Integrative meta-analysis and experimental validation reveal the oncogenic role of SACS and its therapeutic targeting potential in colorectal cancer.

ABSTRACT: This study aims to identify core genes closely associated with the diagnosis and prognosis of colorectal cancer (CRC) using transcriptome-based meta-analysis approach and machine learning algorithms. Nine CRC datasets from the GEO database were integrated for differential gene expression analysis and WGCNA to identify key genes. Ninety-six combinations of machine learning algorithms were employed to further refine the selection of core genes and validate their diagnostic performance. Functional enrichment, molecular pathways, and associations with the immune microenvironment of core genes were analyzed using GSEA, CIBERSORT, and ssGSEA. Drug sensitivity predictions were performed to evaluate the impact of core genes on CRC drug response, and molecular docking simulations were used to ident

SUBMITTER: Chen C 

PROVIDER: S-EPMC12606282 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature