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

作者：Hao Chen, Ze Wang, Chenglong Sun, Yang Zhong, Yuan Liu, Yikun Li, Tongchao Zhang, Yuan Zhang, Xingyu Zhu, Leping Li, Feifei Teng, Ming‐Chi Lu, Wei Chong · 发表于：Advanced Science · 年份：2025 · DOI：10.1002/advs.202501333 · 被引用次数：5 · 研究领域：Cancer, Hypoxia, and Metabolism、Colorectal Cancer Treatments and Studies、Cancer, Lipids, and Metabolism

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 prognosis, activated inflammation, and enriched carbohydrate, glycosaminoglycan, and lipid metabolism, with therapeutic potential for IGF-1R and Wnt/β-catenin inhibitor. In contrast, the low-risk group displayed a TGF-β pathway inactivating mutation and enriched in nucleotides, cofactors, and amino acids metabolism. Metabolite profiling in the in-house SDCRC dataset validated the distinct metabolic alterations between the two groups. These findings indicate that MALMPS is a valuable instrument for predicting the recurrence risk of stage II/III colorectal cancer, particularly for identifying individuals at high risk.