Objective  To establish a carbohydrate metabolism-related genes (CRGs) prognostic model for clear cell renal cell carcinoma (ccRCC) and investigate its clinical value.

Methods ccRCC mRNA expression data were sourced from The Cancer Genome Atlas (TCGA) database. CRGs were retrieved from the MSigDB and KEGG databases. A prognostic model based on CRGs was constructed using the LASSO linear regression model, and the risk score (RS) was calculated. Patients were assigned into high- and low-risk groups according to the median RS. Differences in survival, immune infiltration, mutation, and immune response between the two groups were analyzed using Kaplan-Meier curves and bioinformatics methods. Constructing a nomogram based on the RS and clinical features and validating its accuracy of prognostic predictions. The expression of CRGs in the ccRCC samples was detected using RT-qPCR.

Results A total of eight key genes were utilized to construct a prognostic risk model for ccRCC. Survival analysis revealed that patients in the low-risk group had a better prognosis (P<0.001). Bioinformatics analysis showed that the RS correlated with immune cell infiltration, mutation, and immune responses. The nomogram based on the RS and clinical features demonstrated a strong predictive ability for prognosis. In vitro experiments confirmed notable differences in the expression of the eight CRGs between ccRCC and adjacent non-malignant tissues.

Conclusions  A prognostic model based on CRGs can effectively predict the prognosis of patients with ccRCC.