Identification of an IRGP Signature to Predict Prognosis and Immunotherapeutic Efficiency in Bladder Cancer

BACKGROUND: Identify immune-related gene pairs (IRGPs) signature related to the prognosis and immunotherapeutic efficiency for bladder cancer (BLCA) patients. MATERIALS AND METHODS: One RNA-seq dataset (The Cancer Genome Atlas Program) and two microarray datasets (GSE13507 and GSE31684) were included in this study. We defined these cohorts as training set to construct IRGPs and one immunotherapy microarray dataset as validation set. Identifying BLCA subclasses based on IRGPs by consensus clustering. The Lasso penalized Cox proportional hazards regression model was used to construct prognostic signature and potential molecular mechanisms were analyzed. RESULTS: This signature can accurately predict the overall survival of BLCA patients and was verified in the immunotherapy validation set. IRGP-signatures can be used as independent prognostic risk factor in various clinical subgroups. Use the CIBERSORT algorithm to assess the abundance of infiltrating immune cells in each sample, and combine the results of the gene set enrichment analysis of a single sample to explore the differences in the immune microenvironment between IRPG signature groups. According to the results of GSVA, GSEA, and CIBERSORT algorithm, we found that IRGP is strikingly positive correlated with tumor microenvironment (TME) stromal cells infiltration, indicating that the poor prognosis and immunotherapy might be caused partly by enrichment of stromal cells. Finally, the results from the TIDE analysis revealed that IRGP could efficiently predict the response of immunotherapy in BLCA. CONCLUSION: The novel IRGP signature has a significant prognostic value for BLCA patients might facilitate personalized for immunotherapy.