A novel glycolysis-related prognostic risk model for colorectal cancer patients based on single-cell and bulk transcriptomic data.
- Author:
Kai YAO
1
;
Jingyi XIA
1
;
Shuo ZHANG
1
;
Yun SUN
1
;
Junjie MA
2
;
Bo ZHU
3
;
Li REN
4
,
5
;
Congli ZHANG
5
,
6
Author Information
1. School of Clinical Medicine, Bengbu Medical University, Bengbu 233030, China.
2. Department of Anesthesiology First Affiliated Hospital of Bengbu Medical University, Bengbu 233004, China.
3. Department of Pathology, First Affiliated Hospital of Bengbu Medical University, Bengbu 233004, China.
4. Department of Nuclear Medicine, School of Laboratory Medicine, Bengbu Medical University, Bengbu 233030, China. *Corresponding author, E-mail: liren0812@
5. com.
6. Department of Anesthesiology, First Affiliated Hospital of Bengbu Medical University, Bengbu 233004, China. *Corresponding author, E-mail: byfy1010@
- Publication Type:Journal Article
- MeSH:
Humans;
Colorectal Neoplasms/pathology*;
Glycolysis/genetics*;
Prognosis;
Transcriptome;
Tumor Microenvironment/genetics*;
Gene Expression Profiling;
Single-Cell Analysis;
Gene Expression Regulation, Neoplastic;
Male;
Female;
Kaplan-Meier Estimate
- From:
Chinese Journal of Cellular and Molecular Immunology
2025;41(2):105-115
- CountryChina
- Language:Chinese
-
Abstract:
Objective To explore the prognostic value of glycolysis-related genes in colorectal cancer (CRC) patients and formulate a novel glycolysis-related prognostic risk model. Methods Single-cell and bulk transcriptomic data of CRC patients, along with clinical information, were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Glycolysis scores for each sample were calculated using single-sample Gene Set Enrichment Analysis (ssGSEA). Kaplan-Meier survival curves were generated to analyze the relationship between glycolysis scores and overall survival. Novel glycolysis-related subgroups were defined among the cell type with the highest glycolysis scores. Gene enrichment analysis, metabolic activity assessment, and univariate Cox regression were performed to explore the biological functions and prognostic impact of these subgroups. A prognostic risk model was built and validated based on genes significantly affecting the prognosis. Gene Set Enrichment Analysis (GSEA) was conducted to explore differences in biological processes between high- and low-risk groups. Differences in immune microenvironment and drug sensitivity between these groups were assessed using R packages. Potential targeted agents for prognostic risk genes were predicted using the Enrichr database. Results Tumor tissues showed significantly higher glycolysis scores than normal tissues, which was associated with a poor prognosis in CRC patients. The highest glycolysis score was observed in epithelial cells, within which we defined eight novel glycolysis-related cell subpopulations. Specifically, the P4HA1+ epithelial cell subpopulation was associated with a poor prognosis. Based on signature genes of this subpopulation, a six-gene prognostic risk model was formulated. GSEA revealed significant biological differences between high- and low-risk groups. Immune microenvironment analysis demonstrated that the high-risk group had increased infiltration of macrophages and tumor-associated fibroblasts, along with evident immune exclusion and suppression, while the low-risk group exhibited higher levels of B cell and T cell infiltration. Drug sensitivity analysis indicated that high-risk patients were more sensitive to Abiraterone, while low-risk patients responded to Cisplatin. Additionally, Valproic acid was predicted as a potential targeted agent. Conclusion High glycolytic activity is associated with a poor prognosis in CRC patients. The novel glycolysis-related prognostic risk model formulated in this study offers significant potential for enhancing the diagnosis and treatment of CRC.
