膀胱癌（BLCA）是一种普遍存在的侵袭性癌症，死亡率高，预后不良。目前，关于二硫下垂相关的长非编码RNA（DRL）在BLCA中的作用的研究还很有限。本研究旨在构建基于DRL的预后模型，以提高患者生存预测的准确性，并确定BLCA管理中治疗干预的新靶点。BLCA患者的转录组和临床数据集来自癌症基因组图谱。使用多元 Cox 回归、最小绝对收缩和选择算子技术，开发了由 DRL 定义的风险预后特征。通过 Kaplan-Meier 生存图、受试者工作特征曲线、一致性指数和主成分分析来评估模型的准确性和预后相关性。进行功能和途径富集分析，包括基因本体论、京都基因和基因组百科全书以及基因集富集分析，以阐明潜在的生物过程。使用 CIBERSORT 算法对免疫细胞浸润进行量化。通过单样本基因集富集分析评估不同风险人群免疫细胞的差异和功能。利用肿瘤免疫功能障碍和排除预测因子以及肿瘤突变负荷（TMB）评估来评估对免疫治疗产生反应的可能性。使用癌症数据库药物敏感性基因组学进行药物敏感性预测。强大的 8-DRL 风险预后模型，包括 LINC00513、SMARCA5-AS1、MIR4435-2HG、MIR4713HG、AL122035.1、AL359762.3、AC006160.1 和AL590428.1被确定为独立的预后指标。该模型对 BLCA 患者的总生存期表现出强大的预测能力，揭示了高风险组和低风险组之间在肿瘤微环境、免疫浸润、免疫功能、TMB、肿瘤免疫功能障碍和排除评分以及药物敏感性方面的显着差异。引入了 8 个 DRL 的创新预后特征，为膀胱癌提供了有价值的预后工具和潜在的治疗靶点。这些发现对 TMB、免疫格局以及患者对免疫疗法和靶向治疗的反应具有重大影响。版权所有 © 2024 作者。由 Wolters Kluwer Health, Inc. 出版

Bladder cancer (BLCA) is a prevalent and aggressive cancer associated with high mortality and poor prognosis. Currently, studies on the role of disulfidptosis-related long non-coding RNAs (DRLs) in BLCA are limited. This study aims to construct a prognostic model based on DRLs to improve the accuracy of survival predictions for patients and identify novel targets for therapeutic intervention in BLCA management.Transcriptomic and clinical datasets for patients with BLCA were obtained from The Cancer Genome Atlas. Using multivariate Cox regression and least absolute shrinkage and selection operator techniques, a risk prognostic signature defined by DRLs was developed. The model's accuracy and prognostic relevance were assessed through Kaplan-Meier survival plots, receiver operating characteristic curves, concordance index, and principal component analysis. Functional and pathway enrichment analyses, including Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, and Gene Set Enrichment Analysis, were conducted to elucidate the underlying biological processes. Immune cell infiltration was quantified using the CIBERSORT algorithm. Differences and functions of immune cells in different risk groups were evaluated through single-sample Gene Set Enrichment Analysis. The Tumor Immune Dysfunction and Exclusion predictor and tumor mutational burden (TMB) assessments were utilized to gauge the likelihood of response to immunotherapy. Drug sensitivity predictions were made using the Genomics of Drug Sensitivity in Cancer database.A robust 8-DRL risk prognostic model, comprising LINC00513, SMARCA5-AS1, MIR4435-2HG, MIR4713HG, AL122035.1, AL359762.3, AC006160.1, and AL590428.1, was identified as an independent prognostic indicator. This model demonstrated strong predictive power for overall survival in patients with BLCA, revealing significant disparities between high- and low-risk groups regarding tumor microenvironment, immune infiltration, immune functions, TMB, Tumor Immune Dysfunction and Exclusion scores, and drug susceptibility.This study introduces an innovative prognostic signature of 8 DRLs, offering a valuable prognostic tool and potential therapeutic targets for bladder carcinoma. The findings have significant implications for TMB, the immune landscape, and patient responsiveness to immunotherapy and targeted treatments.Copyright © 2024 the Author(s). Published by Wolters Kluwer Health, Inc.