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

IF 2.2 Q3 BIOCHEMISTRY & MOLECULAR BIOLOGY

Biochemistry and Biophysics Reports Pub Date : 2025-05-26 DOI:10.1016/j.bbrep.2025.102060

Zhixiong Zhang , Jinghua Zhong , Muhammad Sarfaraz Iqbal , Zhiwen Zeng , Xiaolu Duan

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引用次数: 0

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.

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一种综合的双曲视相关lncrna风险模型预测膀胱癌的预后和治疗意义

背景：双硫细胞死亡是一种新兴的细胞死亡形式；然而，其相关的长链非编码rna （dr-lncRNAs）在膀胱癌（BLCA）中的作用仍然知之甚少。通过利用迄今为止最全面的双曲光相关基因数据集，我们系统地开发并验证了一种新的dr-lncRNA特征，该特征阐明了BLCA的预后意义和免疫微环境动力学。方法利用肿瘤基因组图谱（Cancer Genome Atlas， TCGA）数据库，提取BLCA患者有意义的临床和RNA测序数据。采用多变量Cox和Lasso回归建立风险模型。对ROC分析、Kaplan-Meier分析和nomogram分析进行了仔细的效度评估。经过验证的研究评估了功能富集、免疫细胞浸润、癌症突变负荷和治疗敏感性之间复杂的相互作用。无监督的共识聚类确定了反映免疫系统改变、药物易感性和预后的亚组模式。结果共鉴定出9个与预后显著相关的lncrna，为构建由7个lncrna组成的风险模型奠定了基础。该模型在预测患者预后方面表现出显著的优势，有效地区分了高风险和低风险个体。功能富集分析揭示了它们可能参与免疫相关的生物学途径。高危组患者表现出更高的肿瘤突变负担、更活跃的免疫功能和对化疗药物的更高敏感性。通过一致聚类分析确定BLCA亚组之间的差异，包括临床特征、预后、lncRNA表达、免疫细胞浸润和免疫检查点概况。结论基于dr- lncrnas的风险模型是预测BLCA患者预后和指导个性化免疫治疗和治疗策略的一个很有前景的工具。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Biochemistry and Biophysics Reports Biochemistry, Genetics and Molecular Biology-Biophysics

CiteScore

4.60

自引率

0.00%

发文量

191

审稿时长

59 days

期刊介绍： Open access, online only, peer-reviewed international journal in the Life Sciences, established in 2014 Biochemistry and Biophysics Reports (BB Reports) publishes original research in all aspects of Biochemistry, Biophysics and related areas like Molecular and Cell Biology. BB Reports welcomes solid though more preliminary, descriptive and small scale results if they have the potential to stimulate and/or contribute to future research, leading to new insights or hypothesis. Primary criteria for acceptance is that the work is original, scientifically and technically sound and provides valuable knowledge to life sciences research. We strongly believe all results deserve to be published and documented for the advancement of science. BB Reports specifically appreciates receiving reports on: Negative results, Replication studies, Reanalysis of previous datasets.