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

Journal: Advanced science (Weinheim, Baden-Wurttemberg, Germany)
Published Date: Jul 17, 2025

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 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.

Authors

  • Hao Chen
    The First School of Medicine, Wenzhou Medical University, Wenzhou, China.
  • Ze Wang
    School of Traditional Chinese Materia Medica, Tianjin University of Traditional Chinese Medicine, 312 Anshanwest Road, Nankai District, Tianjin 300193, China.
  • Chenglong Sun
    Key Laboratory for Applied Technology of Sophisticated Analytical Instruments of Shandong Province, Shandong Analysis and Test Center, Qilu University of Technology, Shandong Academy of Sciences, Shandong, 250014, China.
  • Yang Zhong
    Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, 200032, China.
  • Yuan Liu
    Department of General Surgery, Wuxi People's Hospital Affiliated to Nanjing Medical University, Wuxi, China.
  • Yikun Li
    Department of Radiation Oncology, Jinling Hospital, Nanjing, Jiangsu, 210002, China.
  • Tongchao Zhang
    Clinical Epidemiology Unit, Clinical Research Center of Shandong University, Qilu Hospital of Shandong University, Jinan, 250012, China.
  • Yuan Zhang
    Department of Urology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • Xingyu Zhu
    West China School of Medicine, Sichuan University, Chengdu 610041, China.
  • Leping Li
    Biostatistics and Computational Biology Branch, National Institute of Environmental Health Sciences, Durham, North Carolina, United States of America.
  • Feifei Teng
    Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, 250117, China.
  • Ming Lu
    Clinical Epidemiology Unit, Clinical Research Center of Shandong University, Qilu Hospital of Shandong University, Jinan, 250012, China.
  • Wei Chong
    Department of Emergency, The First Hospital of China Medical University, Shenyang, China.

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