Abstract
BACKGROUND: Ovarian cancer (OC) is a complex disease with significant tumor heterogeneity with the worst prognosis and highest mortality among all gynecological cancers. Glycosylation is a specific post-translational modification that plays an important role in tumor progression, immune escape and metastatic spread. The aim of this work was to identify the major glycosylation-related genes (GRGs) in OC and construct an effective GRGs signature to predict prognosis and immunotherapy. METHODS: AUCell algorithm was used to identify glycosylation-related genes (GRGs) based on the scRNA-seq and bulk RNA-seq data. An effective GRGs signature was conducted using COX and LASSO regression algorithm. The texting dataset and clinical sample data were used to assessed the accuracy of GRGs signature. We evaluated the differences in immune cell infiltration, enrichment of immune checkpoints, immunotherapy response, and gene mutation status among different risk groups. Finally, RT-qPCR, Wound-healing assay, Transwell assay were performed to verify the effect of the CYBRD1 on OC. RESULTS: A total of 1187 GRGs were obtained and a GRGs signature including 16 genes was established. The OC patients were divided into high- and low- risk group based on the median riskscore and the patients in high-risk group have poor outcome. We also found that the patients in low-risk group have higher immune cell infiltration, enrichment of immune checkpoints and immunotherapy response. The results of laboratory test showed that CYBRD1 can promote the invasion, and migration of OC and is closely related to the poor prognosis of OC patients. CONCLUSIONS: Our study established a GRGs signature consisting of 16 genes based on the scRNA-seq and bulk RNA-seq data, which provides a new perspective on the prognosis prediction and treatment strategy for OC.