Gene expression profiles contribute to robustly predicting prognosis in hepatocellular carcinoma

作     者：Long Liu Yuhui Wang Yuyuan Zhang Siyuan Weng Hui Xu Zaoqu Liu Xinwei Han Long Liu;Yuhui Wang;Yuyuan Zhang;Siyuan Weng;Hui Xu;Zaoqu Liu;Xinwei Han

作者机构：Department of Hepatobiliary and Pancreatic SurgeryThe First Affiliated Hospital of Zhengzhou UniversityZhengzhouHenan 450052China Prenatal Diagnosis CenterThe Third Affiliated Hospital of Zhengzhou UniversityZhengzhouHenan 450052China Department of Interventional RadiologyThe First Affiliated Hospital of Zhengzhou UniversityZhengzhouHenan 450052China Interventional Institute of Zhengzhou UniversityZhengzhouHenan 450052China Interventional Treatment and Clinical Research Center of Henan ProvinceZhengzhouHenan 450052China 

出 版 物：《Genes & Diseases》 (基因与疾病（英文）)

年 卷 期：2024年第11卷第2期

页      面：593-596页

核心收录：

学科分类：0710[理学-生物学] 1002[医学-临床医学] 100214[医学-肿瘤学] 10[医学] 

基　　金：supported by the Henan Province Medical Research Project Henan China(No.LHGJ20190388) 

主　　题：prognosis clinical arterial 

摘      要：Hepatocellular carcinoma(HCC)is characterized by both inter-and intra-tumor heterogeneity and has distinct clinical outcomes.1 A promising clinical tool to perform patient stratification,prognosis evaluation,and treatment recommendations is ***,we enrolled a total of 1595 tumor patients from 13 independent cohorts,including seven cohorts with survival data,four cohorts with immunotherapy information,and two cohorts with transcatheter arterial chemoembolization(TACE)and Sorafenib information,respectively(Table S1).Using 96 algorithms combinations derived from 10 popular machinelearning approaches,a novel framework was constructed and described in Figure ***,a total of 26 stable consensus prognostic genes were screened in seven cohorts harboring complete survival information via univariate Cox regression analysis(Fig.S2A).