Identification of G protein-coupled receptor gene and immune infiltration analysis to construct a prognostic model for renal clear cell carcinoma patients
Authors: Wang Weizhuo , Zhang Xi , Wang Fei , Tang Zheng , Lu Kaiyu , Li Kai , Zhang Ke 
Published: August 2023

Abstract
Background
The most common type of renal cell carcinoma (RCC) is clear cell carcinoma. Existing studies have shown that G protein-coupled receptor (GPR)-related genes play an important role in the occurrence and development of renal clear cell carcinoma, as well as in the tumor microenvironment. The aim of this study is to construct a prognostic model using GPR-related genes combined with immune infiltration scoring, and to explore the relationship between the model genes and the immune microenvironment using single-cell data.
Methods
The data of renal clear cell carcinoma patients is sourced from the TCGA-KIRC database. Single-cell sequencing data from 21 patients is obtained from the GSE135337 database, and verifiable microarray data is obtained from ArrayExpress. A G protein-coupled receptor (GPR) prognostic model is constructed using univariate, Lasso, and multivariate Cox regression analyses. Additionally, the CIBERSORT algorithm is used to calculate immune infiltration scores for TCGA patients. These scores, along with patient survival and COX regression, are used to build a tumor microenvironment (TME) prognostic model. Pathway differences between different model groups are evaluated using gene set enrichment analysis (GSEA). Subsequently, a GPR and TME combined prognostic model is established by dividing patients based on median values. The reliability of the model is evaluated using ROC(receiver operating characteristic curve) curves and KM༈Kaplan-Meier༉survival analysis. Further assessment of pathway differences in the model is conducted using FGSEA and WGCNA༈Weighted correlation network analysis). After integrating single-cell data, the cellchat and monocle packages are used to evaluate the role of GPR model genes in cell communication and tumor differentiation. Finally, the prognostic features, immune characteristics, and drug sensitivity of the model are further evaluated.
Results
We downloaded all G protein-coupled receptor-related genes from the GSEA database, and 8 of them were selected to establish the prognostic model. KM curves demonstrate that both GPR scores and TME scores have good predictive value for patient survival. GSEA analysis reveals significant differences in tumor occurrence pathways between GPR scores and TME. Therefore, we constructed a combined GPR and TME prognostic model, which exhibited good performance in both TCGA data and the validation set according to ROC curves and KM curves. Subsequently, our calculations using FGSEA and WGCNA demonstrated significant pathway differences between different groups in the combined model, which were associated with classical tumor occurrence and development pathways. Furthermore, the cell communication data from single-cell data analysis showed substantial differences in intercellular communication levels between the high and low expression groups of GPR model genes. Pseudotime analysis indicated that F2RL1, PDGFRL, and GABBR1 exhibit high expression in the early stages of tumor occurrence and development. Further analysis of the TCGA immune microenvironment revealed significant differences in immune microenvironment and immune checkpoint markers between different groups. Moreover, there were notable differences in immunotherapy and chemotherapy among different groups.
Conclusion
These results indicate that the combined GPR and TME prognostic model is highly accurate and effective, and it may prove to have significant value for the treatment and prognosis of future renal clear cell carcinoma patients. F2RL1, PDGFRL, and GABBR1 could potentially serve as better prognostic biomarkers and potential therapeutic targets, guiding further clinical treatment decisions.