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Bioinformatics and Computational Biology for Cancer Prediction and Prognosis, 2nd...
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Bioinformatics and Computational Biology for Cancer Prediction and Prognosis, 2nd Edition

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

Deadline for manuscript submissions: closed (5 May 2025) | Viewed by 2561

Special Issue Editors

Dr. Garrett M. Dancik

E-Mail Website
Guest Editor
Department of Computer Science, Eastern Connecticut State University, Willimantic, CT, USA
Interests: bioinformatics and computational biology; cancer bioinformatics
Special Issues, Collections and Topics in MDPI journals
Dr. Spiros Vlahopoulos

E-Mail Website
Guest Editor
First Department of Pediatrics, National and Kapodistrian University of Athens, 11527 Goudi-Athens, Greece
Interests: cancer biology; leukemia
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Bioinformatics tools play a vital role in understanding the biological complexity of cancer through the extraction of meaningful information from a large volume of diverse datasets. Of utmost importance are tools for data analysis, visualization, and interpretation that would aid in the attainment of personalized medicine based on omics (genomic, transcriptomic, or proteomic) data, as well as on images and texts.

This Special Issue aims to provide an overview of new and current bioinformatics tools for cancer prediction and prognosis. Contributions may describe novel approaches, or the application of new and existing ones, that aid in the identification of diagnostic, prognostic, or predictive cancer biomarkers; that identify potential therapeutic targets and important cancer-related pathways; or that otherwise provide valuable insight into cancer biology and treatment. To make progress in the field of cancer bioinformatics, contributions by experts in the field in the form of research papers and critical reviews are welcome.

Dr. Garrett M. Dancik
Dr. Spiros Vlahopoulos
Guest Editors

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 bioinformatics
  • biomarkers
  • biostatistics
  • genomic sequencing
  • image recognition
  • databases

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Published Papers (3 papers)

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Research

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19 pages, 24469 KiB  
Article
Beyond Transposons: TIGD1 as a Pan-Cancer Biomarker and Immune Modulator
by Merve Gulsen Bal Albayrak, Tuğcan Korak, Gurler Akpinar and Murat Kasap
Genes 2025, 16(6), 674; https://doi.org/10.3390/genes16060674 - 30 May 2025
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Abstract
Background/Objectives: TIGD1 (Trigger Transposable Element Derived 1) is a recently identified oncogene with largely unexplored biological functions. Emerging evidence suggests its involvement in multiple cellular processes across cancer types. This study aimed to perform a comprehensive pan-cancer analysis of TIGD1 to evaluate [...] Read more.
Background/Objectives: TIGD1 (Trigger Transposable Element Derived 1) is a recently identified oncogene with largely unexplored biological functions. Emerging evidence suggests its involvement in multiple cellular processes across cancer types. This study aimed to perform a comprehensive pan-cancer analysis of TIGD1 to evaluate its expression patterns, diagnostic utility, prognostic value, and association with immunotherapy response and drug resistance. Methods: Transcriptomic and clinical data from TCGA and GTEx were analyzed using various bioinformatic tools. Expression profiling, survival analysis, immune correlation studies, gene set enrichment, single-cell sequencing, and drug sensitivity assessments were performed. Results: TIGD1 was found to be significantly upregulated in various tumor types, with notably high expression in colon adenocarcinoma. Elevated TIGD1 expression was associated with poor prognosis in several cancers. TIGD1 levels correlated with key features of the tumor immune microenvironment, including immune checkpoint gene expression, TMB, and MSI, suggesting a role in modulating anti-tumor immunity. GSEA and single-cell analyses implicated TIGD1 in oncogenic signaling pathways. Furthermore, high TIGD1 expression was linked to resistance to several therapeutic agents, including Zoledronate, Dasatinib, and BLU-667. Conclusions: TIGD1 may serve as a promising diagnostic and prognostic biomarker, particularly in colon, gastric, liver, and lung cancers. Its strong associations with immune modulation and therapy resistance highlight its potential as a novel target for precision oncology and immunotherapeutic intervention. Full article
(This article belongs to the Special Issue Bioinformatics and Computational Biology for Cancer Prediction and Prognosis, 2nd Edition)
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10 pages, 761 KiB  
Article
CCNE1 Gene Amplification Might Be Associated with Lymph Node Metastasis of Gastric Cancer
by Hinano Nishikubo, Kyoka Kawabata, Tomoya Sano, Saki Kanei, Rika Aoyama, Dongheng Ma, Daiki Imanishi, Takashi Sakuma, Koji Maruo, Yurie Yamamoto, Canfeng Fan and Masakazu Yashiro
Genes 2025, 16(6), 617; https://doi.org/10.3390/genes16060617 - 22 May 2025
Viewed by 400
Abstract
Background: Lymph node (LN) metastasis is one of the most frequent metastatic patterns in patients with gastric cancer (GC); however, few genes predictive of LN status in GC have been identified. Aims: We aimed to identify candidate genes associated with LN [...] Read more.
Background: Lymph node (LN) metastasis is one of the most frequent metastatic patterns in patients with gastric cancer (GC); however, few genes predictive of LN status in GC have been identified. Aims: We aimed to identify candidate genes associated with LN metastasis by analyzing the Center for Cancer Genomics and Advanced Therapeutics (C-CAT) database and performing immunohistochemical analysis of GC cases at our hospital. Patients and Methods: A total of 2028 GCs from the C-CAT database were enrolled to identify genetic alterations. A total of 360 GC patients who underwent gastrectomy at our hospital were enrolled to examine the clinical significance of CCNE1 expression via an immunohistochemical study. Results: A total of 977 cases out of 2028 GC patients showed LN metastasis. Genetic alterations of ERBB2, CCNE1, MYC, ZNF217, and GNAS were frequent in the LN metastasis group. CCNE1-positive expression was found in 108 (30.0%) of the 360 GC samples. LN metastasis was significantly (p = 0.01) more frequent in CCNE1-positive patients. In addition, the CCNE1-positive group had a significantly (p < 0.001) poorer prognosis than the CCNE1-negative group, which was especially evident for GC patients at stage I. CCNE1 positivity was significantly (p < 0.001) correlated with postoperative recurrence. Conclusions: CCNE1 gene amplification is associated with LN metastasis of GC. Full article
(This article belongs to the Special Issue Bioinformatics and Computational Biology for Cancer Prediction and Prognosis, 2nd Edition)
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Review

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23 pages, 1673 KiB  
Review
Multiomics with Evolutionary Computation to Identify Molecular and Module Biomarkers for Early Diagnosis and Treatment of Complex Disease
by Han Cheng, Mengyu Liang, Yiwen Gao, Wenshan Zhao and Wei-Feng Guo
Genes 2025, 16(3), 244; https://doi.org/10.3390/genes16030244 - 20 Feb 2025
Viewed by 953
Abstract
It is important to identify disease biomarkers (DBs) for early diagnosis and treatment of complex diseases in personalized medicine. However, existing methods integrating intelligence technologies and multiomics to predict key biomarkers are limited by the complex dynamic characteristics of omics data, making it [...] Read more.
It is important to identify disease biomarkers (DBs) for early diagnosis and treatment of complex diseases in personalized medicine. However, existing methods integrating intelligence technologies and multiomics to predict key biomarkers are limited by the complex dynamic characteristics of omics data, making it difficult to meet the high-precision requirements for biomarker characterization in large dimensions. This study reviewed current analysis methods of evolutionary computation (EC) by considering the essential characteristics of DB identification problems and the advantages of EC, aiming to explore the complex dynamic characteristics of multiomics. In this study, EC-based biomarker identification strategies were summarized as evolutionary algorithms, swarm intelligence and other EC methods for molecular and module DB identification, respectively. Finally, we pointed out the challenges in current research and future research directions. This study can enrich the application of EC theory and promote interdisciplinary integration between EC and bioinformatics. Full article
(This article belongs to the Special Issue Bioinformatics and Computational Biology for Cancer Prediction and Prognosis, 2nd Edition)
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