MALMPS: A Machine Learning-Based Metabolic Gene Prognostic Signature for Stratifying Clinical Outcomes and Molecular Heterogeneity in Stage II/III Colorectal Cancer.

ABSTRACT: Colorectal cancer (CRC) is a frequently lethal disease, with stage II/III CRC accounting for ≈70%. Metabolic reprogramming plays a pivotal role in deciphering cancer heterogeneity and progression. Here, 9 datasets and 83 machine learning algorithm combinations are leveraged‌ to develop the Machine Learning-based Metabolic gene Prognostic Signature (MALMPS) model. The MALMPS model outperformed traditional clinical traits and molecular features in predicting prognosis for stage II/III CRC patients across training and validation datasets. COX7B, a key gene in MALMPS, is shown to promote CRC malignancy through multi-omics analysis and in vitro assays. CRC patients are stratified into high- and low-risk groups based on the median cutoff of MALMPS. Notably, the high-risk subgroup exhibited poor

SUBMITTER: Chen H 

PROVIDER: S-EPMC12499476 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature