# A comprehensive risk model of disulfidoptosis-related lncRNAs predicts prognosis and therapeutic implications in bladder cancer

## TL;DR

This study identifies a new risk model based on seven long non-coding RNAs linked to disulfidoptosis, which helps predict bladder cancer prognosis and treatment response.

## Contribution

A novel dr-lncRNA risk model is developed for bladder cancer prognosis and immune-related treatment insights.

## Key findings

- A seven-lncRNA risk model effectively predicts patient survival in bladder cancer.
- High-risk patients show higher tumor mutation burdens and immune activity.
- Consensus clustering reveals distinct subgroups with unique clinical and immune features.

## Abstract

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.

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.

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.

The dr-lncRNAs-based risk model presents a promising tool for predicting prognosis and guiding personalized immunotherapy and treatment strategies in BLCA patients.

•Identified 7 disulfidoptosis-related lncRNAs predictive of prognosis in BLCA.•Developed a robust risk model surpassing traditional factors in survival prediction.•High-risk patients exhibit increased tumor mutation burden and immune activity.•Consensus clustering identifies subgroups with distinct clinical and immune traits.

Identified 7 disulfidoptosis-related lncRNAs predictive of prognosis in BLCA.

Developed a robust risk model surpassing traditional factors in survival prediction.

High-risk patients exhibit increased tumor mutation burden and immune activity.

Consensus clustering identifies subgroups with distinct clinical and immune traits.