背景:肌肉浸润性膀胱癌(MIBC)是一种普遍存在的侵袭性恶性肿瘤。铁凋亡和铜凋亡是最近发现的程序性细胞死亡(PCD)形式,引起了广泛关注。然而,它们之间的相互作用以及对MIBC总生存期(OS)和治疗结局的影响尚不清楚.
方法:来自TCGA-BLCA项目的数据(作为训练集),cBioPortal数据库,和GEO数据集(GSE13507和GSE32894,作为测试集)被用来识别集线器铁凋亡/铜凋亡相关基因(FRGs和CRGs)并开发预后特征。进行了差异表达分析(DEA),其次是单变量和多变量Cox回归分析和多机器学习(ML)技术来选择遗传特征。使用Kaplan-Meier(K-M)生存分析和接受者工作特征(ROC)曲线评估铁凋亡/角化相关特征的性能。还探索了突变和肿瘤免疫微环境景观。实时定量逆转录聚合酶链反应(RT-qPCR)实验证实了hub基因的表达模式,和功能测定评估SCD敲低对细胞活力的影响,扩散,和移民。
结果:DEA显示TCGAMIBC队列中FRGs和CRGs失调。SCD,DDR2和MT1A被鉴定为hub基因。基于这些基因的加权表达的总和的预后特征在训练和测试集中证明了强的预测功效。包含此签名的列线图准确预测了1-,3-,TCGA队列和GSE13507数据集中的5年生存概率。拷贝数变异(CNV)和肿瘤免疫微环境分析显示,高风险评分水平组与免疫抑制和较低的肿瘤纯度有关。还探讨了风险评分与免疫治疗和化学药物的关系,表明他们有可能指导MIBC患者的治疗。通过RT-qPCR实验验证了三个hub基因的失调表达模式。
结论:靶向枢纽FRGs和CRGs可能是MIBC的一种有希望的治疗方法。我们的预后模型为MIBC亚型分型提供了一个新的框架,可以为个性化的治疗策略提供信息。
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.