Zhao Guo-Jiang, Wu Zonglong, Ge Liyuan, Yang Feilong, Hong Kai, Zhang Shudong, Ma Lulin
Department of Urology, Peking University Third Hospital, Beijing, China.
Front Genet. 2021 Jun 9;12:650416. doi: 10.3389/fgene.2021.650416. eCollection 2021.
Clear cell renal cell carcinoma (ccRCC) is one of the most common tumors in the urinary system. Ferroptosis plays a vital role in ccRCC development and progression. We did an update of ferroptosis-related multigene expression signature for individualized prognosis prediction in patients with ccRCC. Differentially expressed ferroptosis-related genes in ccRCC and normal samples were screened using The Cancer Genome Atlas. Univariate and multivariate Cox regression analyses and machine learning methods were employed to identify optimal prognosis-related genes. , , , , , , and were selected to establish a prognostic risk score model. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses revealed that these genes were mainly enriched in immune-related pathways; single-sample Gene Set Enrichment Analysis revealed several immune cells potentially related to ferroptosis. Kaplan-Meier survival analysis demonstrated that patients with high-risk scores had significantly poor overall survival (log-rank = 7.815 × 10). The ferroptosis signature was identified as an independent prognostic factor. Finally, a prognostic nomogram, including the ferroptosis signature, age, histological grade, and stage status, was constructed. Analysis of The Cancer Genome Atlas-based calibration plots, C-index, and decision curve indicated the excellent predictive performance of the nomogram. The ferroptosis-related seven-gene risk score model is useful as a prognostic biomarker and suggests therapeutic targets for ccRCC. The prognostic nomogram may assist in individualized survival prediction and improve treatment strategies.
透明细胞肾细胞癌（ccRCC）是泌尿系统中最常见的肿瘤之一。铁死亡在ccRCC的发生发展中起着至关重要的作用。我们对与铁死亡相关的多基因表达特征进行了更新，以用于ccRCC患者的个体化预后预测。利用癌症基因组图谱筛选ccRCC和正常样本中差异表达的铁死亡相关基因。采用单因素和多因素Cox回归分析以及机器学习方法来鉴定最佳的预后相关基因。选择了[具体基因名称未给出]来建立预后风险评分模型。基因本体论和京都基因与基因组百科全书通路分析表明，这些基因主要富集于免疫相关通路；单样本基因集富集分析揭示了几种可能与铁死亡相关的免疫细胞。Kaplan-Meier生存分析表明，高风险评分的患者总体生存率显著较差（对数秩检验 = 7.815×10）。铁死亡特征被确定为一个独立的预后因素。最后，构建了一个预后列线图，包括铁死亡特征、年龄、组织学分级和分期状态。基于癌症基因组图谱的校准图、C指数和决策曲线分析表明列线图具有出色的预测性能。与铁死亡相关的七基因风险评分模型作为一种预后生物标志物很有用，并为ccRCC提示了治疗靶点。预后列线图可能有助于个体化生存预测并改善治疗策略。
