列线图
肿瘤科
免疫系统
生物标志物
单变量
比例危险模型
内科学
医学
生物
计算生物学
多元统计
免疫学
遗传学
计算机科学
机器学习
作者
X-L Xing,Y Liu,J-H Liu,H-F Zhou,H-R Zhang,Q Zuo,P Bu,T Duan,Y Zhou,Z-Q Xiao
标识
DOI:10.26355/eurrev_202209_29634
摘要
Approximately 60% of patients with kidney renal clear cell carcinoma (KIRC) die within the first 2-3 years. The prognosis for patients with KIRC and its metastases is poor. Ferroptosis and providing immunity are novel treatment targets for several cancers, including KIRC. Therefore, it is important to identify suitable ferroptosis- and immune-related signatures to predict the prognosis and diagnosis of patients with KIRC.The corresponding data of patients with KIRC were obtained from the Cancer Genome Atlas. Univariate and multivariate Cox regression analyses were used to screen candidate biomarkers in patients with KIRC.We found that four FI-DEGs (BID, MET, LTB4R, and HMOX1) were independently associated with the overall survival of patients with KIRC. The prognosis and diagnosis model constructed using these four biomarkers could predict the outcome of KIRC, as measured by the receiver operating characteristic analyses.We identified 4 FI-DEGs that could be used as biomarkers in patients with KIRC. The present study not only contributes to understanding the roles of ferroptosis and immunity in the development of KIRC, but also to the diagnosis and prognosis of KIRC, although it remains to be further studied.
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