Construction and verification of a prognostic risk model based on immunogenomic landscape analysis of bladder caner

Abstract

This study was designed to construct a prognostic risk model to predict prognosis and immunotherapy response of bladder cancer (BCa) patinets. 350 differential expressed immune-related genes (DEIRGs) were obtained according to the transcriptome profiling and immune-related genes from the Cancer Genome Atlas (TCGA) database and ImmPort database, respectively. A prognostic risk model was constructed based on 15 hub genes through univariate, multivariate, and LASSO Cox regression analyses. The area under the receiver operating characteristic (ROC) curve was 0.743, indicating the superiority of the model. The scatter plot showed that as the risk score increased, the overall survival decreased significantly. In addition, all results were internally verified by the TCGA cohort. The model showed that the higher the grade, clinical stage, and TNM stage of BCa, the higher the risk score of patients. The tumor mutation burden of the low-risk group was generally higher than that of the high-risk group. Immune cell infiltration analysis showed that CD8 T cells, naive CD4 T cells, follicular helper T cells and M0 Macrophage were significantly different between the two groups. Several key immune checkpoint genes were found to be significantly different between the two groups, such as CTLA4, PD-L1, CD47, CD276, CXCL8, and HAVCR2/TIM3. Finally, the analysis of immunotherapy revealed that the efficacy of CTLA4 or PD1 blockers alone was better in the low-risk group than in the high-risk group. Taken together, we developed and validated a prognostic risk model based on 15 hub genes, which performed well in predicting prognosis and immunotherapy response of BCa patients.