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ORIGINAL RESEARCH article

Front. Pharmacol.
Sec. Experimental Pharmacology and Drug Discovery
Volume 15 - 2024 | doi: 10.3389/fphar.2024.1539120
This article is part of the Research Topic Advances in Biomarkers and Drug Targets: Harnessing Traditional and AI Approaches for Novel Therapeutic Mechanisms View all articles

Integrating Traditional Machine Learning with qPCR Validation to Identify Solid Drug Targets in Pancreatic Cancer: A 5-Gene Signature Study

Provisionally accepted
Xiaoyan Wang Xiaoyan Wang 1*Pengcheng Yu Pengcheng Yu 2*Zhougui Ling Zhougui Ling 2Wei Jia Wei Jia 3*Bingbing Wan Bingbing Wan 1Yangyang Tang Yangyang Tang 2*
  • 1 Key Laboratory of Systems Biomedicine, Ministry of Education, Shanghai Center for Systems Biomedicine, Shanghai Jiao Tong University, Shanghai, China
  • 2 The Fourth Affiliated Hospital of Guangxi Medical University, Liuzhou,, Liuzhou, China
  • 3 Department of Pharmacology and Pharmacy, LKS Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong, SAR China

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

    Background: Pancreatic cancer remains one of the deadliest malignancies, largely due to its late diagnosis and lack of effective therapeutic targets.Using traditional machine learning methods, including random-effects metaanalysis and forward-search optimization, we developed a robust signature validated across 14 publicly available datasets, achieving a summary AUC of 0.99 in training datasets and 0.89 in external validation datasets. To further validate its clinical relevance, we analyzed 55 peripheral blood samples from pancreatic cancer patients and healthy controls using qPCR.Results: This study identifies and validates a novel five-gene transcriptomic signature (LAMC2, TSPAN1, MYO1E, MYOF, and SULF1) as both diagnostic biomarkers and potential drug targets for pancreatic cancer. The differential expression of these genes was confirmed, demonstrating their utility in distinguishing cancer from normal conditions with an AUC of 0.83. These findings establish the five-gene signature as a promising tool for both early, non-invasive diagnostics and the identification of actionable drug targets.A five-gene signature is established robustly and has utility in diagnostics and therapeutic targeting. These findings lay a foundation for developing diagnostic tests and targeted therapies, potentially offering a pathway toward improved outcomes in pancreatic cancer management.

    Keywords: Pancreatic Cancer, biomarkers, Peripheral Blood, Drug Targets, machine learning

    Received: 03 Dec 2024; Accepted: 20 Dec 2024.

    Copyright: © 2024 Wang, Yu, Ling, Jia, Wan and Tang. 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:
    Xiaoyan Wang, Key Laboratory of Systems Biomedicine, Ministry of Education, Shanghai Center for Systems Biomedicine, Shanghai Jiao Tong University, Shanghai, 200240, China
    Pengcheng Yu, The Fourth Affiliated Hospital of Guangxi Medical University, Liuzhou,, Liuzhou, China
    Wei Jia, Department of Pharmacology and Pharmacy, LKS Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong, SAR China
    Yangyang Tang, The Fourth Affiliated Hospital of Guangxi Medical University, Liuzhou,, Liuzhou, China

    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.