Work

Constructing a Prognostic Model for Clear Cell Renal Cell Carcinoma Based on Glycosyltransferase Gene and Verification of Key Gene Identification

Year: 2025

Type: article

Abstract: Clear cell renal cell carcinoma (ccRCC) is the most common and aggressive subtype of kidney cancer. This study aimed to construct a prognostic model for ccRCC based on glycosyltransferase genes, which play important roles in cell processes like proliferation, apoptosis. Glycosyltransferase genes were collected from four public databases and analyzed using RNA-seq data with clinical information from three ccRCC datasets. Prognostic models were constructed using eight machine learning algorithms, generating a total of 117 combinatorial algorithm models, and the StepCox[forward]+Ridge model with the highest predictive accuracy (C-index = 0.753) which selected and named the Glycosyltransferases Risk Score (GTRS) model. The GTRS effectively stratified patients into high- and low-risk groups with significantly different overall survival and maintained robust performance across TCGA, CPTAC, and E-MTAB1980 cohorts (AUC > 0.75). High-risk patients exhibited higher tumor mutational burden, immunosuppressive microenvironment, and poorer response to immunotherapy. TYMP and GCNT4 were experimentally validated as key genes, functioning as oncogenic and tumor-suppressive factors. In conclusion, GTRS serves as a reliable prognostic tool for ccRCC and provides mechanistic insights into glycosylation-related tumor progression.

Source: International Journal of Molecular Sciences

DOI: 10.3390/ijms262010182