Identification of glycosyltransferase-related genes signature and integrative analyses in patients with ovarian cancer

Background: Glycosyltransferases (GT) play a crucial role in glycosylation reactions, and aberrant expression of glycosyltransferase-related genes (GTs) leads to abnormal glycosylation, which is associated with tumor progression. However, the prognostic value of aberrant expression of GTs in ovarian cancer (OC) and the correlation between GTs and tumor microenvironment (TME) remain unknown. Methods: TCGA and GSE53963 databases were used to obtain data on OC patient samples. The association of GTs with OC was analyzed. Molecular subtypes were identified by consensus unsupervised clustering, followed by immune infiltration and functional enrichment analyses. Survival analysis was performed using Kaplan-Meier curves and log -rank tests. Least Absolute Shrinkage and Selection Operator (LASSO) and multifactorial cox regression were used to screen for signature genes associated with OC and used to establish prognostic models. Result: OC patients were categorized into 5 GTs clusters using consensus unsupervised cluster analysis. Clusters D and E showed significant differences between survival, signaling pathways and immune infiltration. Then, a risk model was developed based on the 12 signature genes, which provides a more accurate evaluation of the prognosis of OC patients. We categorized patients into high -risk and low-risk groups based on the risk score and found that the survival of patients in the high -risk group was significantly lower than that in the low-risk group. Moreover, the risk score was significantly correlated with tumor microenvironment, immune infiltration, and chemotherapy sensitivity. Conclusion: Overall, we performed a comprehensive analysis of GTs in OC patients and developed a risk model for OC. Our findings will provide a new insight to OC prognosis and treatment.