Bioinformatics in Cancer Diagnostics and Screening

A special issue of Cancers (ISSN 2072-6694). This special issue belongs to the section "Cancer Informatics and Big Data".

Deadline for manuscript submissions: closed (30 November 2023) | Viewed by 11675

Special Issue Editor

Department of Biomedical Sciences and Human Oncology, Università degli Studi di Bari Aldo Moro, Bari, Italy
Interests: hereditary diseases; variant interpretation; splicing regulation; cancer genetics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Over the last few years, the growing knowledge on cancer genetics and molecular bases has changed the way physicians, researchers, and patients approach cancer diagnosis and treatment. The use of bioinformatics in cancer diagnostics and screening represents a very recent development and an emerging field which combines omics-based technologies, artificial intelligence, and clinical and computational sciences.

The integration of clinical information with molecular profiling is improving cancer diagnosis, therapies, and prognosis. Bioinformatics in cancer is expected to play a pivotal role in the identification and validation of cancer biomarkers and its association with specific clinical phenotypes, including disease progression monitoring, response to therapy, and accurate prognosis prediction.

These recent findings are translating “precision medicine” into a routine practice for all professionals in the field and are improving cancer patients’ quality of life.

“Bioinformatics in Cancer Diagnostics and Screening” calls for papers dealing with the development of novel methods to diagnose and screen for human cancers using bioinformatics, machine (deep) learning, neural networks, processing and analysis of biological data, and anticancer drug discovery and design. This SI aims to collect works that have successfully translated into clinical practice or have high chances of becoming clinically relevant.

Prof. Dr. Alessandro Stella
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Cancers is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2900 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • bioinformatics
  • cancer diagnosis
  • cancer screening
  • biomarkers
  • precision medicine
  • omics mass screening
  • drug design

Published Papers (7 papers)

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Research

23 pages, 4255 KiB  
Article
Comprehensive Profiling and Therapeutic Insights into Differentially Expressed Genes in Hepatocellular Carcinoma
by Wesley Ladeira Caputo, Milena Cremer de Souza, Caroline Rodrigues Basso, Valber de Albuquerque Pedrosa and Fábio Rodrigues Ferreira Seiva
Cancers 2023, 15(23), 5653; https://doi.org/10.3390/cancers15235653 - 30 Nov 2023
Cited by 2 | Viewed by 1053
Abstract
Background: Drug repurposing is a strategy that complements the conventional approach of developing new drugs. Hepatocellular carcinoma (HCC) is a highly prevalent type of liver cancer, necessitating an in-depth understanding of the underlying molecular alterations for improved treatment. Methods: We searched [...] Read more.
Background: Drug repurposing is a strategy that complements the conventional approach of developing new drugs. Hepatocellular carcinoma (HCC) is a highly prevalent type of liver cancer, necessitating an in-depth understanding of the underlying molecular alterations for improved treatment. Methods: We searched for a vast array of microarray experiments in addition to RNA-seq data. Through rigorous filtering processes, we have identified highly representative differentially expressed genes (DEGs) between tumor and non-tumor liver tissues and identified a distinct class of possible new candidate drugs. Results: Functional enrichment analysis revealed distinct biological processes associated with metal ions, including zinc, cadmium, and copper, potentially implicating chronic metal ion exposure in tumorigenesis. Conversely, up-regulated genes are associated with mitotic events and kinase activities, aligning with the relevance of kinases in HCC. To unravel the regulatory networks governing these DEGs, we employed topological analysis methods, identifying 25 hub genes and their regulatory transcription factors. In the pursuit of potential therapeutic options, we explored drug repurposing strategies based on computational approaches, analyzing their potential to reverse the expression patterns of key genes, including AURKA, CCNB1, CDK1, RRM2, and TOP2A. Potential therapeutic chemicals are alvocidib, AT-7519, kenpaullone, PHA-793887, JNJ-7706621, danusertibe, doxorubicin and analogues, mitoxantrone, podofilox, teniposide, and amonafide. Conclusion: This multi-omic study offers a comprehensive view of DEGs in HCC, shedding light on potential therapeutic targets and drug repurposing opportunities. Full article
(This article belongs to the Special Issue Bioinformatics in Cancer Diagnostics and Screening)
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15 pages, 3315 KiB  
Article
Identification of a Novel Eight-Gene Risk Model for Predicting Survival in Glioblastoma: A Comprehensive Bioinformatic Analysis
by Huy-Hoang Dang, Hoang Dang Khoa Ta, Truc Tran Thanh Nguyen, Chih-Yang Wang, Kuen-Haur Lee and Nguyen Quoc Khanh Le
Cancers 2023, 15(15), 3899; https://doi.org/10.3390/cancers15153899 - 31 Jul 2023
Cited by 1 | Viewed by 1476
Abstract
Glioblastoma (GBM) is one of the most progressive and prevalent cancers of the central nervous system. Identifying genetic markers is therefore crucial to predict prognosis and enhance treatment effectiveness in GBM. To this end, we obtained gene expression data of GBM from TCGA [...] Read more.
Glioblastoma (GBM) is one of the most progressive and prevalent cancers of the central nervous system. Identifying genetic markers is therefore crucial to predict prognosis and enhance treatment effectiveness in GBM. To this end, we obtained gene expression data of GBM from TCGA and GEO datasets and identified differentially expressed genes (DEGs), which were overlapped and used for survival analysis with univariate Cox regression. Next, the genes’ biological significance and potential as immunotherapy candidates were examined using functional enrichment and immune infiltration analysis. Eight prognostic-related DEGs in GBM were identified, namely CRNDE, NRXN3, POPDC3, PTPRN, PTPRN2, SLC46A2, TIMP1, and TNFSF9. The derived risk model showed robustness in identifying patient subgroups with significantly poorer overall survival, as well as those with distinct GBM molecular subtypes and MGMT status. Furthermore, several correlations between the expression of the prognostic genes and immune infiltration cells were discovered. Overall, we propose a survival-derived risk score that can provide prognostic significance and guide therapeutic strategies for patients with GBM. Full article
(This article belongs to the Special Issue Bioinformatics in Cancer Diagnostics and Screening)
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0 pages, 6599 KiB  
Article
Analyses of Genes Critical to Tumor Survival Reveal Potential ‘Supertargets’: Focus on Transcription
by Darya Chetverina, Nadezhda E. Vorobyeva, Balazs Gyorffy, Alexander A. Shtil and Maksim Erokhin
Cancers 2023, 15(11), 3042; https://doi.org/10.3390/cancers15113042 - 03 Jun 2023
Cited by 1 | Viewed by 1745
Abstract
The identification of mechanisms that underlie the biology of individual tumors is aimed at the development of personalized treatment strategies. Herein, we performed a comprehensive search of genes (termed Supertargets) vital for tumors of particular tissue origin. In so doing, we used the [...] Read more.
The identification of mechanisms that underlie the biology of individual tumors is aimed at the development of personalized treatment strategies. Herein, we performed a comprehensive search of genes (termed Supertargets) vital for tumors of particular tissue origin. In so doing, we used the DepMap database portal that encompasses a broad panel of cell lines with individual genes knocked out by CRISPR/Cas9 technology. For each of the 27 tumor types, we revealed the top five genes whose deletion was lethal in the particular case, indicating both known and unknown Supertargets. Most importantly, the majority of Supertargets (41%) were represented by DNA-binding transcription factors. RNAseq data analysis demonstrated that a subset of Supertargets was deregulated in clinical tumor samples but not in the respective non-malignant tissues. These results point to transcriptional mechanisms as key regulators of cell survival in specific tumors. Targeted inactivation of these factors emerges as a straightforward approach to optimize therapeutic regimens. Full article
(This article belongs to the Special Issue Bioinformatics in Cancer Diagnostics and Screening)
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12 pages, 1017 KiB  
Article
Platelet-Based Liquid Biopsies through the Lens of Machine Learning
by Sebastian Cygert, Krzysztof Pastuszak, Franciszek Górski, Michał Sieczczyński, Piotr Juszczyk, Antoni Rutkowski, Sebastian Lewalski, Robert Różański, Maksym Albin Jopek, Jacek Jassem, Andrzej Czyżewski, Thomas Wurdinger, Myron G. Best, Anna J. Żaczek and Anna Supernat
Cancers 2023, 15(8), 2336; https://doi.org/10.3390/cancers15082336 - 17 Apr 2023
Cited by 2 | Viewed by 1455
Abstract
Liquid biopsies offer minimally invasive diagnosis and monitoring of cancer disease. This biosource is often analyzed using sequencing, which generates highly complex data that can be used using machine learning tools. Nevertheless, validating the clinical applications of such methods is challenging. It requires: [...] Read more.
Liquid biopsies offer minimally invasive diagnosis and monitoring of cancer disease. This biosource is often analyzed using sequencing, which generates highly complex data that can be used using machine learning tools. Nevertheless, validating the clinical applications of such methods is challenging. It requires: (a) using data from many patients; (b) verifying potential bias concerning sample collection; and (c) adding interpretability to the model. In this work, we have used RNA sequencing data of tumor-educated platelets (TEPs) and performed a binary classification (cancer vs. no-cancer). First, we compiled a large-scale dataset with more than a thousand donors. Further, we used different convolutional neural networks (CNNs) and boosting methods to evaluate the classifier performance. We have obtained an impressive result of 0.96 area under the curve. We then identified different clusters of splice variants using expert knowledge from the Kyoto Encyclopedia of Genes and Genomes (KEGG). Employing boosting algorithms, we identified the features with the highest predictive power. Finally, we tested the robustness of the models using test data from novel hospitals. Notably, we did not observe any decrease in model performance. Our work proves the great potential of using TEP data for cancer patient classification and opens the avenue for profound cancer diagnostics. Full article
(This article belongs to the Special Issue Bioinformatics in Cancer Diagnostics and Screening)
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23 pages, 6671 KiB  
Article
Systematic Investigation of the Multifaceted Role of SOX11 in Cancer
by Qingqing Sun, Jun Du, Jie Dong, Shuaikang Pan, Hongwei Jin, Xinghua Han and Jinguo Zhang
Cancers 2022, 14(24), 6103; https://doi.org/10.3390/cancers14246103 - 12 Dec 2022
Cited by 1 | Viewed by 1618
Abstract
SRY-box transcription factor 11 (SOX11), as a member of the SOX family, is a transcription factor involved in the regulation of specific biological processes and has recently been found to be a prognostic marker for certain cancers. However, the roles of [...] Read more.
SRY-box transcription factor 11 (SOX11), as a member of the SOX family, is a transcription factor involved in the regulation of specific biological processes and has recently been found to be a prognostic marker for certain cancers. However, the roles of SOX11 in cancer remain controversial. Our study aimed to explore the various aspects of SOX11 in pan-cancer. The expression of SOX11 was investigated by the Genotype Tissue-Expression (GTEX) dataset and the Cancer Genome Atlas (TCGA) database. The protein level of SOX11 in tumor tissues and tumor-adjacent tissues was verified by human pan-cancer tissue microarray. Additionally, we used TCGA pan-cancer data to analyze the correlations among SOX11 expression and survival outcomes, clinical features, stemness, microsatellite instability (MSI), tumor mutation burden (TMB), mismatch repair (MMR) related genes and the tumor immune microenvironment. Furthermore, the cBioPortal database was applied to investigate the gene alterations of SOX11. The main biological processes of SOX11 in cancers were analyzed by Gene Set Enrichment Analysis (GSEA). As a result, aberrant expression of SOX11 has been implicated in 27 kinds of cancer types. Aberrant SOX11 expression was closely associated with survival outcomes, stage, tumor recurrence, MSI, TMB and MMR-related genes. In addition, the most frequent alteration of the SOX11 genome was mutation. Our study also showed the correlations of SOX11 with the level of immune infiltration in various cancers. In summary, our findings underline the multifaceted role and prognostic value of SOX11 in pan-cancer. Full article
(This article belongs to the Special Issue Bioinformatics in Cancer Diagnostics and Screening)
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10 pages, 945 KiB  
Article
Analysis of the Risk of Oral Squamous Cell Carcinoma in Patients with and without Recurrent Aphthous Stomatitis: A Retrospective Evaluation of Real-World Data of about 150,000 Patients
by Moritz Hertel, Senem Birinci, Max Heiland, Robert Preissner, Susanne Nahles, Andrea-Maria Schmidt-Westhausen and Saskia Preissner
Cancers 2022, 14(23), 6011; https://doi.org/10.3390/cancers14236011 - 06 Dec 2022
Cited by 1 | Viewed by 1473
Abstract
Background: Recurrent aphthous stomatitis (RAS) is found among the most frequent diseases of the oral cavity. It is characterized by repeated formation of painful ulcers. The question has risen if due to potential tumor-promoting inflammation and sustaining proliferative signaling RAS may contribute to [...] Read more.
Background: Recurrent aphthous stomatitis (RAS) is found among the most frequent diseases of the oral cavity. It is characterized by repeated formation of painful ulcers. The question has risen if due to potential tumor-promoting inflammation and sustaining proliferative signaling RAS may contribute to oral cancer. Accordingly, the aim of the study was to assess if an association of RAS and the development oral squamous cell carcinoma (OSCC) could be found in a larger cohort. As recurrent aphthous stomatitis is not classified as an oral potentially malignant disorder, it was assumed that the risk of OSCC did not differ between patients with (cohort I) and without RAS (cohort II). Methods: Retrospective clinical data of patients diagnosed with and without RAS (International Classification of Diseases (ICD)-10 code K12) within the past 20 years and a body mass index of 19–30 kg/m2 were retrieved from the TriNetX database to gain initial cohort 0. Subjects suffering from RAS were assigned to cohort I, whereby cohort II was obtained from the remaining individuals, and by matching for age, gender, as well as (history of) nicotine and alcohol dependence. After defining the primary outcome as “OSCC” (ICD-10 codes C00-C14), a Kaplan–Meier analysis was performed, and risk and odds ratios were calculated. Results: Of a total of 24,550,479 individuals in cohort 0, 72,845 subjects were each assigned to cohort I (females: 44,031 (60.44%); males: 28,814 (39.56%); mean current age (±standard deviation) = 35.51 ± 23.55 years) and II (females: 44,032 (60.45%); males: 28,813 (39.55%); mean current age (±standard deviation) = 35.51 ± 23.56 years). Among the cohorts I and II, 470 and 135 patients were diagnosed with OSCC within five years. The according risk of developing oral cancer was 0.65% and 0.18%, whereby the risk difference of 0.47% was highly significant (p < 0.0001; Log-Rank test). The RR and OR were calculated as 3.48 (95% confidence interval (CI) lower: 2.88 and upper: 4.21) and 3.50 (95% CI lower: 2.89 and upper: 4.24). Conclusions: Among the patients suffering from RAS, a significantly augmented risk of developing OSCC was found. However, it has to be emphasized that the recent literature does not provide any confirmatory evidence that supports the retrieved results. Furthermore, the findings need to be interpreted cautiously due to specific limitations that come along with the applied methods. It should thus far only be concluded that further research is necessary to evaluate hypotheses that may be retrieved from the obtained results. Despite this controversy, oral ulcers suspicious of OSCC should undergo biopsy. Trial Registration: Due to the retrospective nature of the study, no registration was necessary. Full article
(This article belongs to the Special Issue Bioinformatics in Cancer Diagnostics and Screening)
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21 pages, 5443 KiB  
Article
A Novel m7G-Related Genes-Based Signature with Prognostic Value and Predictive Ability to Select Patients Responsive to Personalized Treatment Strategies in Bladder Cancer
by Guichuan Lai, Xiaoni Zhong, Hui Liu, Jielian Deng, Kangjie Li and Biao Xie
Cancers 2022, 14(21), 5346; https://doi.org/10.3390/cancers14215346 - 29 Oct 2022
Cited by 6 | Viewed by 1621
Abstract
Although N7-methylguanosine (m7G) modification serves as a tumor promoter in bladder cancer (BLCA), the comprehensive role of m7G-related characterization in BLCA remains unclear. In this study, we systematically evaluated the m7G-related clusters of 760 BLCA patients through consensus unsupervised clustering analysis. Next, we [...] Read more.
Although N7-methylguanosine (m7G) modification serves as a tumor promoter in bladder cancer (BLCA), the comprehensive role of m7G-related characterization in BLCA remains unclear. In this study, we systematically evaluated the m7G-related clusters of 760 BLCA patients through consensus unsupervised clustering analysis. Next, we investigated the underlying m7G-related genes among these m7G-related clusters. Univariate Cox and LASSO regressions were used for screening out prognostic genes and for reducing the dimension, respectively. Finally, we developed a novel m7G-related scoring system via the GSVA algorithm. The correlation between tumor microenvironment, prediction of personalized therapies and this m7G-related signature was gradually revealed. We first identified three m7G-related clusters and 1108 differentially expressed genes relevant to the three clusters. Based on the profile of 1108 genes, we divided BLCA patients into two clusters, which were quantified by our established m7G-related scoring system. Patients with higher m7G-related scores tended to have a better OS and more chances to benefit from immunotherapy. A significantly negative connection between sensitivity to classic chemotherapeutic drugs and m7G-related signature was uncovered. In summary, our data show that m7G-related characterization of BLCA patients can be of value for prognostic stratification and for patient-oriented therapeutic options, designing personalized treatment strategies in the preclinical setting. Full article
(This article belongs to the Special Issue Bioinformatics in Cancer Diagnostics and Screening)
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