Unveiling tumor senescence-driven prognostic heterogeneity via MALISS in stage II/III colorectal cancer.

ABSTRACT:

Background

Prognostic heterogeneity in stage II/III colorectal cancer (CRC) challenges clinical management, yet effective prognostic stratification is still lacking. To address this, we developed a novel machine learning-based signature focused on immunosenescence.

Methods

This study developed a machine learning-based immunosenescence signature (MALISS) using transcriptomic data from 1296 patients. The final 30-gene model was derived via a CoxBoost-Lasso algorithm and validated across multiple independent cohorts.

Results

The MALISS signature effectively stratified patients into high- and low-risk groups with distinct progression-free survival. Functional analysis identified NR1D2 as a key gene promoting tumor migration through cellular senescence. The high-risk group

SUBMITTER: Liu X 

PROVIDER: S-EPMC12816352 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature