Predictive Significance of Glycosyltransferase-Related lncRNAs in Endometrial Cancer: A Comprehensive Analysis and...

Glycosylation is a crucial post-translational modification that regulates cellular functions and the surrounding microenvironment. The abnormal expression of glycosyltransferases, essential enzymes in this process, has been linked to tumor development. In addition, long noncoding RNAs (lncRNAs) have been shown to exert a multifaceted regulatory influence on gene expression. Nevertheless, research on glycosyltransferase-related lncRNAs (GTRLs) in endometrial cancer (EC) is still unclear. In this study, we constructed a novel prognostic model based on GTRLs using univariate Cox regression, Lasso, and multivariate stepwise Cox regression analysis. Our findings identified four GTRLs that were significantly associated with the prognosis of EC patients. The Kaplan-Meier (KM) survival analysis revealed a notable reduction in overall survival (OS) among high-risk populations. Furthermore, independent prognostic assessments, receiver operating characteristic (ROC) curves and nomograms, demonstrated that risk scores derived from these GTRLs outperformed other clinical variables within the TCGA-UCEC clinical data set in predicting patient prognosis. And the low-risk cohort exhibited increased immune infiltration and decreased tumor purity. Additionally, significant differences in tumor mutation profiles were observed, with the tumor mutation burden (TMB) being higher in the low-risk cohort, suggesting a potentially better response to immunotherapy. Different patient subgroups also displayed varying sensitivities to several chemotherapeutic agents. Finally, the model's validity was confirmed through clinical specimens and in vitro experiments, which demonstrated that AC090617.5 and AP001107.9 can influence the proliferation, migratory functions, and cisplatin sensitivity of EC. Therefore, the prognostic features can effectively predict the prognosis of EC and guide therapeutic decision-making.