaszewski, J.E.; Renshaw, A.A.; Wein, A.; et al. Pretreatment nomogram for prostate-specific antigen recurrence after radical prostatectomy or external-beam radiation therapy for clinically localized prostate cancer. J. Clin. Oncol. 1999, 17, 168–172. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  8. Thompson, I.M.; Ankerst, D.; Chi, C.; Goodman, P.J.; Tangen, C.M.; Lucia, M.S.; Feng, Z.; Parnes, H.L.; Coltman, C.A. Assessing prostate cancer risk: Results from the prostate cancer prevention trial. J. Natl. Cancer Inst. 2006, 98, 529–534. [Google Scholar] [CrossRef] [Green Version]
  9. Egevad, L.; Granfors, T.; Karlberg, L.; Bergh, A.; Stattin, P. Prognostic value of the Gleason score in prostate cancer. BJU Int. 2002, 89, 538–542. [Google Scholar] [CrossRef]
  10. Rusthoven, C.G.; Carlson, J.A.; Waxweiler, T.V.; Yeh, N.; Raben, D.; Flaig, T.W.; Kavanagh, B.D. The prognostic significance of Gleason scores in metastatic prostate cancer. Urol. Oncol. 2014, 32, 707–713. [Google Scholar] [CrossRef]
  11. Brimo, F.; Montironi, R.; Egevad, L.; Erbersdobler, A.; Lin, D.W.; Nelson, J.B.; Rubin, M.A.; Van Der Kwast, T.; Amin, M.; Epstein, J.I.; et al. Contemporary grading for prostate cancer: Implications for patient care. Eur. Urol. 2013, 63, 892–901. [Google Scholar] [CrossRef]
  12. Sehn, J.K. Prostate Cancer Pathology: Recent Updates and Controversies. Mol. Med. 2018, 115, 151–155. [Google Scholar]
  13. McKenney, J.K.; Wei, W.; Hawley, S.; Auman, H.; Newcomb, L.F.; Boyer, H.D.; Fazli, L.; Simko, J.; Hurtado-Coll, A.; Troyer, D.A.; et al. Histologic Grading of Prostatic Adenocarcinoma Can Be Further Optimized: Analysis of the Relative Prognostic Strength of Individual Architectural Patterns in 1275 Patients From the Canary Retrospective Cohort. Am. J. Surg. Pathol. 2016, 40, 1439–1456. [Google Scholar] [CrossRef]
  14. Zhou, Z.; Cheng, Y.; Jiang, Y.; Liu, S.; Zhang, M.; Liu, J.; Zhao, Q. Ten hub genes associated with progression and prognosis of pancreatic carcinoma identified by co-expression analysis. Int. J. Biol. Sci. 2018, 14, 124–136. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  15. Abate-Shen, C.; Shen, M.M. Molecular genetics of prostate cancer. Genes Dev. 2000, 14, 2410–2434. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  16. Ross-Adams, H.; Lamb, A.D.; Dunning, M.J.; Halim, S.; Lindberg, J.; Massie, C.M.; Egevad, L.A.; Russell, R.; Ramos-Montoya, A.; Vowler, S.L.; et al. Integration of copy number and transcriptomics provides risk stratification in prostate cancer: A discovery and validation cohort study. EbioMedicine 2015, 2, 1133–1144. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  17. Taylor, B.S.; Schultz, N.; Hieronymus, H.; Gopalan, A.; Xiao, Y.; Carver, B.S.; Arora, V.K.; Kaushik, P.; Cerami, E.; Reva, B.; et al. Integrative Genomic Profiling of Human Prostate Cancer. Cancer Cell 2010, 18, 11–22. [Google Scholar] [CrossRef] [Green Version]
  18. Seibert, T.M.; Fan, C.C.; Wang, Y.; Zuber, V.; Karunamuni, R.; Parsons, J.K.; Eeles, R.A.; Easton, D.F.; Kote-Jarai, Z.; Al Olama, A.A.; et al. PRACTICAL Consortium. Polygenic hazard score to guide screening for aggressive prostate cancer: Development and validation in large scale cohorts. BMJ 2018, 360, j5757. [Google Scholar] [CrossRef] [Green Version]
  19. Wang, Y.; Yang, Z. A Gleason score-related outcome model for human prostate cancer: A comprehensive study based on weighted gene co-expression network analysis. Cancer Cell Int. 2020, 20, 159. [Google Scholar] [CrossRef]
  20. Abeshouse, A.; Ahn, J.; Akbani, R.; Ally, A.; Amin, S.; Andry, C.D.; Annala, M.; Aprikian, A.; Armenia, J.; Arora, A.; et al. The Molecular Taxonomy of Primary Prostate Cancer. Cell 2015, 163, 1011–1025. [Google Scholar] [CrossRef] [Green Version]
  21. Luca, B.-A.; Moulton, V.; Ellis, C.; Edwards, D.R.; Campbell, C.; Cooper, R.A.; Clark, J.; Brewer, D.S.; Cooper, C.S. A novel stratification framework for predicting outcome in patients with prostate cancer. Br. J. Cancer 2020, 122, 1467–1476. [Google Scholar] [CrossRef] [Green Version]
  22. Moghadas-Dastjerdi, H.; Sha-E-Tallat, H.R.; Sannachi, L.; Sadeghi-Naini, A.; Czarnota, G.J. A priori prediction of tumour response to neoadjuvant chemotherapy in breast cancer patients using quantitative CT and machine learning. Sci. Rep. 2020, 10, 10936. [Google Scholar] [CrossRef]
  23. Yang, E.V.; Glaser, R. Stress-induced immunomodulation: Implications for tumorigenesis. Brain Behav. Immun. 2003, 17, 37–40. [Google Scholar] [CrossRef]
  24. Bloch, S.; Love, A.; Macvean, M.; Duchesne, G.; Couper, J.; Kissane, D. Psychological adjustment of men with prostate cancer: A review of the literature. Biopsychosoc. Med. 2007, 1, 2. [Google Scholar] [CrossRef] [Green Version]
  25. Gidron, Y.; Fabre, B.; Grosman, H.; Nolazco, C.; Mesch, V.; Mazza, O.; Berg, G. Life events, cortisol and levels of prostate specific antigen: A story of synergism. Psychoneuroendocrinology 2011, 36, 874–880. [Google Scholar] [CrossRef] [PubMed]
  26. Fabre, B.; Grosman, H.; Gonzalez, D.; Machulsky, N.F.; Repetto, E.M.; Mesch, V.; Lopez, M.A.; Mazza, O.; Berg, G. Prostate Cancer, High Cortisol Levels and Complex Hormonal Interaction. Asian Pac. J. Cancer Prev. 2016, 17, 3167–3171. [Google Scholar] [PubMed]
  27. Li, T.; Yu, Y.; Song, Y.; Li, X.; Lan, D.; Zhang, P.; Xiao, Y.; Xing, Y. Activation of BDNF/TrkB pathway promotes prostate cancer progression via induction of epithelial-mesenchymal transition and anoikis resistance. FASEB J. 2020, 34, 9087–9101. [Google Scholar] [CrossRef]
  28. Barron, D.A.; Rowley, D.R. The reactive stroma microenvironment and prostate cancer progression. Endocr. Relat. Cancer 2021, 19, R187–R204. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  29. Perletti, G.; Monti, E.; Magri, V.; Cai, T.; Cleves, A.; Trinchieri, A.; Montanari, E. The association between prostatitis and prostate cancer. Systematic review and meta-analysis. Arch. Ital. Urol. Androl. 2017, 89, 259–265. [Google Scholar] [CrossRef] [Green Version]
  30. Cai, T.; Santi, R.; Tamanini, I.; Galli, I.C.; Perletti, G.; Johansen, T.E.B.; Nesi, G. Current knowledge of the potential links between inflammation and prostate cancer. Int. J. Mol. 2019, 20, 3833. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  31. Karakiewicz, P.I.; Benayoun, S.; Begin, L.R.; Duclos, A.; Valiquette, L.; McCormack, M.; Bénard, F.; Saad, F.; Perrotte, P. Chronic inflammation is negatively associated with prostate cancer and high-grade prostatic intraepithelial neoplasia on needle biopsy. Int. J. Clin. Pract. 2007, 61, 425–430. [Google Scholar] [CrossRef]
  32. Vitkin, N.; Nersesian, S.; Siemens, D.R.; Koti, M. The tumor immune contexture of prostate cancer. Front. Immunol. 2019, 10, 603. [Google Scholar] [CrossRef] [Green Version]
  33. Varambally, S.; Dhanasekaran, S.; Zhou, M.; Barrette, T.R.; Kumar-Sinha, C.; Sanda, M.G.; Ghosh, D.; Pienta, K.J.; Sewalt, R.G.; Otte, A.P.; et al. The polycomb group protein EZH2 is involved in progression of prostate cancer. Nature 2002, 419, 624–629. [Google Scholar] [CrossRef]
  34. Rhodes, D.R.; Sandra, M.G.; Otte, A.P.; Chinnaiyan, A.M.; Rubin, M.A. Multiplex biomarker approach for determining risk of prostate-specific-defined recurrence of prostate cancer. J. Natl. Cancer Inst. 2003, 95, 661–668. [Google Scholar] [CrossRef] [Green Version]
  35. Nowinska, K.; Chmielewska, M.; Piotrowska, A.; Pula, B.; Pastuszewski, W.; Krecicki, T.; Podhorska-Okołow, M.; Zabel, M.; Dziegiel, P. Correlation between levels of expression of minichromosome maintenance proteins, Ki-67 proliferation antigen and metallothionein I/II in laryngeal squamous cell cancer. Int. J. Oncol. 2016, 48, 635–645. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  36. Zhong, X.; Chen, X.; Guan, X.; Zhang, H.; Ma, Y.; Zhang, S.; Wang, E.; Zhang, L.; Han, Y. Overexpression of G9a and MCM7 in oesophageal squamous cell carcinoma is associated with poor prognosis. Histopathology 2015, 66, 192–200. [Google Scholar] [CrossRef] [PubMed]
  37. Zhou, Y.-M.; Zhang, X.-F.; Cao, L.; Li, B.; Sui, C.-J.; Li, Y.-M.; Yin, Z. MCM7 expression predicts post-operative prognosis for hepatocellular carcinoma. Liver Int. 2012, 32, 1505–1509. [Google Scholar] [CrossRef] [PubMed]
  38. Ren, B.; Yu, G.; Tseng, G.C.; Cieply, K.; Gavel, T.; Nelson, J.B.; Michalopoulos, G.K.; Yu, Y.P.; Luo, J.-H. MCM7 amplification and overexpression are associated with prostate cancer progression. Oncogene 2006, 25, 1090–1098. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  39. Laitinen, S.; Martikainen, P.M.; Tolonen, T.; Isola, J.; Tammela, T.L.; Visakorpi, T. EZH2, Ki-67 and MCM7 are prognostic markers in prostatectomy treated patients. Int. J. Cancer 2008, 122, 595–602. [Google Scholar] [CrossRef]
  40. Wu, Z.; Lee, S.T.; Qiao, Y.; Li, Z.; Lee, P.L.; Lee, Y.J.; Jiang, X.; Tan, J.; Aau, M.; Lim, C.Z.H.; et al. Polycomb protein EZH2 regulates cancer cell fate decision in response to DNA damage. Cell Death Differ. 2011, 18, 1771–1779. [Google Scholar] [CrossRef]
  41. Campbell, S.; Ismail, I.H.; Young, L.C.; Poirier, G.G.; Hendzel, M.J. Polycomb repressive complex 2 contributes to DNA double-strand break repair. Cell Cycle 2013, 12, 2675–2683. [Google Scholar] [CrossRef]
  42. Rondinelli, B.; Gogola, E.; Yücel, H.; Duarte, A.A.; van de Ven, M.; van der Sluijs, R.; Konstantinopoulos, P.A.; Jonkers, J.; Ceccaldi, R.; Rottenberg, S.; et al. EZH2 promotes degradation of stalled replication forks by recruiting MUS81 through histone H3 trimethylation. Nat. Cell Biol. 2017, 19, 1371–1378. [Google Scholar] [CrossRef]
  43. Gurel, B.; Iwata, T.; Koh, C.M.; Jenkins, R.B.; Lan, F.; Dang, C.; Hicks, J.L.; Morgan, J.; Cornish, T.; Sutcliffe, S.; et al. Nuclear MYC protein overexpression is an early alteration in human prostate carcinogenesis. Mod. Pathol. 2008, 21, 1156–1167. [Google Scholar] [CrossRef]
  44. Koh, C.M.; Bieberich, C.; Dang, C.; Nelson, W.G.; Yegnasubramanian, S.; De Marzo, A.M. MYC and prostate cancer. Genes Cancer 2012, 1, 617–628. [Google Scholar] [CrossRef] [Green Version]
  45. Dang, C.V. MYC on the path to cancer. Cell 2012, 149, 22–35. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  46. Gerhauser, C.; Favero, F.; Risch, T.; Simon, R.; Feuerbach, L.; Assenov, Y.; Heckmann, D.; Sidiropoulos, N.; Waszak, S.M.; Hübschmann, D.; et al. Molecular Evolution of Early-Onset Prostate Cancer Identifies Molecular Risk Markers and Clinical Trajectories. Cancer Cell 2018, 34, 996.e8–1011.e8. [Google Scholar] [CrossRef] [Green Version]
  47. Long, G.; Ouyang, W.; Zhang, Y.; Sun, G.; Gan, J.; Hu, Z.; Li, H. Identification of a DNA Repair Gene Signature and Establishment of a Prognostic Nomogram Predicting Biochemical-Recurrence-Free Survival of Prostate Cancer. Front. Mol. Biosci. 2021, 8, 608369. [Google Scholar] [CrossRef] [PubMed]
  48. Mayfield, E. Progression-Free Survival: Patient Benefit or Lower Standard; Life Raft Group: Wayne, NJ, USA, 2007; Available online: https://liferaftgroup.org/2008/08/progression-free-survival-patient-benefit-or-lower-standard/ (accessed on 23 March 2009).
  49. Chakravarty, A.; Sridhara, R. Use of progression-free survival as a surrogate marker in oncology trials: Some regulatory issues. Stat. Methods Med. Res. 2008, 17, 515–518. [Google Scholar] [CrossRef] [PubMed]
  50. Panageas, K.S.; Ben-Porat, L.; Dickler, M.N.; Chapman, P.B.; Schrag, D. When you look matters: The effect of assessment schedule on progression-free survival. J. Natl. Cancer Inst. 2007, 99, 428–432. [Google Scholar] [CrossRef] [PubMed]
  51. Zhou, Y.; Lin, C.; Hu, Z.; Yang, C.; Zhang, R.; Ding, Y.; Wang, Z.; Tao, S.; Qin, Y. Differences in survival of prostate cancer Gleason 8–10 disease and the establishment of a new Gleason survival grading system. Cancer Med. 2021, 10, 87–97. [Google Scholar] [CrossRef] [PubMed]
  52. Kumar, A.; Coleman, I.; Morrissey, C.; Zhang, X.; True, L.D.; Gulati, R.; Nelson, P.S. Substantial interindividual and limited intraindividual genomic diversity among tumors from men with metastatic prostate cancer. Nat. Med. 2016, 22, 369–378. [Google Scholar] [CrossRef]
  53. Ribeiro, F.R.; Jerónimo, C.; Henrique, R.; Fonseca, D.; Oliveira, J.; Lothe, R.A.; Teixeira, M.R. 8q gain is an independent predictor of poor survival in diagnostic needle biopsies from prostate cancer suspects. Clin. Cancer Res. 2006, 12, 3961–3970. [Google Scholar] [CrossRef] [Green Version]
  54. Dardenne, E.; Beltran, H.; Benelli, M.; Gayvert, K.; Berger, A.; Puca, L.; Cyrta, J.; Sboner, A.; Noorzad, Z.; Macdonald, T.; et al. N-Myc Induces an EZH2-Mediated Transcriptional Program Driving Neuroendocrine Prostate Cancer. Cancer Cell. 2016, 30, 563–577. [Google Scholar] [CrossRef] [Green Version]
  55. Neves Filho, E.H.; Hirth, C.G.; Frederico, I.A.; Burbano, R.M.; Carneiro, T.; Rabenhorst, S.H. EZH2 expression is dependent on MYC and TP53 regulation in diffuse large B-cell lymphoma. APMIS 2020, 128, 308–315. [Google Scholar] [CrossRef]
  56. Bretones, G.; Delgado, M.D.; León, J. Myc and cell cycle control. Biochem. Biophys. Acta 2015, 1849, 506–516. [Google Scholar] [CrossRef] [PubMed]
  57. Toyokawa, G.; Masuda, K.; Daigo, Y.; Cho, H.-S.; Yoshimatsu, M.; Takawa, M.; Hayami, S.; Maejima, K.; Chino, M.; I Field, H.; et al. Minichromosome Maintenance Protein 7 is a potential therapeutic target in human cancer and a novel prognostic marker of non-small cell lung cancer. Mol. Cancer 2011, 10, 65. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  58. Facoetti, A.; Ranza, E.; Grecchi, I.; Benericetti, E.; Ceroni, M.; Morbini, P.; Nano, R. Immunohistochemical evaluation of minichromosome maintenance protein 7 in astrocytoma grading. Anticancer Res. 2006, 26, 3513–3516. [Google Scholar] [PubMed]
  59. Marnerides, A.; Vassilakopoulos, T.P.; Boltetsou, E.; Levidou, G.; Angelopoulou, M.K.; Thymara, I.; Kyrtsonis, M.-C.; Pappi, V.; Tsopra, O.; Panayiotidis, P.; et al. Immunohistochemical expression and prognostic significance of CCND3, MCM2 and MCM7 in Hodgkin lymhoma. Anticancer Res. 2011, 31, 3585–3594. [Google Scholar] [PubMed]
  60. Shomori, K.; Nishihara, K.; Fujioka, S.; Tokuyasu, N.; Inaba, A.; Osaki, M.; Ogawa, T. Minichromosome maintenance protein 7 in colorectal cancer: Implication of prognostic significance. Int. J. Oncol. 2008, 33, 245–251. [Google Scholar] [CrossRef] [Green Version]
  61. Ota, T.; Clayton, A.C.; Minot, D.M.; Shridhar, V.; Hartmann, L.C.; Gilks, C.B.; Chien, J.R. Minichromosome maintenance protein 7 as a potential prognostic factor for progression-free survival in high-grade serous carcinomas of the ovary. Mod. Pathol. 2011, 24, 277–287. [Google Scholar] [CrossRef] [Green Version]
  62. Qu, K.; Wang, Z.; Fan, H.; Li, J.; Liu, J.; Li, P.; Liang, Z.; An, H.; Jiang, Y.; Lin, Q.; et al. MCM7 promotes cancer progression through cyclin D1-dependent signaling and serves as a prognostic marker for patients with hepatocellular carcinoma. Cell Death Dis. 2017, 8, e2603, reprinted in Cell Death Dis. 2018, 9, 681. [Google Scholar] [CrossRef]
  63. Welcsh, P.L.; King, M.C. BRCA1 and BRCA2 and the genetics of breast and ovarian cancer. Hum. Mol. Genet. 2001, 10, 705–713. [Google Scholar] [CrossRef]
  64. Ma, J.; Setton, J.; Lee, N.Y.; Riaz, N.; Powell, S.N. The therapeutic significance of mutational signatures from DNA repair deficiency in cancer. Nat. Commun. 2018, 9, 3292. [Google Scholar] [CrossRef]
  65. Demple, B. Special problems for Base Excision Repair in coping with oxidatively-induced DNA damage. In DNA Damage, DNA Repair and Disease; The Royal Society of Chemistry: London, UK, 2020; pp. 204–219. [Google Scholar]
  66. Wang, Z.; Zhang, J.; Zhang, Y.; Deng, Q.; Liang, H. Expression and mutations of BRCA in breast cancer and ovarian cancer: Evidence from bioinformatics analyses. Int. J. Mol. Med. 2018, 42, 3542–3550. [Google Scholar] [CrossRef] [Green Version]
  67. Grasso, C.S.; Wu, Y.-M.; Robinson, D.R.; Cao, X.; Dhanasekaran, S.M.; Khan, A.P.; Quist, M.J.; Jing, X.; Lonigro, R.J.; Brenner, J.C.; et al. The mutational landscape of lethal castration-resistant prostate cancer. Nature 2012, 487, 239–243. [Google Scholar] [CrossRef] [Green Version]
  68. Beltran, H.; Yelensky, R.; Frampton, G.M.; Park, K.; Downing, S.R.; MacDonald, T.Y.; Rubin, M.A. Targeted next-generation sequencing of advanced prostate cancer identifies potential therapeutic targets and disease heterogeneity. Eur. Urol. 2013, 63, 920–926. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  69. Fong, P.C.; Boss, D.S.; Yap, T.A.; Tutt, A.; Wu, P.; Mergui-Roelvink, M. Inhibition of poly(ADP-ribose) polymerase in tumors from BRCA mutation carriers. N. Engl. J. Med. 2009, 361, 123–134. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  70. Fong, P.C.; Boss, D.S.; Yap, T.A.; Tutt, A.; Wu, P.; Mergui-Roelvink, M.; De Bono, J.S. Poly (ADP-ribose) polymerase (PARP) inhibitors for the treatment of advanced germline BRCA2 mutant prostate cancer. Ann. Oncol. 2013, 24, 1416–1418. [Google Scholar]
  71. Castro, E.; Goh, C.; Olmos, D.; Saunders, E.; Leongamornlert, D.; Tymrakiewicz, M.; Mahmud, N.; Dadaev, T.; Govindasami, K.; Guy, M.; et al. Germline BRCA mutations are associated with higher risk of nodal involvement, distant metastasis, and poor survival outcomes in prostate cancer. J. Clin. Oncol. 2013, 31, 1748–1757. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  72. Mateo, J.; Carreira, S.; Sandhu, S.; Miranda, S.; Mossop, H.; Perez-Lopez, R.; Rodrigues, D.N.; Robinson, D.; Omlin, A.; Tunariu, N.; et al. DNA-Repair Defects and Olaparib in Metastatic Prostate Cancer. N. Engl. J. Med. 2015, 373, 1697–1708. [Google Scholar] [CrossRef] [PubMed]
  73. Bracci, M.; Ciarapica, V.; Zabaleta, M.E.; Tartaglione, M.F.; Pirozzi, S.; Giuliani, L.; Piva, F.; Valentino, M.; Ledda, C.; Rapisarda, V.; et al. BRCA1 and BRCA2 Gene Expression: Diurnal Variability and Influence of Shift Work. Cancers 2019, 11, 1146. [Google Scholar] [CrossRef] [Green Version]
  74. Satih, S.; Savinel, H.; Rabiau, N.; Fontana, L.; Bignon, Y.J.; Bernard-Gallon, D.J. Expression analyses of nuclear receptor genes in breast cancer cell lines exposed to soy phytoestrogens after BRCA2 knockdown by TaqMan Low-Density Array (TLDA). J. Mol. Signal. 2009, 4, 3. [Google Scholar] [CrossRef] [Green Version]
  75. Blando, J.; Moore, T.; Hursting, S.; Jiang, G.; Saha, A.; Beltran, L.; Shen, J.; Repass, J.; Strom, S.; DiGiovanni, J. Dietary energy balance modulates prostate cancer progression in Hi-Myc mice. Cancer Prev. Res. 2011, 4, 2002–2014. [Google Scholar] [CrossRef] [Green Version]
  76. Adjakly, M.; Ngollo, M.; Dagdemir, A.; Judes, G.; Pajon, A.; Karsli-Ceppioglu, S.; Penault-Llorca, F.; Boiteux, J.P.; Bignon, Y.J.; Guy, L.; et al. Prostate cancer: The main risk and protective factors-Epigenetic modifications. Ann. Endocrinol. 2015, 76, 25–41. [Google Scholar] [CrossRef] [PubMed]
  77. Matsushita, M.; Fujita, K.; Nonomura, N. Influence of Diet and Nutrition on Prostate Cancer. Int. J. Mol. Sci. 2020, 21, 1447. [Google Scholar] [CrossRef] [Green Version]
  78. Flores, I.E.; Sierra-Fonseca, J.A.; Davalos, O.; Saenz, L.A.; Castellanos, M.M.; Zavala, J.K.; Gosselink, K.L. Stress alters the expression of cancer-related genes in the prostate. BMC Cancer 2017, 17, 621. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  79. Moreno-Smith, M.; Lutgendorf, S.K.; Sood, A.K. Impact of stress on cancer metastasis. Future Oncol. 2010, 6, 1863–1881. [Google Scholar] [CrossRef] [Green Version]
  80. Yuan, A.; Wang, S.; Li, Z.; Huang, C. Psychological aspect of cancer: From stressor to cancer progression. Exp. Ther. Med. 2010, 1, 13–18. [Google Scholar] [CrossRef]
  81. Lutgendorf, S.; Sood, A. Biobehavioral factors and cancer progression: Physiological pathways and mechanisms. Psychosom. Med. 2011, 73, 724–730. [Google Scholar] [CrossRef] [Green Version]
  82. Garssen, B. Psychological factors and cancer development: Evidence after 30 years of research. Clin. Psychol. Rev. 2004, 24, 315–338. [Google Scholar] [CrossRef]
  83. Chida, Y.; Hamer, M.; Wardle, J.; Steptoe, A. Do stress-related psychosocial factors contribute to cancer incidence and survival? Nat. Clin. Pract. Oncol. 2008, 5, 466–475. [Google Scholar] [CrossRef] [PubMed]
  84. Denaro, N.; Tomasello, L.; Russi, E.G. Cancer and stress: What’s matter? From epidemiology: The psychologist and oncologist point of view. J. Cancer Ther. Res. 2014, 3, 1–11. [Google Scholar] [CrossRef] [Green Version]
  85. Zhao, X.Y.; Malloy, P.J.; Krishnan, A.V.; Swami, S.; Navone, N.M.; Peehl, D.M.; Feldman, D. Glucocorticoids can promote androgen-independent growth of prostate cancer cells through a mutated androgen receptor. Nat. Med. 2000, 6, 703–706. [Google Scholar] [CrossRef] [PubMed]
  86. Payne, J.K. State of the science: Stress, inflammation, and cancer. Oncol. Nurs. Forum 2013, 41, 533–540. [Google Scholar] [CrossRef]
  87. Powell, N.D.; Tarr, A.J.; Sheridan, J.F. Psychosocial stress and inflammation in cancer. Brain Behav. Immun. 2013, 30, S41–S47. [Google Scholar] [CrossRef] [PubMed]
  88. Hinshaw, D.C.; Shevde, L.A. The Tumor Microenvironment Innately Modulates Cancer Progression. Cancer Res. 2019, 79, 4557–4566. [Google Scholar] [CrossRef] [Green Version]
  89. De Marzo, A.M.; Platz, E.A.; Sutcliffe, S.; Xu, J.; Grönberg, H.; Drake, C.G.; Nakai, Y.; Isaacs, W.B.; Nelson, W.G. Inflammation in prostate carcinogenesis. Nat. Rev. Cancer 2007, 7, 256–269. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  90. Madan, R.A.; Gulley, J.L. Finding an Immunologic Beachhead in the Prostate Cancer Microenvironment. J. Natl. Cancer Inst. 2019, 111, 219–220. [Google Scholar] [CrossRef] [PubMed]
  91. Bishop, J.L.; Sio, A.; Angeles, A.; Roberts, M.; Azad, A.A.; Chi, K.N.; Zoubeidi, A. PD-L1 is highly expressed in Enzalutamide resistant prostate cancer. Oncotarget 2015, 6, 234. [Google Scholar] [CrossRef] [Green Version]
  92. Gaudreau, P.O.; Stagg, J.; Soulieres, D.; Saad, F. The present and future of biomarkers in prostate cancer: Proteomics, genomics, and immunology advancements. Biomark. Cancer 2016, 8 (Suppl. S2), 15–33. [Google Scholar] [CrossRef] [Green Version]
  93. Intasqui, P.; Bertolla, R.P.; Sadi, M.V. Prostate cancer proteomics: Clinically useful protein biomarkers and future perspectives. Expert. Rev. Proteom. 2018, 15, 65–79. [Google Scholar] [CrossRef]
  94. Martin, R.M.; Donovan, J.L.; Turner, E.L.; Metcalfe, C.; Young, G.J.; Walsh, E.I.; CAP Trial Group. Effect of a low-intensity psa-based screening intervention on prostate cancer mortality: The CAP randomized clinical trial. JAMA 2018, 319, 883–895. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  95. Pin, E.; Fredolini, C.; Petricoin, E. F, 3rd. The role of proteomics in prostate cancer research: Biomarker discovery and validation. Clin. Biochem. 2013, 46, 524–538. [Google Scholar] [CrossRef] [PubMed]
Genes 12 01350 g001 550
Figure 1. Differences in gene expression by Gleason score classification (Groups 1 though 5). (A)—EZH2 expression, (B)—MCM7 expression, (C)—BRCA2 expression. (*) p < 0.05.
Figure 1. Differences in gene expression by Gleason score classification (Groups 1 though 5). (A)—EZH2 expression, (B)—MCM7 expression, (C)—BRCA2 expression. (*) p < 0.05.
Genes 12 01350 g001
Genes 12 01350 g002 550
Figure 2. Map of the association among the variables included in the multi-dimensional scaling (MDS) model. Gene expression of target genes and clinical output (Overall Survival and Disease-free Interval) were included in the model. The target genes were grouped in three different clusters according to their main functions. The new dimensions provided by the MDS were named “Survivability” (being the X-axis highly related to the clinical output OS and DFI) and “Inflammation/Proliferation” (two main functions of the clusters of genes identified highly correlated with the Y-axis).
Figure 2. Map of the association among the variables included in the multi-dimensional scaling (MDS) model. Gene expression of target genes and clinical output (Overall Survival and Disease-free Interval) were included in the model. The target genes were grouped in three different clusters according to their main functions. The new dimensions provided by the MDS were named “Survivability” (being the X-axis highly related to the clinical output OS and DFI) and “Inflammation/Proliferation” (two main functions of the clusters of genes identified highly correlated with the Y-axis).
Genes 12 01350 g002
Genes 12 01350 g003 550
Figure 3. K-M of the cumulative disease-free intervals by Cluster 3.
Figure 3. K-M of the cumulative disease-free intervals by Cluster 3.
Genes 12 01350 g003
Table
Table 1. Number of patients by Gleason score and group classification.
Table 1. Number of patients by Gleason score and group classification.
ClassificationPrimary GSSecondary GSN. of Cases
Group 13325
Group 23473
Group 34347
Group 44429
Group 54, 54, 569
GS: Gleason score.
Table
Table 2. Average ± SD of clinical output data (prostate-specific antigen (PSA) scores, Age at diagnosis in years, overall survival (OS) in months, and disease-free interval (DFI) in months) by Gleason score classification (Group 1 through 5).
Table 2. Average ± SD of clinical output data (prostate-specific antigen (PSA) scores, Age at diagnosis in years, overall survival (OS) in months, and disease-free interval (DFI) in months) by Gleason score classification (Group 1 through 5).
Gleason Score
Group 1Group 2Group 3Group 4Group 5TotalFp
MeanSDMeanSDMeanSDMeanSDMeanSDMeanSD
OS34.9127.6338.4629.1437.6123.8641.5524.4636.8926.3637.8526.530.250.91
DFI34.9127.6337.8529.5235.2422.6739.8524.4827.6822.9434.4725.911.790.13
PSA0.080.200.090.400.512.370.812.338.0141.932.4522.271.300.27
Age59.247.9260.537.0661.686.0761.106.0763.036.5261.406.762.010.1
Table
Table 3. Correlation among clinical output (prostate-specific antigen (PSA) scores, Age at diagnosis, overall survival (OS), and disease-free interval (DFI) and target gene expression. Significant values are in bold. (*) p < 0.05; (**) p < 0.01.
Table 3. Correlation among clinical output (prostate-specific antigen (PSA) scores, Age at diagnosis, overall survival (OS), and disease-free interval (DFI) and target gene expression. Significant values are in bold. (*) p < 0.05; (**) p < 0.01.
AgeOSDFIMYCEZH2MCM7BRCA2PDL1cortisolBDNFCTLA4
PSAr −0.011 −0.115 −0.184 **0.322 **0.634 **0.135 * −0.032 0.008 −0.045 −0.044 −0.004
p-value 0.868 0.092 0.007 0.000 0.000 0.048 0.636 0.905 0.514 0.521 0.953
Ager −0.104 −0.105 0.016 0.057 0.107 0.067 0.168 ** −0.011 0.129 * 0.096
p-value 0.105 0.105 0.799 0.379 0.097 0.295 0.009 0.866 0.044 0.137
OSr 0.911 ** −0.018 −0.096 −0.132 *−0.133* −0.125 0.127 * −0.077 −0.009
p-value 0.000 0.783 0.134 0.040 0.039 0.051 0.048 0.231 0.885
DFIr 0.008 −0.136 *−0.155 *−0.127 *−0.133 * 0.092 −0.078 −0.036
p-value 0.897 0.035 0.017 0.049 0.039 0.154 0.229 0.576
Table
Table 4. (a) Stepwise regression of the disease-free interval (DFI) by gene expression. (b) Stepwise regression of the disease-free interval (DFI) by gene-clusters.
Table 4. (a) Stepwise regression of the disease-free interval (DFI) by gene expression. (b) Stepwise regression of the disease-free interval (DFI) by gene-clusters.
(a)
ModelUnstandardized CoefficientsStandardized Coefficientstp
BStd. ErrorBeta
1(Constant)41.2662.357 17.5050.000
BRCA2−1.366 × 10−50.000−0.133−2.0770.039
2(Constant)37.3953.001 12.4630.000
BRCA2−1.407 × 10−50.000−0.137−2.1520.032
cortisol5.340 × 10−60.0000.1312.0630.040
3(Constant)39.3243.100 12.6860.000
BRCA2−1.331 × 10−50.000−0.129−2.0500.042
cortisol8.093 × 10−60.0000.1982.8400.005
BDNF−1.116 × 10−50.000−0.156−2.2260.027
(b)
ModelUnstandardized CoefficientsStandardized Coefficientstp
BStd. ErrorBeta
1(Constant)34.4011.650 20.8520.000
CLUS3−2.0770.749−0.177−2.7740.006
Dependent Variable: Disease Free (Months)
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Boldrini, L.; Faviana, P.; Galli, L.; Paolieri, F.; Erba, P.A.; Bardi, M. Multi-Dimensional Scaling Analysis of Key Regulatory Genes in Prostate Cancer Using the TCGA Database. Genes 2021, 12, 1350. https://doi.org/10.3390/genes12091350

AMA Style

Boldrini L, Faviana P, Galli L, Paolieri F, Erba PA, Bardi M. Multi-Dimensional Scaling Analysis of Key Regulatory Genes in Prostate Cancer Using the TCGA Database. Genes. 2021; 12(9):1350. https://doi.org/10.3390/genes12091350

Chicago/Turabian Style

Boldrini, Laura, Pinuccia Faviana, Luca Galli, Federico Paolieri, Paola Anna Erba, and Massimo Bardi. 2021. "Multi-Dimensional Scaling Analysis of Key Regulatory Genes in Prostate Cancer Using the TCGA Database" Genes 12, no. 9: 1350. https://doi.org/10.3390/genes12091350

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop