The combined signatures of G protein-coupled receptor family and immune landscape provide a prognostic and therapeutic biomarker in endometrial carcinoma.

G protein-coupled receptors (GPRs) are one of the largest surface receptor superfamilies, and many of them play essential roles in biological processes, including immune responses. In this study, we aim to construct a GPR- and tumor immune environment (TME-i)-associated risk signature to predict the prognosis of patients with endometrial carcinoma (EC). The GPR score was generated by applying univariate Cox regression and the Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression in succession. This involved identifying the differentially expressed genes (DEGs) in the Cancer Genome Atlas-Uterine Corpus Endometrioid Carcinoma (TCGA-UCEC) cohort. Simultaneously, the CIBERSORT algorithm was applied to identify the protective immune cells for TME score construction. Subsequently, we combined the GPR and TME scores to establish a GPR-TME classifier for conducting clinical prognosis assessments. Various functional annotation algorithms were used to conduct biological process analysis distinguished by GPR-TME subgroups. Furthermore, weighted correlation network analysis (WGCNA) was applied to depict the tumor somatic mutations landscapes. Finally, we compared the immune-related molecules between GPR-TME subgroups and resorted to the Tumor Immune Dysfunction and Exclusion (TIDE) for immunotherapy response prediction. The mRNA and protein expression of GPR-related gene P2RY14 were, respectively, validated by RT-PCR in clinical samples and HPA database. To conclude, our GPR-TME classifier may aid in predicting the EC patients' prognosis and immunotherapy responses.