ArticleFrontiers in immunology2025
Unveiling tumor senescence-driven prognostic heterogeneity via MALISS in stage II/III colorectal cancer.
Article in Frontiers in immunology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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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 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. Conclusion: 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.
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