Machine learning and gene network integration reveal prognostic subnetworks and biomarkers in pancreatic cancer.

ABSTRACT:

Background

Pancreatic cancer has a high mortality rate and lacks early detection markers. Advanced methods, such as machine learning (ML) and network analysis, identify central cancer networks with potential diagnostic and prognostic biomarkers, leading to improved tumor targeting strategies.

Methods

We systematically collected pancreatic cancer transcriptome datasets from the databases TCGA, GTEx, and GEO. Weighted gene co-expression network analysis (WGCNA) identified gene modules associated with clinical traits. Multiple machine learning-based feature selection methods (Random Forest, Support Vector Machine, LASSO, ReliefF) and differential gene expression (DGE) analysis prioritized candidate genes. Functional enrichment (Gene Ontology and KEGG pathway database) ex

SUBMITTER: Salihoglu R 

PROVIDER: S-EPMC12495057 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature