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Computational Genomics and Bioinformatics of Cancer
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Computational Genomics and Bioinformatics of Cancer

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

Deadline for manuscript submissions: 20 February 2026 | Viewed by 233

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 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. 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

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Published Papers (1 paper)

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Research

20 pages, 1763 KiB  
Article
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
Viewed by 91
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 [...] Read more.
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)
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