Integration of multi-omics and clinical treatment data reveals bladder cancer therapeutic vulnerability gene combinations and prognostic risks

Bladder cancer (BCa) is a common malignancy of the urinary tract. Due to the high heterogeneity of BCa, patients have poor prognosis and treatment outcomes. Immunotherapy has changed the clinical treatment landscape for many advanced malignancies, opening new avenues for the precise treatment of malignancies. However, effective predictors and models to guide clinical treatment and predict immunotherapeutic outcomes are still lacking. We downloaded BCa sample data from The Cancer Genome Atlas to identify anti-PD-L1 immunotherapy-related genes through an immunotherapy dataset and used machine learning algorithms to build a new PD-L1 multidimensional regulatory index (PMRI) based on these genes. PMRI-related column-line graphs were constructed to provide quantitative tools for clinical practice. We analyzed the clinical characteristics, tumor immune microenvironment, chemotherapy response, and immunotherapy response of patients based on PMRI system. Further, we performed function validation of classical PMRI genes and their correlation with PD-L1 in BCa cells and screening of potential small-molecule drugs targeting PMRI core target proteins through molecular docking. PMRI, which consists of four anti-PD-L1 immunotherapy-associated genes ( , and ), is a reliable predictor of survival in patients with BCa and has been validated using multiple external datasets. We found higher levels of immune cell infiltration and better responses to immunotherapy and cisplatin chemotherapy in the high PMRI group than in the low PMRI group, which can also be used to predict immune efficacy in a variety of solid tumors other than BCa. Knockdown of inhibited BCa cell proliferation and migration, and was positively correlated with PD-L1 expression. We performed molecular docking prediction for each of the core proteins comprising PMRI and identified 16 small-molecule drugs with the highest affinity to the target proteins. Our PD-L1 multidimensional expression regulation model based on anti-PD-L1 immunotherapy-related genes can accurately assess the prognosis of patients with BCa and identify patient populations that will benefit from immunotherapy, providing a new tool for the clinical management of intermediate and advanced BCa.