Identification of a metabolism-related gene signature predicting overall survival for bladder cancer
Identification of a metabolism-related gene signature predicting overall survival for bladder cancer
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DOI：
10.1016/j.ygeno.2022.110402
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发表时间：
2022-07-01
期刊：
影响因子：
4.4
通讯作者：
Zhang, Hao
中科院分区：
文献类型：
--
作者：
Qiu, Tianzhu;Chen, Yi;Zhang, Hao
新陈代谢的重新编程正在成为癌症的新标志。本研究旨在对膀胱癌代谢相关基因进行生物信息学分析，并构建膀胱癌代谢相关基因的特征标记，以预测膀胱癌的预后。从TCGA数据库中筛选出373个差异表达的代谢相关基因。综合考虑生存时间和临床信息，我们构建了一个风险评分来预测临床预后。低危患者的预后优于高危患者。多因素分析显示，危险评分是膀胱癌独立的预后指标。ROC曲线也证明风险评分较其他单项指标对预后的预测能力更强。列线图也显示了临床净效益，以评估膀胱癌患者的预后。GSEA揭示了几个代谢相关的途径，这些途径在高风险和低风险组中差异富集，这可能有助于解释潜在的机制。该特征被证实是膀胱癌中有效的预后生物标志物。
Reprogramming of metabolism is becoming a novel hallmark of cancer. This study aims to perform bioinformatics analysis of metabolism-related genes in bladder cancer, and to construct a signature of metabolism-related genes for predicting the prognosis. A total of 373 differentially expressed metabolism-related genes were identified from TCGA database. Taking survival time and clinical information into consideration, we constructed a risk score to predict clinical prognosis. Low-risk patients had a better prognosis than high-risk patients. Multivariate analysis showed that risk score was an independent prognostic indicator in bladder cancer. ROC curve also proved that risk score had better ability to predict prognosis than other individual indicators. Nomogram also showed a clinical net benefit to evaluate the prognosis of bladder cancer patients. GSEA revealed several metabolism-related pathways that were differentially enriched in the high-risk and low-risk groups, which might help to explain the underlying mechanisms. This signature was confirmed to be an effective prognostic biomarker in bladder cancer.