Identification of GCNT3 as a glycometabolism-associated biomarker in endometrial cancer

Abstract

Endometrial carcinoma (EC) incidence is increasing, with diabetes mellitus (DM) elevating EC risk. This study investigates the glycometabolism-associated gene GCNT3 in EC. We systematically integrated pancreatic tissue DM datasets (GSE25724, GSE76896, and GSE95849) with RNA sequencing data from the TCGA-UCEC cohort. Our analytical strategy incorporated a comprehensive bioinformatics workflow, primarily including differential gene expression profiling, survival outcome modeling, functional enrichment analysis, and detailed immune infiltration assessment. Three machine-learning algorithms, including LASSO regression, support vector machine-recursive feature elimination (SVM-RFE), and Random Forest, were applied for feature selection. To reduce overestimation from using the same discovery cohort alone, the EC cohort was randomly divided into a training set and a test set at a ratio of 7:3. Feature selection was performed in the training set, with 5-fold cross-validation used for LASSO and SVM-RFE, and model discrimination and decision-curve performance were subsequently evaluated in the independent test set. Finally, clinical validation was performed by immunohistochemical examination of 80 EC tissues and 40 histologically confirmed normal endometrial control tissues to validate GCNT3 expression differences and clarify its potential biological significance in EC. Molecular docking studies were then conducted to explore potential binding interactions between GCNT3 and the selected candidate drugs Afatinib and Selumetinib. GCNT3 was upregulated in EC. In unadjusted Kaplan-Meier analysis, higher GCNT3 expression was associated with improved overall survival and disease-free survival. In multivariable Cox regression analysis adjusting for age, tumor grade, and tumor stage, GCNT3 remained an independent prognostic factor in EC. High GCNT3 expression was also associated with lower tumor grade and earlier stage, together with distinct immune infiltration patterns. High GCNT3 expression was associated with lower predicted IC50 values for Afatinib and Selumetinib. Molecular docking suggested potential binding interactions between GCNT3 and these agents, supporting a possible association between GCNT3 expression and differential drug responsiveness. GCNT3 is a potential biomarker for EC prognosis and therapy, showing consistent associations with glycometabolic signatures and the immune microenvironment.