A systems biology approach to unveil shared therapeutic targets and pathological pathways across major human cancers.

ABSTRACT: Cancer remains a major cause of global mortality. Despite their distinct clinical and molecular characteristics, different cancer types often share fundamental molecular mechanisms that remain underexplored. In this study, we systematically profiled transcriptomic data from four highly prevalent cancers, including breast, lung, colorectal, and prostate, to uncover shared molecular signatures with diagnostic and prognostic value. Using The Cancer Genome Atlas (TCGA) datasets and a rigorous integrative workflow, we combined differential expression analysis, Elastic Net-based feature selection, and weighted gene co-expression network analysis (WGCNA) to identify 179 cross-cancer signature genes linked to clinical traits. Protein-protein interaction (PPI) analysis and Markov Cluster Algorithm

SUBMITTER: Alam A 

PROVIDER: S-EPMC12720046 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature