Development of prognostic model incorporating a ferroptosis/cuproptosis-related signature and mutational landscape analysis in muscle-invasive bladder cancer

膀胱癌 比例危险模型 肿瘤科 接收机工作特性 生存分析 医学 单变量 基因签名 转录组 列线图 肿瘤微环境 多元统计 生物 内科学 癌症 基因 基因表达 遗传学 计算机科学 机器学习
作者
Sida Hao,Zitong Yang,Li Wang,Guofeng Cai,Yong Qin
出处
期刊:BMC Cancer [Springer Nature]
卷期号:24 (1)
标识
DOI:10.1186/s12885-024-12741-5
摘要

Abstract Background Muscle-invasive bladder cancer (MIBC) is a prevalent and aggressive malignancy. Ferroptosis and cuproptosis are recently discovered forms of programmed cell death (PCD) that have attracted much attention. However, their interactions and impacts on MIBC overall survival (OS) and treatment outcomes remain unclear. Methods Data from the TCGA-BLCA project (as the training set), cBioPortal database, and GEO datasets (GSE13507 and GSE32894, as the test sets) were utilized to identify hub ferroptosis/cuproptosis-related genes (FRGs and CRGs) and develop a prognostic signature. Differential expression analysis (DEA) was conducted, followed by univariate and multivariate Cox’s regression analyses and multiple machine learning (ML) techniques to select genetic features. The performance of the ferroptosis/cuproptosis-related signature was evaluated using Kaplan–Meier (K–M) survival analysis and receiver-operating characteristics (ROC) curves. Mutational and tumour immune microenvironment landscapes were also explored. Real-time quantitative reverse transcription polymerase chain reaction (RT-qPCR) experiments confirmed the expression patterns of the hub genes, and functional assays assessed the effects of SCD knockdown on cell viability, proliferation, and migration. Results DEA revealed dysregulated FRGs and CRGs in the TCGA MIBC cohort. SCD, DDR2, and MT1A were identified as hub genes. A prognostic signature based on the sum of the weighted expression of these genes demonstrated strong predictive efficacy in the training and test sets. Nomogram incorporating this signature accurately predicted 1-, 3-, and 5-year survival probabilities in the TCGA cohort and GSE13507 dataset. Copy number variation (CNV) and tumour immune microenvironment analysis revealed that high risk score level groups were associated with immunosuppression and lower tumour purity. The associations of risk scores with immunotherapy and chemical drugs were also explored, indicating their potential for guiding treatment for MIBC patients. The dysregulated expression patterns of three hub genes were validated by RT-qPCR experiments. Conclusions Targeting hub FRGs and CRGs could be a promising therapeutic approach for MIBC. Our prognostic model offers a new framework for MIBC subtyping and can inform personalized therapeutic strategies.
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