Multiomics analysis of ferroptosis-related molecular subtypes in muscle-invasive bladder cancer immunotherapy

BACKGROUND: The purpose of this study was to identify the ferroptosis-related molecular subtypes in muscle invasive bladder cancer (MIBC) associated with the tumor microenvironment (TME) and immunotherapy. METHODS: Expression profiles and corresponding clinical information were obtained from The Cancer Genome Atlas (TCGA) dataset and the Gene Expression Omnibus (GEO) dataset. Nonnegative matrix factorization (NMF) analysis was performed to identify two molecular subtypes based on 41 ferroptosis-related prognostic genes. The differences between the two subtypes were compared in terms of prognosis, somatic mutations, gene ontology (GO), cytokines, pathways, immune cell infiltrations, stromal/immune scores, tumor purity and response to immunotherapy. We also constructed a risk prediction model using multivariate Cox regression analysis to analyze survival data based on differentially expressed genes (DEGs) between subtypes. In combination with clinicopathological features, a nomogram was constructed to provide a more accurate prediction for overall survival (OS). RESULTS: Two molecular subtypes (C1 and C2) of MIBC were identified according to the expression of ferroptosis-related genes. The C2 subtype manifested poor prognosis, high enrichment in the cytokine-cytokine receptor interaction pathway, high abundance of immune cell infiltration, immune/stromal scores and low tumor purity. Additionally, C2 is less sensitive to immunotherapy. The risk prediction model based on five pivotal genes (SLC1A6, UPK3A, SLC19A3, CCL17 and UGT2B4) effectively predicted the prognosis of MIBC patients. CONCLUSIONS: A novel MIBC classification approach based on ferroptosis-related gene expression profiles was established to provide guidance for patients who are more sensitive to immunotherapy. A nomogram with a five-gene signature was built to predict the prognosis of MIBC patients, which would be more accurate when combined with clinical factors.