Development and validation of G2M signature-based prognostic model for stratifying colon cancer prognosis.

ABSTRACT:

Background

We aimed to develop a predictive model integrating G2M-related genes to enhance the prognostication of colon cancer.

Methods

Based on the data from TCGA-COAD and GEO (GSE39582, GSE17536, GSE17537), we applied Cox regression with LASSO to create a colon cancer prognostic model and developed a Nomogram for survival prediction.

Results

Our initial univariate Cox regression analysis identified 15 common prognostic genes. Subsequent LASSO modeling refined this set to nine hub genes: BRIP1, CDCA2, HMGCR, HOXC10, PSMA5, PSMD6, SIX4, SLC12A2, and SPA17. Multi-omics analysis explored their protein expression, miRNA regulation, protein interactions, genomic variations, and drug sensitivity. The G2M prognostic model effectively categorized colon cancer patients into h

SUBMITTER: Song X 

PROVIDER: S-EPMC12336104 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature