An Immune-Related Clinical-Gene-Risk-Score (CGRS) prognosis Model for Bladder Cancer

Abstract

Background Immunotherapy for bladder cancer is prevalent, while efficacy is uncertain. To predict the outcomes of immunotherapy in bladder cancer patients, an immune-related prognosis model was conducted in this study. Methods Single sample Gene Set Enrichment Analysis was used to delineate the landscape of the tumor immune microenvironment based on two independent public databases. Hierarchical clustering analysis and t-distributed Stochastic Neighbor Embedding cluster map were used to classify the bladder cancers in the training cohort as well as the validating cohort into the immune high and immune low subtypes. Differentially expression analysis and The least absolute shrinkage and selection operator regression analysis identified 23 differentially expressed immune-related genes between the two subtype patients to build a prognostic gene risk score model. Further combining the model with other clinicopathologic signatures, a Clinical-Gene-Risk-Score (CGRS) prognosis model was developed. Results Validations of the two models in independent cohorts revealed good accuracy. Conclusions Our model could provide prognostic indicators and potential immunotherapeutic targets for bladder cancers.