Submit to Special Issue Submit Abstract to Special Issue Review for Genes Propose a Special Issue

Journal Menu

Journal Browser

► Journal Browser

Computational Genomics and Bioinformatics of Cancer

Special Issue Editors
Special Issue Information
Keywords
Benefits of Publishing in a Special Issue
Published Papers

A special issue of Genes (ISSN 2073-4425). This special issue belongs to the section "Bioinformatics".

Deadline for manuscript submissions: 5 August 2026 | Viewed by 12035

Share This Special Issue

Special Issue Editor

Dr. Ilya Ioshikhes

E-Mail
Guest Editor

Independent Researcher, Ottawa, ON, Canada
Interests: gene regulation; computational biology; bioinformatics; genomics; algorithms
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Cancer is one of the deadliest illnesses in the modern world, with multiple challenges for diagnosis, treatment, and patient survival. Yet it is also one of the most enigmatic illnesses with numerous possible causes and factors involved.

Cancer belongs to the category of complex diseases (related to multiple genes, their modifications (both genetic and epigenetic) and regulation, and environmental factors.

In this sense, it is similar to other complex diseases, such as heart disease, diabetes, autoimmune and psychiatric diseases. From scientific standpoint, it may be the most complex disease, often characterized as a family of multiple illnesses, not just one.

This makes cancer formidable scientific challenge. Huge amounts of data, originating from multiple and various sources, should be obtained, classified, and analyzed, to result in practical recommendations. Bioinformatically, this is highly multidimensional task, with all the respective problems and challenges involved.

The present issue invites original research papers and reviews that address this tremendous problem, either in general or in the specific aspects.

We hope that besides understanding the cancer(s) it will be helpful in understanding complex diseases in general.

Dr. Ilya Ioshikhes
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 250 words) can be sent to the Editorial Office for assessment.

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. Genes is an international peer-reviewed open access monthly 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 2600 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

cancer
data analysis
bioinformatics
complex diseases
dimensionality reduction
genetics
epigenetics
gene regulation
molecular mechanism
environmental factors

Benefits of Publishing in a Special Issue

Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (9 papers)

Download All Papers

Order results

Result details

Show export options Show export options

Select all

Export citation of selected articles as:

Research

Jump to: Review

10 pages, 2178 KB

Open AccessArticle

Pan-Cancer Prediction of Genomic Alterations from H&E Whole-Slide Images in a Real-World Clinical Cohort

by Dongheng Ma, Hinano Nishikubo, Tomoya Sano and Masakazu Yashiro

Genes 2026, 17(4), 371; https://doi.org/10.3390/genes17040371 - 25 Mar 2026

Viewed by 456

Abstract

Background: Predicting genomic alterations from routine hematoxylin and eosin (H&E) whole-slide images (WSIs) may help triage molecular testing. Methods: We retrospectively enrolled 437 patients at Osaka Metropolitan University Hospital across 26 cancers, matched with clinical gene-panel data. We curated 1023 binary endpoints across SNV, CNV, and SV categories. We extracted slide embeddings from five pathology foundation models (Prism, GigaPath, Feather, Chief, and Titan) using a unified feature extraction pipeline and benchmarked them using a lightweight downstream Multi-Layer Perceptron (MLP) classifier. Using the best-performing patch feature system, we trained a multi-instance learning model to assess incremental benefit. Results: Titan achieved the highest and most stable transfer performance, with a median endpoint-wise Area Under the Receiver Operating Characteristic curve (AUROC) of 0.77 in the slide benchmarking; at the patch-level, prediction of APC_SNV reached an AUROC of 0.916, and prediction of KRAS_SNV reached an AUROC of 0.811 on the held-out test set. Conclusions: In a heterogeneous clinical gene-panel setting, pathology foundation models can provide strong baseline genomic-prediction signals without additional fine-tuning. We propose a practical, deployment-oriented two-stage workflow: rapid slide-embedding screening to prioritize robust representations and candidate endpoints, followed by patch-level training for high-value tasks where additional performance gains and interpretable regions are clinically worthwhile. Full article

(This article belongs to the Special Issue Computational Genomics and Bioinformatics of Cancer)

► Show Figures

Figure 1

21 pages, 4361 KB

Open AccessArticle

Multi-Omics Analysis of CDKN2A (p16INK4a) in Cervical Carcinoma in the Context of Human Papillomavirus and in Endometrial Carcinoma

by Rasha Elsayim, Heba W. Alhamdi, Nihal Almuraikhi, Mariam Abdulaziz Alkhateeb, Taghreed Mohamed Osman Derar, Sami Habiballa Abdalla Mohamed and Esra’a Abudouleh

Genes 2026, 17(3), 281; https://doi.org/10.3390/genes17030281 - 27 Feb 2026

Viewed by 805

Abstract

Background: CDKN2A (p16^INK4a^) is integral to the regulation of the RB–E2F cell-cycle checkpoint and is widely acknowledged as a surrogate marker for high-risk human papillomavirus (HPV)-related cervical neoplasia. Nevertheless, its diagnostic and prognostic significance in uterine corpus endometrial carcinoma (UCEC), a predominantly HPV-independent malignancy, remains inadequately characterized. This study utilized an integrated multi-omics approach to examine CDKN2A dysregulation in cervical squamous cell carcinoma (CESC) and UCEC. Methods: Pan-cancer and tumor–normal differential expression analyses were performed using TIMER2.0 and GEPIA2 (TCGA/GTEx). Clinicopathological correlations were assessed with UALCAN. Protein expression patterns were analyzed using immunohistochemistry data from the Human Protein Atlas (HPA). Prognostic significance and immune-infiltration associations were evaluated using TCGA survival data and TIMER modules. Independent transcriptomic validation and diagnostic classification performance were assessed using GEO datasets GSE9750 (CESC) and GSE63678 (UCEC), including ROC-AUC analysis with cross-validation. Results: Integrated analyses revealed elevated CDKN2A expression in both CESC and UCEC across multiple transcriptomic cohorts, with pronounced tumor-specific protein expression on immunohistochemistry. TCGA-only tumor–normal RNA comparisons were non-significant, likely due to limited normal sample representation. In independent GEO cohorts, CDKN2A exhibited excellent tumor–normal discrimination in CESC (AUC = 0.982) and moderate discrimination in UCEC (AUC = 0.761). Survival analysis indicated tumor-specific patterns, with limited prognostic stratification in CESC and context-dependent associations in UCEC. Immune-infiltration analysis suggested tumor-type-specific interactions between CDKN2A expression and immune cell subsets. Conclusions: CDKN2A exhibits strong diagnostic performance in HPV-associated cervical cancer and moderate, cohort-dependent discriminatory ability in endometrial carcinoma. These findings reinforce its established diagnostic role in CESC and propose adjunctive utility in UCEC, underscoring the importance of tumor-contextual interpretation of CDKN2A expression in gynecologic malignancies. Full article

(This article belongs to the Special Issue Computational Genomics and Bioinformatics of Cancer)

► Show Figures

Figure 1

19 pages, 1901 KB

Open AccessArticle

The Regulation of p53 by Ubiquitination and Implications for Therapeutic Targeting in Colorectal Cancer

by Ioannis A. Voutsadakis

Genes 2026, 17(3), 270; https://doi.org/10.3390/genes17030270 - 26 Feb 2026

Viewed by 581

Abstract

Background: The turnaround of the tumor suppressor p53 protein, the guardian of the genome, is closely regulated to ensure avoidance of its untimely activation, which could lead to the demise of normal cells. Cancer cells often display mutations in the gene TP53 encoding for p53, which interferes with its normal function. Methods: The genomic series of colorectal cancer from the Cancer Genome Atlas (TCGA) was interrogated to discover genomic alterations and determine the mRNA expression of enzymes affecting p53 ubiquitination in colorectal cancers with wild-type and mutant TP53. Results: Genomic alterations of p53-regulating E3 ubiquitin ligases were uncommon in colorectal cancers, the most frequent being mutations in RCHY1. Several p53-regulating E3 ligases were well expressed in subsets of colorectal cancers, two of which, MDM2 and TRIM24, displayed higher mRNA expressions than the normal colorectal epithelia. The former was particularly upregulated in TP53 wild-type colorectal cancers, and the latter was upregulated in both wild-type and mutant TP53 cancers. Upregulation of TRIM24 in TP53 mutant cancers was observed independently of the type of mutations (gain-of-function or other). Among E3 ligases used in proteolysis-targeting chimeras (PROTACs), VHL was upregulated together with its E2-conjugating enzyme UBE2S in colorectal cancers. Conclusions: This survey of p53-targeting ubiquitin ligases provides a roadmap for potential therapeutic strategies working by promoting the destruction of the mutant protein or reactivating its normal function in TP53-mutated colorectal cancers and promoting p53 function by preventing degradation in TP53 wild-type cancers. Full article

(This article belongs to the Special Issue Computational Genomics and Bioinformatics of Cancer)

► Show Figures

Figure 1

17 pages, 2035 KB

Open AccessArticle

Integrative Computational Analysis of TP53 Exon 5–6 Mutations in Oral Cavity, Prostate, and Breast Cancers in a Senegalese Population

by Mouhamed Mbaye, Fatimata Mbaye and Mbacke Sembene

Genes 2026, 17(2), 245; https://doi.org/10.3390/genes17020245 - 20 Feb 2026

Viewed by 537

Abstract

Background/Objectives: The tumor suppressor gene TP53 is one of the most frequently mutated genes in human cancers, with alterations predominantly affecting its DNA-binding domain (DBD). However, the mutational landscape and functional consequences of TP53 variants remain poorly characterized in African populations. This study aimed to characterize mutations in exons 5–6 of TP53 in oral cavity cancer (OCC), prostate cancer (PC), and breast cancer (BC) in a Senegalese population, and to assess their structural effects, functional consequences, and impact on protein–protein interactions with BCL-2. Methods: Seventy-eight archived tumor DNA samples from Senegalese patients with OCC, PC, and BC were analyzed. Variants were annotated using COSMIC and dbSNP databases. Functional impact was evaluated with PolyPhen-2. Structural stability changes (

Δ Δ G

) were predicted using FoldX, conformational dynamics (

Δ Δ S_{v i b}

) were assessed with ENCoM, and effects on the p53–BCL-2 interaction were analyzed using DDMut-PPI. Statistical analyses were also performed. Results: BC exhibited the highest TP53 mutation frequency, whereas OCC showed greater mutational diversity. Exon-level analysis revealed a significant enrichment of exon 6 mutations in BC. Structural analyses indicated that exon 5 mutations across all cancers and mutations in OCC were predominantly destabilizing and associated with loss-of-function effects. In contrast, recurrent exon 6 mutations in PC and BC, particularly V217L and V218M, were predicted to stabilize the p53 structure. Conformational dynamics differences between exons were significant only in PC. All analyzed mutations were predicted to stabilize the p53–BCL-2 interaction. Conclusions: This integrative in silico study identified cancer and exon-specific TP53 mutation patterns in a Senegalese population, highlighting exon 6 as a context-dependent hotspot with potential oncogenic implication in PC and BC. Despite its computational nature, the study provides valuable insights that merit further investigation. Full article

(This article belongs to the Special Issue Computational Genomics and Bioinformatics of Cancer)

► Show Figures

Graphical abstract

19 pages, 24066 KB

Open AccessArticle

Identification of a Tertiary Lymphoid Structure Signature for Predicting Tumor Outcomes Through Transcriptomics Analysis

by Mengdi Zhou, Fangliangzi Meng, Fan Wu and Chi Zhou

Genes 2026, 17(2), 239; https://doi.org/10.3390/genes17020239 - 16 Feb 2026

Viewed by 742

Abstract

Background: Tertiary lymphoid structures (TLSs) play a crucial role in regulating tumor invasion and metastasis and serve as a promising prognostic biomarker in immunotherapy, influencing survival and immune response in multiple cancers. However, existing studies rely on limited gene signatures to assess TLSs, and there remains a lack of comprehensive TLS-related features for pan-cancer prognosis or immunotherapy response prediction. Methods: Based on published TLS gene signatures, mutation data, and expression profiles from 33 tumor types in TCGA, along with data from 15 immune checkpoint blockade (ICB) cohorts, we first systematically evaluated six TLS gene signatures in relation to immune-related indicators and assessed their predictive and prognostic performance across tumors and immunotherapy. Subsequently, using meta-analysis, we constructed a de novo TLS-related gene feature set, termed predictTLS, designed to predict ICB efficacy and prognosis. The rationality and effectiveness of predictTLS were validated using internal validation sets, single-cell transcriptomic, and spatial transcriptomic data. Results: The evaluation revealed associations between TLS gene signatures and key immune-related indicators. The newly constructed predictTLS feature set demonstrated effectiveness in predicting both ICB therapy outcomes and patient prognosis across the analyzed cohorts. Validation across internal datasets, single-cell profiles, and spatial transcriptomics supported the robustness and biological relevance of predictTLS. Conclusions: This study provides a systematically validated, de novo TLS-related gene signature that can serve as a clinical biomarker for predicting immunotherapy response and prognosis in pan-cancer settings. These findings offer new tools for risk stratification and potential therapeutic targeting in tumor immunotherapy. Full article

(This article belongs to the Special Issue Computational Genomics and Bioinformatics of Cancer)

► Show Figures

Figure 1

21 pages, 7362 KB

Open AccessArticle

Integrative Bioinformatics Analysis Reveals Key Regulatory Genes and Therapeutic Targets in Ulcerative Colitis Pathogenesis

by Sheikh Atikur Rahman, Mst. Tania Khatun, Mahendra Singh, Viplov Kumar Biswas, Forkanul Hoque, Nurun Nesa Zaman, Anzana Parvin, Mohammad Khaja Mafij Uddin, Md. Mominul Islam Sheikh, Most Morium Begum, Rakesh Arya and Hossain Md. Faruquee

Genes 2025, 16(11), 1296; https://doi.org/10.3390/genes16111296 - 1 Nov 2025

Viewed by 1999

Abstract

Background: Ulcerative colitis (UC), a chronic and relapsing form of inflammatory bowel disease (IBD), arises from a multifactorial interplay of genetic predisposition, immune dysregulation, and environmental triggers. Despite advances in understanding UC pathogenesis, the identification of reliable biomarkers and key regulatory genes remains essential for unraveling disease mechanisms. Such insights are crucial for improving diagnostic precision and developing personalized therapeutic strategies. Methods: In this study, gene expression profiles from publicly available microarray and RNA-sequencing datasets were systematically analyzed using advanced bioinformatics tools. Differentially expressed genes (DEGs) were identified through statistical comparisons, and functional enrichment analyses were performed to explore their biological relevance. A total of 141 overlapping DEGs were extracted from three GEO datasets, and 20 key DEGs were further prioritized via protein–protein interaction (PPI) network construction. Hub genes, relevant signaling pathways, associated transcription factors (TFs), and microRNAs (miRNAs) linked to disease progression were identified. Potential therapeutic compounds were also predicted through computational drug–gene interaction analysis. Results: The analysis revealed a panel of novel biomarkers-TLR2, IFNG, CD163, CXCL9, CCL4, PRF1, TLR8, ARG1, LILRB2, FPR2, and PPARG-that function as key hub genes implicated in ulcerative colitis (UC) pathogenesis. These genes were associated with critical biological processes including signal transduction, inflammatory and immune responses, proteolysis, lipid transport, and cholesterol/triglyceride homeostasis. Furthermore, transcription factors (FOXC1, GABPA, GATA2, SUPT5H) and microRNAs (hsa-miR-34a-5p, hsa-miR-335-5p, hsa-miR-24-3p, hsa-miR-23a-5p, hsa-miR-26a-5p) revealed key regulatory networks influencing post-transcriptional gene regulation. Molecular docking analysis predicted Apremilast and Golotimod as promising therapeutic candidates for UC intervention. Conclusions: In conclusion, this study enhances our understanding of ulcerative colitis pathogenesis by identifying key biomarkers and therapeutic targets, paving the way for future advancements in personalized diagnosis and treatment strategies. Full article

(This article belongs to the Special Issue Computational Genomics and Bioinformatics of Cancer)

► Show Figures

Figure 1

20 pages, 1763 KB

Open AccessArticle

Identification of Key Genes Associated with Overall Survival in Glioblastoma Multiforme Using TCGA RNA-Seq Expression Data

by Lilies Handayani, Denis Chegodaev, Ray Steven and Kenji Satou

Genes 2025, 16(7), 755; https://doi.org/10.3390/genes16070755 - 27 Jun 2025

Cited by 1 | Viewed by 4834

Abstract

Background/Objectives: Glioblastoma multiforme (GBM) is an aggressive and heterogeneous brain tumor with poor prognosis, emphasizing the need for reliable molecular biomarkers to improve patient stratification and treatment planning. This study aimed to identify key genes associated with overall survival in GBM by employing and comparing machine learning (ML) and deep learning (DL) approaches using RNA-Seq gene expression data. Methods: RNA-Seq expression and clinical data for primary GBM tumors were obtained from The Cancer Genome Atlas (TCGA). A univariate Cox proportional hazards regression was used to identify survival-associated genes. For survival prediction, ML-based feature selection techniques—RF, GB, SVM-RFE, RF-RFE, and PCA—were used to construct multivariate Cox models. Separately, DeepSurv, a DL-based survival model, was trained using the significant genes from the univariate analysis. Gradient-based importance scoring was applied to determine key genes from the DeepSurv model. Results: Univariate analysis yielded 694 survival-associated genes. The best ML-based Cox model (RF-RFE with 90% training data) achieved a c-index of 0.725. In comparison, DeepSurv demonstrated superior performance with a c-index of 0.822. The top 10 genes were identified from the DeepSurv analysis, including CMTR1, GMPR, and PPY. Kaplan–Meier survival curves confirmed their prognostic significance, and network analysis highlighted their roles in processes such as purine metabolism, RNA processing, and neuroendocrine signaling. Conclusions: This study demonstrates the effectiveness of combining ML and DL models to identify prognostic gene expression biomarkers in GBM, with DeepSurv providing higher predictive accuracy. The findings offer valuable insights into GBM biology and highlight candidate biomarkers for further validation and therapeutic development. Full article

(This article belongs to the Special Issue Computational Genomics and Bioinformatics of Cancer)

► Show Figures

Figure 1

Review

Jump to: Research

20 pages, 999 KB

Open AccessReview

Emerging Genomic and Immunological Correlates Defining Oligometastatic Trajectories in Intermediate/High-Grade Soft-Tissue Sarcomas

by Alessandro Ottaiano, Francesco Sabbatino, Carmine Picone, Nadia Di Carluccio, Igino Simonetti, Annabella Di Mauro and Salvatore Tafuto

Genes 2026, 17(3), 323; https://doi.org/10.3390/genes17030323 - 16 Mar 2026

Viewed by 486

Abstract

Soft-tissue sarcomas (STSs) comprise a rare, heterogeneous group of mesenchymal malignancies in which histologic grade remains the strongest determinant of outcome, metastatic risk, and therapeutic strategy. Intermediate/high-grade STSs exhibit a pronounced propensity for early distant relapse, yet growing evidence indicates that metastatic behaviour is not uniform. Within this spectrum, an oligometastatic phenotype, characterised by a limited number of metastases, often confined to the lung, has emerged as a clinically and biologically distinct state associated with more indolent metastatic kinetics and improved survival when treated with aggressive local interventions. However, the criteria that define true oligometastatic STSs remain unsettled, and prospective evidence is lacking. Emerging molecular and immunological correlates provide a potential framework for biological triage. Low genomic complexity (low-risk CINSARC), a B-cell/TLS-rich tumour microenvironment, high immune-cytotoxic signatures, and persistently low or undetectable circulating tumour DNA (ctDNA) are each linked to reduced metastatic competence and may underpin oligometastatic trajectories. Conversely, high chromosomal instability, immunosuppressive microenvironments, and elevated ctDNA levels align with covertly polymetastatic biology despite limited radiographic disease. In this context, artificial intelligence and machinelearning approaches applied to computational genomics, immune profiling, imaging, and liquid-biopsy data offer a powerful strategy to integrate these multi-dimensional features and refine predictions of metastatic behaviour in STS. Oligometastatic STS therefore represents a biologically definable subset amenable to multimodal management integrating local ablative therapies, systemic agents, and immune-based strategies. Prospective, biomarker-stratified trials are needed to validate selection frameworks and optimise treatment sequencing in this evolving therapeutic space. Full article

(This article belongs to the Special Issue Computational Genomics and Bioinformatics of Cancer)

► Show Figures

Figure 1

14 pages, 823 KB

Open AccessReview

Genomic Subtypes and Computational Biomarkers in Non-Muscle-Invasive Bladder Cancer Guiding Optimal Timing of Radical Cystectomy and BCG Response Prediction

by Vlad-Horia Schițcu, Vlad Cristian Munteanu, Mihnea Bogdan Borz, Ion Cojocaru, Octavia Morari, Mircea Gîrbovan and Andrei-Ionuț Tișe

Genes 2026, 17(2), 153; https://doi.org/10.3390/genes17020153 - 29 Jan 2026

Viewed by 782

Abstract

Non-muscle-invasive bladder cancer (NMIBC) accounts for approximately 70% of newly diagnosed bladder cancer cases but exhibits significant clinical heterogeneity in treatment response and progression risk. While intravesical bacillus Calmette–GuérinCa (BCG) therapy remains the gold standard for high-risk disease, approximately 30–50% of patients experience BCG failure, creating a critical decision point between additional bladder-sparing therapy (BST) and early radical cystectomy (RC). Recent clinical data from the CISTO study suggest that, in appropriately selected patients, RC may be associated with higher 12-month recurrence-free survival while maintaining comparable cancer-specific survival and physical functioning. In this narrative review, we synthesize contemporary evidence on NMIBC genomic and transcriptomic subtypes, immune contexture, and clinicopathologic features associated with BCG response and progression risk, with emphasis on clinically oriented classification systems such as BCG Response Subtypes (BRS1–3) and UROMOL21. We highlight how tumor-intrinsic biology (e.g., EMT-associated programs), immune phenotypes (inflamed vs. immune-cold microenvironments), and genomic alterations may help refine risk stratification beyond traditional clinicopathologic models. To facilitate clinical integration, we propose a conceptual decisional framework that combines molecular subtype assignment, immune profiling, key pathologic risk factors, and patient considerations to generate probabilistic risk tiers that support selection among early RC, BST, and clinical trial strategies. Standardized multicenter cohorts and prospective evaluation are needed to validate integrated models and define their clinical utility for the precision timing of cystectomy in BCG-unresponsive NMIBC. Full article

(This article belongs to the Special Issue Computational Genomics and Bioinformatics of Cancer)

► Show Figures

Journal Menu

Journal Browser

Computational Genomics and Bioinformatics of Cancer

Share This Special Issue

Special Issue Editor

Special Issue Information

Keywords

Benefits of Publishing in a Special Issue

Published Papers (9 papers)

Research

Review

Further Information

Guidelines

MDPI Initiatives

Follow MDPI