BACKGROUND: Clear cell renal cell carcinoma (ccRCC) is more prone to metastasis and is associated with a poorer prognosis than renal cell carcinoma (RCC). Numerous studies have reported a correlation between the expression of glycosyltransferases (GTs)-related genes and tumor. We aimed to establish a risk model based on GTs-related genes in ccRCC, and explore their correlation with tumor immune characteristics and treatment sensitivity. METHODS: The messenger ribonucleic acid (mRNA) expression data were retrieved from The Cancer Genome Atlas (TCGA). Univariate, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression were used to construct prognostic model. Kaplan-Meier survival and receiver operating characteristic (ROC) curves were used to evaluate the accuracy of the model. Calibration curves and decision curve analysis (DCA) curves were used to evaluate the model. The quantitative real-time polymerase chain reaction (qRT-PCR) was applied to detect the expression of the signature genes in human renal epithelial cells and human renal cancer cells. The ESTIMATE algorithm was used to estimate the immune scores in tumor tissues. Single-sample gene set enrichment analysis (ssGSEA) was used to evaluate the immune microenvironment. Tumor Immune Dysfunction and Exclusion (TIDE) and immune checkpoint analysis were used to assess the benefit of immunotherapy. Tumor mutational burden (TMB) analysis was used to calculate the frequency of gene mutations. Susceptibility to anticancer drugs in different risk groups was also analyzed. RESULTS: Four signature genes were identified as potential biomarkers, and the prognostic model demonstrated good predictive performance. qRT-PCR results were consistent with the actual predictions, confirming the credibility of the signature genes. The high- and low-risk groups exhibited different abundance and enrichment of immune cell infiltration. The high-risk group exhibited a higher frequency of tumor mutations than the low-risk group. TIDE and drug sensitivity analysis results demonstrated appropriate treatments for different risk groups, respectively. CONCLUSIONS: A prognostic model for ccRCC with four signature genes, was established and demonstrated high predictive performance. Four signature genes provided a foundation for studying the mechanism of GTs-related genes in ccRCC progression.