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ORIGINAL RESEARCH article

Front. Bioinform.
Sec. Integrative Bioinformatics
Volume 5 - 2025 | doi: 10.3389/fbinf.2025.1523524

DNA methylation biomarker analysis from low-survival-rate cancers based on genetic functional approaches

Provisionally accepted
Yi-Hsuan Tsai Yi-Hsuan Tsai 1*Prasenjit Mitra Prasenjit Mitra 2David Taniar David Taniar 3Tun-Wen Pai Tun-Wen Pai 1*
  • 1 Department of Computer Science and Information Engineering, College of Electrical Engineering and Computer Science, National Taipei University of Technology, Taipei, Taiwan
  • 2 College of Engineering, The Pennsylvania State University, University Park, Pennsylvania, United States
  • 3 Department of Software Systems and Cybersecurity, Monash University, Clayton, Australia

The final, formatted version of the article will be published soon.

    Identifying cancer biomarkers through DNA methylation analysis is a potential approach toward the detection of aberrant changes in epigenetic regulation associated with early-stage cancers. Among all cancer types, cancers with relatively low five-year survival rates and high incidence rates were pancreatic (10%), esophageal (20%), liver (20%), lung (21%), and brain (27%) cancers. This study integrated DNA methylation profiles to identify the common biomarkers with multi-functional analytics across the aforementioned five cancers. ALX3, HOXD8, IRX1, HOXA9, HRH1, PTPRN2, TRIM58, and NPTX2 were identified as important methylation biomarkers for the five cancers characterized by low five-year survival rates. To extend the applicability of these biomarkers, their annotated genetic functions were explored through GO and KEGG pathway analyses. The combination of ALX3, NPTX2, and TRIM58 was selected from distinct functional groups. An accuracy prediction of 93.3% could be achieved by validating the ten most common cancers, including the initial five low-survivalrate cancer types.

    Keywords: comorbidity pattern, Support vector machine, Early detection, KEGG pathway, gene ontology

    Received: 06 Nov 2024; Accepted: 08 Jan 2025.

    Copyright: © 2025 Tsai, Mitra, Taniar and Pai. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence:
    Yi-Hsuan Tsai, Department of Computer Science and Information Engineering, College of Electrical Engineering and Computer Science, National Taipei University of Technology, Taipei, 10608, Taiwan
    Tun-Wen Pai, Department of Computer Science and Information Engineering, College of Electrical Engineering and Computer Science, National Taipei University of Technology, Taipei, 10608, Taiwan

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.