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

## TL;DR

A new machine learning model called MALISS helps predict outcomes in colorectal cancer patients by analyzing tumor senescence and immune aging.

## Contribution

A novel machine learning-based immunosenescence signature (MALISS) for risk stratification in stage II/III colorectal cancer.

## Key findings

- MALISS stratified patients into high- and low-risk groups with distinct progression-free survival.
- NR1D2 was identified as a key gene promoting tumor migration through cellular senescence.
- The high-risk group showed a unique mutational landscape and altered tumor microenvironment.

## Abstract

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.

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.

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 was characterized by a unique mutational landscape, an altered tumor microenvironment, and differential drug sensitivity. Furthermore, a prognostic nomogram integrating MALISS with clinical biomarkers demonstrated improved predictive performance.

MALISS serves as a robust tool for risk stratification and provides valuable insights into tumor biology, offering a promising approach to address prognostic heterogeneity in stage II/III CRC.