Abstract
BACKGROUND: Disulfidoptosis is an emerging form of regulated cell death; however, the roles of its associated long non-coding RNAs (dr-lncRNAs) in bladder cancer (BLCA) remain poorly characterized. By leveraging the most comprehensive curated dataset of disulfidoptosis-related genes to date, we systematically developed and validated a novel dr-lncRNA signature that elucidates the prognostic significance and immune microenvironmental dynamics in BLCA. METHODS: The Cancer Genome Atlas (TCGA) database was utilized to extract significant clinical and RNA sequencing data of BLCA patients. Cox and Lasso regression with several variables was used to create a risk model. ROC, Kaplan-Meier, and nomogram analyses were carefully reviewed for validity. The validated study evaluated intricate interactions between functional enrichment, immune cell infiltration, cancer mutation load, and treatment sensitivity. Unsupervised consensus clustering identified subgroup patterns that reflected immune system alterations, medication susceptibility, and prognosis. RESULTS: Nine lncRNAs significantly correlated with prognosis were collectively identified, subsequently forming the basis for constructing a risk model consisting of seven lncRNAs. The model exhibited significant superiority in predicting patient outcomes, effectively distinguishing between high-risk from low-risk individuals. Functional enrichment analysis uncovered their potential involvement in immune-related biological pathways. Patients in the high-risk group exhibited higher tumor mutation burdens, more active immune functions and a higher sensitivity to chemotherapeutic drugs. Variations among BLCA subgroups were identified by consensus cluster analysis, including clinical characteristics, prognosis, lncRNA expression, immune cell infiltration, and immune checkpoint profiles. CONCLUSION: The dr-lncRNAs-based risk model presents a promising tool for predicting prognosis and guiding personalized immunotherapy and treatment strategies in BLCA patients.