Screening of Prognostic Molecular Markers and Establishment of Prognostic Model for G-protein Coupled Receptor-Related Genes in Epithelial Ovarian Serous Cancer Based on Machine Learning Method

Abstract

Abstract Background. Ovarian cancer（OV） is one of the most common malignant tumors of the female reproductive system, five-year survival rate is in the low to mid 30% range, threatening the lives of female patients worldwide. Inefficient early diagnosis and prognostic prediction of OV leads to poor survival in most patients. G protein-coupled receptors (GPCRs) are currently the largest family of cell-surface receptors within the human genome are associated with OV. We aimed to identify G protein-coupled receptor-related genes GPCRRGs signatures and develop a novel model for predicting OV prognosis. Methods.We downloaded data from The Cancer Genome Atlas (TCGA), and Gene Expression Omnibus (GEO) databases. Prognostic GPCRRGs were screened by Least absolute shrinkage and selection operator (LASSO)-Cox regression analysis, and a prognostic model was constructed. The model’s predictive ability was evaluated by Kaplan–Meier (K-M) survival analysis. The expression levels of these GPCRRGs included in the model were examined in normal and OV cell lines using quantitative reverse transcriptase polymerase chain reaction. We finally analyzed the immunological characteristics of the prognostic diagnostic model for differences between high and low risk groups using two methods: single-sample gene-set enrichment analysis（ssGSEA）and （CIBERSORT）. Results. We screened a total of 17 GPCRRGs through TCGA and GEO databases. The K-M analysis showed that the prognostic model was able to significantly distinguish between high- and low-risk groups, corresponding to worse and better prognoses. M0 Macrophages , M2 Macrophages , Monocytes, Neutrophils, and T cells follicular helper have significant differences in the percentage of infiltration abundance among five types of cells. Immune cell infiltration, immune checkpoint expression levels, and Tumor Immune are also insightful for OV immunotherapy. Conclusion. The prognostic model constructed in this study has potential for improving our understanding of GPCRRGs and providing a new tool for prognosis and immune response prediction in patients with OV.