Machine Learning Prediction Models Based on Hub Genes Related to Immune Phenotypes in Muscle-Invasive Bladder Cancer Treated With Immune Checkpoint Blockade

Abstract

Background: Bladder cancer is one of the most frequent cancer in the world. Muscle-invasive bladder cancer (MIBC) is a more aggressive subtype with higher morbidity and mortality. ICB therapy has shown potential for treating MIBC, but is limited due to the lack of predictive biomarkers.Methods: 1601 MIBC transcriptomic profiles were obtained from 10 datasets. Unsupervised clustering of immune phenotypes in MIBC were performed based on immune-related signature genes. We analyzed the characteristics including immune microenvironments, metabolic pathways, and survival rates in different phenotypes. Multi-omic analysis and WGCNA were performed to identify hub genes distinguishing phenotypes related to prognosis. A model was established and CART was employed to predict the response of patients treated with ICB.Results: Of various immune phenotypes, cluster 3C was the most “inflamed” subcluster with the best prognosis, while cluster 1A was associated with “non-inflammation” and worse prognosis. WGCNA analysis identified 5 hub genes related to the survival rate of patients, IFNG, CXCR6, IL2RB, LCK, and PSMB10, which were utilized for prognostic score model and decision tree analysis. The areas under the curve (AUC) of the ROC curves predicting 5-year endpoint generated from the risk-based prediction model were 0.652. The mean accuracy, sensitivity, and specificity of CART for predicting stable disease/progressive disease were 70.1%, 70.0% and 71.7%.Conclusions: The 5 hub genes and generated models showed the potential for predicting the prognosis for patients receiving ICB therapy. The molecular mechanisms regulating the expression of the hub genes require further studies in the future.