Machine learning-based glycolysis-associated molecular classification reveals differences in prognosis, TME, and immunotherapy for colorectal cancer patients

Zhenling Wang; Yu Shao; Hongqiang Zhang; Yunfei Lu; Yang Chen; Hengyang Shen; Changzhi Huang; Jingyu Wu; Zan Fu

doi:10.3389/fimmu.2023.1181985

Machine learning-based glycolysis-associated molecular classification reveals differences in prognosis, TME, and immunotherapy for colorectal cancer patients

Front Immunol. 2023 May 5:14:1181985. doi: 10.3389/fimmu.2023.1181985. eCollection 2023.

Authors

Zhenling Wang^{1

2}, Yu Shao^{1

2}, Hongqiang Zhang^{1

2}, Yunfei Lu^{1

2}, Yang Chen^{1

2}, Hengyang Shen^{1

2}, Changzhi Huang^{1

2}, Jingyu Wu^{1

2}, Zan Fu^{1

2}

Affiliations

¹ Department of General Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China.
² The First School of Clinical Medicine, Nanjing Medical University, Nanjing, Jiangsu, China.

Abstract

Background: Aerobic glycolysis is a process that metabolizes glucose under aerobic conditions, finally producing pyruvate, lactic acid, and ATP for tumor cells. Nevertheless, the overall significance of glycolysis-related genes in colorectal cancer and how they affect the immune microenvironment have not been investigated.

Methods: By combining the transcriptome and single-cell analysis, we summarize the various expression patterns of glycolysis-related genes in colorectal cancer. Three glycolysis-associated clusters (GAC) were identified with distinct clinical, genomic, and tumor microenvironment (TME). By mapping GAC to single-cell RNA sequencing analysis (scRNA-seq), we next discovered that the immune infiltration profile of GACs was similar to that of bulk RNA sequencing analysis (bulk RNA-seq). In order to determine the kind of GAC for each sample, we developed the GAC predictor using markers of single cells and GACs that were most pertinent to clinical prognostic indications. Additionally, potential drugs for each GAC were discovered using different algorithms.

Results: GAC1 was comparable to the immune-desert type, with a low mutation probability and a relatively general prognosis; GAC2 was more likely to be immune-inflamed/excluded, with more immunosuppressive cells and stromal components, which also carried the risk of the poorest prognosis; Similar to the immune-activated type, GAC3 had a high mutation rate, more active immune cells, and excellent therapeutic potential.

Conclusion: In conclusion, we combined transcriptome and single-cell data to identify new molecular subtypes using glycolysis-related genes in colorectal cancer based on machine-learning methods, which provided therapeutic direction for colorectal patients.

Keywords: colorectal cancer; glycolysis; machine learning; molecular subtypes; single-cell analysis; tumor immune infiltration.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Colorectal Neoplasms* / genetics
Colorectal Neoplasms* / therapy
Glycolysis / genetics
Humans
Immunotherapy
Machine Learning
Prognosis
Tumor Microenvironment* / genetics

Grants and funding

This work was supported by the National Natural Science Foundation of China (81470881 to ZF, 82172956 to ZF) and Jiangsu Commission of Health (LGY2017031 and BRA2015473 to ZF).