Machine learning-based integration of tumor deposit molecular signatures improves prognostic stratification in colon adenocarcinoma.

ABSTRACT:

Background

Colon adenocarcinoma (COAD) remains a leading cause of cancer-related mortality worldwide. Although tumor deposits (TDs) are established prognostic indicators, their molecular characteristics and potential for improving risk stratification remain unexplored.

Methods

We performed an integrative analysis of transcriptomic and clinical data from TCGA and GEO databases to identify TD-associated molecular signatures. A hybrid ML framework combining random survival forest and stepwise Cox regression was developed to construct a risk stratification model. Model performance was validated through survival analysis, time-dependent ROC curves, and multivariate analyses. Gene set enrichment analysis explored underlying mechanisms and therapeutic implications.

Results

T

SUBMITTER: Wu J 

PROVIDER: S-EPMC12789243 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature