AUTHOR=Wang Xiaoyan , Yu Pengcheng , Jia Wei , Wan Bingbing , Ling Zhougui , Tang Yangyang TITLE=Integrating traditional machine learning with qPCR validation to identify solid drug targets in pancreatic cancer: a 5-gene signature study JOURNAL=Frontiers in Pharmacology VOLUME=Volume 15 - 2024 YEAR=2025 URL=https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2024.1539120 DOI=10.3389/fphar.2024.1539120 ISSN=1663-9812 ABSTRACT=BackgroundPancreatic cancer remains one of the deadliest malignancies, largely due to its late diagnosis and lack of effective therapeutic targets.Materials and methodsUsing traditional machine learning methods, including random-effects meta-analysis 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.ResultsThis 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.ConclusionA 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.