一种新型肝细胞癌预后模型的开发与验证

Development and validation of a novel prognostic model for hepatocellular carcinoma

目的建立一种预测能力佳、成本低的肝细胞癌预后模型。方法从癌症基因组图谱(TCGA)和国际癌症基因组联盟(ICGC)下载肝细胞癌患者mRNA表达数据及其临床信息。筛选与总生存期(overall survival,OS)相关的差异基因,采用排列组合方法批量基于TCGA队列建模,进行ICGC队列验证,通过受试者工作特征曲线(receiver operating characteristiccurve,ROC)的曲线下面积(AUC)筛选满足条件的基因组合。结果本研究共筛选预后相关差异基因692个,并筛选出最佳基因组合DPYSL4、DTYMK、HOXD9,构建一个含3个基因的模型,将患者分为高、低风险2组。与低风险组相比,高风险组患者的OS显著降低(TCGA队列中P<0.001,ICGC队列中P<0.001)。在多因素Cox回归分析中,风险评分仍是OS的独立预测因子(TCGA队列中HR>1且P<0.001,ICGC队列中HR>1且P<0.001)。功能分析显示细胞周期、核糖体、代谢相关过程富集。结论本研究开发了一种新型肝细胞癌预后模型,可用于预测肝细胞患者的预后。

Objective To establish a prognostic model for hepatocellular carcinoma patients with good predictive performance and low cost.Methods The transcriptome data and clinical information were downloaded from the Cancer Genome Atlas(TCGA)and International Cancer Genome Consortium(ICGC).The differential genes associated with overall sunival(OS)were screened.TCGA cohort was modeled using permutation and the predictive performance was evaluated by the AUC of ROC curve based on TCGA cohort and ICGC cohort.Results A total of 692 prognosis-related differential genes were screened out,with the best gene combinations of DPYSL4,DTYMK,and HOXD9.Then,a three-gene model was constructed where patients were classified into two risk groups.Patients in the high-risk group showed significantly reduced OS compared with those in the low-risk group(P<0.001 in both TCGA and ICGC cohorts).The risk score was an independent predictor for OS in multivariate Cox regression analysis(HR>1 and P<0.001).Functional analysis revealed that ribosome-related,cell cycle,and metabolism-related pathways were enriched.Conclusions This study establishes a novel hepatocellular carcinoma prognostic model that can be used to predict the prognosis of hepatocellular patients.