A novel TRP channel-related prognostic model of glioma based on transcriptomics and single cell sequencing analysis

Abstract

Abstract Background Glioma is the most malignant intracranial tumor. Transient receptor potential (TRP) channel family has been found to be involved in malignant progression of many tumors. However, the relationship between TRP channel-related genes (TCRGs) and glioma remains unclear. Methods The TCGA-glioma, CGGA_325, GSE108474 and GSE16011 glioma cohorts were downloaded from the public database. Differential analysis, univariate Cox regression analysis and consistent clustering were performed for 120 TCRGs, and differences in immune microenvironment were analyzed. Lasso Cox regression analysis was used to construct a prognostic model, and the patients were divided into high- and low- risk groups. Results Thirty differentially expressed prognostic TCRGs were identified and patients were divided into three subtypes. Lasso Cox regression analysis showed that 10 genes were included in the prognostic model (TRPM6, TRPV3, BDKRB1, HTR2A, P2RY2, PLA2G4D, CAMK2G, MAPK13, ADCY5 and PRKCB). Kaplan-Meier survival analysis showed that patients of the high-risk group had lower survival. Finally, we analyzed five single-cell sequencing data sets (GSE103224, GSE131928, GSE138794, GSE148842 and GSE162631). MAPK13, CAMK2G and PRKCB were significantly expressed in exhausted CD8 + T cells, suggesting that they were closely related to anti-tumor immunity. Conclusion Based on the expression of TCRGs, we conducted the new subtype classification and a prognostic model for glioma, and is expected to provide theoretical basis for the development of new targets.