A machine learning-based prognostic model integrating mRNA stemness index, hypoxia, and glycolysis‑related biomarkers for colorectal cancer.

ABSTRACT:

Background

Cancer stemness, hypoxia, and glycolysis collectively influence colorectal cancer (CRC) progression. However, the intricate relationships among these factors remain incompletely understood.

Methods

This study (1) explored hypoxia and glycolysis-related genes (HGRGs) in CRC by mRNA stemness index (mRNAsi), analyzed the gene expression profiles from Gene Expression Omnibus and The Cancer Genome Atlas (TCGA) databases, (2) established a Cox-prognostic model based on single-sample gene set enrichment analysis, differentially expressed gene analysis, weighted gene co-expression network analysis, and Least Absolute Shrinkage and Selection Operator (LASSO) and Cox regression analyses, and (3) assessed the predictive accuracy of the model. Decision curve analysis (DCA) wa

SUBMITTER: Liu D 

PROVIDER: S-EPMC12452079 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature