Lin Ting, Gu Jingxian, Qu Kai, Zhang Xing, Ma Xiaohua, Miao Runchen, Xiang Xiaohong, Fu Yunong, Niu Wenquan, She Junjun, Liu Chang
Department of Hepatobiliary Surgery, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'a, Shaanxi 710061, China.
Institute of Clinical Medical Sciences, China-Japan Friendship Hospital, Beijing 100029, China.
Aging (Albany NY). 2018 Sep 21;10(9):2480-2497. doi: 10.18632/aging.101563.
A large panel of molecular biomarkers have been identified to predict the prognosis of hepatocellular carcinoma (HCC), yet with limited clinical application due to difficult extrapolation. We here generated a genetic risk score system comprised of 12 HCC-specific genes to better predict the prognosis of HCC patients. Four genomics profiling datasets (GSE5851, GSE28691, GSE15765 and GSE14323) were searched to seek HCC-specific genes by comparisons between cancer samples and normal liver tissues and between different subtypes of hepatic neoplasms. Univariate survival analysis screened HCC-specific genes associated with overall survival (OS) in the training dataset for next-step risk model construction. The prognostic value of the constructed HCC risk score system was then validated in the TCGA dataset. Stratified analysis indicated this scoring system showed better performance in elderly male patients with HBV infection and preoperative lower levels of creatinine, alpha-fetoprotein and platelet and higher level of albumin. Functional annotation of this risk model in high-risk patients revealed that pathways associated with cell cycle, cell migration and inflammation were significantly enriched. In summary, our constructed HCC-specific gene risk model demonstrated robustness and potentiality in predicting the prognosis of HCC patients, especially among elderly male patients with HBV infection and relatively better general conditions.
已经鉴定出大量分子生物标志物来预测肝细胞癌(HCC)的预后,但由于难以外推,其临床应用有限。我们在此生成了一个由12个HCC特异性基因组成的遗传风险评分系统,以更好地预测HCC患者的预后。通过比较癌症样本与正常肝组织以及肝肿瘤不同亚型之间的差异,搜索了四个基因组图谱数据集(GSE5851、GSE28691、GSE15765和GSE14323)以寻找HCC特异性基因。单变量生存分析在训练数据集中筛选出与总生存期(OS)相关的HCC特异性基因,用于下一步风险模型构建。然后在TCGA数据集中验证构建的HCC风险评分系统的预后价值。分层分析表明,该评分系统在老年男性HBV感染患者以及术前肌酐、甲胎蛋白和血小板水平较低且白蛋白水平较高的患者中表现更好。对高危患者中该风险模型的功能注释显示,与细胞周期、细胞迁移和炎症相关的通路显著富集。总之,我们构建的HCC特异性基因风险模型在预测HCC患者的预后方面表现出稳健性和潜力,尤其是在老年男性HBV感染且一般状况相对较好的患者中。