Department of Urology, Jinling Hospital, Medical School of Nanjing University, Nanjing 210002, China.
The Comprehensive Cancer Centre of Drum Tower Hospital, Medical School of Nanjing University & Clinical Cancer Institute of Nanjing University, Nanjing 210008, China.
Comput Math Methods Med. 2022 Aug 30;2022:4456987. doi: 10.1155/2022/4456987. eCollection 2022.
In this study, we used the TCGA database and ICGC database to establish a prognostic model of iron death associated with renal cell carcinoma, which can provide predictive value for the identification of iron death-related genes and clinical treatment of renal clear cell carcinoma.
The gene expression profiles and clinical data of renal clear cell carcinoma and normal tissues were obtained in the TCGA database and ICGC database, and the differential genes related to iron death were screened out. The differential genes were screened out by single and multifactor Cox risk regression model. software, "edge" package (version 4.0), was used to identify the DELs of 551 transcriptional gene samples and 522 clinical samples. The risk prediction model with genes was established to analyze the correlation between the genes in the established model and clinical characteristics, Through the final screening of iron death related genes, it can be used to predict the prognosis of renal clear cell carcinoma and provide advice for clinical targeted therapy.
Seven iron death differential genes (CLS2, FANCD2, PHKG2, ACSL3, ATP5MC3, CISD1, PEBP1) associated with renal clear cell carcinoma were finally screened and were refer to previous relevant studies. These genes are closely related to iron death and have great value for the prognosis of renal clear cell carcinoma.
Seven iron death genes can accurately predict the survival of patients with renal clear cell carcinoma.
本研究利用 TCGA 数据库和 ICGC 数据库建立了与肾透明细胞癌相关的铁死亡预后模型，可为铁死亡相关基因的识别和肾透明细胞癌的临床治疗提供预测价值。
在 TCGA 数据库和 ICGC 数据库中获取肾透明细胞癌和正常组织的基因表达谱和临床资料，筛选出与铁死亡相关的差异基因。通过单因素和多因素 Cox 风险回归模型筛选差异基因。使用 R 语言软件“edge”包（版本 4.0）对 551 个转录基因样本和 522 个临床样本的 DELs 进行识别。建立以基因预测风险的模型，分析建立模型中基因与临床特征的相关性，通过最终筛选出与铁死亡相关的基因，可用于预测肾透明细胞癌的预后，并为临床靶向治疗提供建议。
最终筛选出与肾透明细胞癌相关的 7 个铁死亡差异基因（CLS2、FANCD2、PHKG2、ACSL3、ATP5MC3、CISD1、PEBP1），并参考既往相关研究，这些基因均与铁死亡密切相关，对肾透明细胞癌的预后具有重要价值。
7 个铁死亡基因可准确预测肾透明细胞癌患者的生存情况。
