Muscle-invasive bladder cancer subtyping and prognosis: a multi-omics study based on a ferroptosis and cuproptosis-related signature

Abstract Background: Muscle-invasive bladder cancer (MIBC), is a common, aggressive malignancy with significant mortality. Recent advancements have identified ferroptosis and cuproptosis as potential therapeutic targets. However, their interactions and impacts on MIBC overall survival (OS) and treatment outcomes are unclear. This study uses multi-omics approaches to explore these roles and their therapeutic potential, aiming to improve MIBC understanding and management. Methods: Using Cox’s regression and multiple machine learning (ML) techniques, a nomogram-based model incorporating ferroptosis and cuproptosis-related signature was developed based on the data of MIBC patients from The Cancer Genome Atlas (TCGA). Two Gene Expression Omnibus (GEO) datasets (GSE13507 and GSE32894) were utilised to externally validate this signature. Biological experimental validation was also performed in human bladder cancer cell lines. Results: The study identified a robust ferroptosis and cuproptosis-associated gene signature that significantly stratifies MIBC patients into distinct prognostic categories. This signature was not only predictive of overall survival but also correlated with distinct molecular subtypes of MIBC. Validation in independent datasets confirmed the prognostic value of the ferroptosis and cuproptosis-related signature and nomogram-based model. Conclusions: The established bioinformatics model based on ferroptosis and cuproptosis markers offers a novel framework for MIBC subtyping and could potentially guide personalised therapeutic strategies.