基于肝硬化代谢相关基因的肝细胞癌预后预测模型的构建

CONSTRUCTION OF A PROGNOSTIC MODEL FOR HEPATOCELLULAR CARCINOMA BASED ON METABOLISM-RELATED GENES IN LIVER CIRRHOSIS

目的利用肝硬化代谢相关基因构建肝细胞癌(HCC)患者预后预测模型。方法在肿瘤基因组图谱(TCGA)数据库中获取肝细胞癌及癌旁组织的表达图谱和临床数据,根据Wilcoxon检验和单因素分析确定与预后相关的差异基因,应用LASSO Cox回归模型在TCGA数据库中创建预后预测模型,并用国际癌症基因组联盟(ICGC)数据库进行验证。采用受试者工作特征(ROC)曲线、生存分析和多因素Cox分析评估模型的预后预测价值,采用校准曲线及临床决策曲线(DCA)验证模型的校准度和临床适应性。对高风险组和低风险组的风险差异基因进行GO、KEGG及ssGSEA分析。结果TCGA数据库的筛选分析结果显示,有5个风险基因被纳入预后预测模型,分别为CYP2C9、ME1、MMP1、UQCRH、UQCRHL。由此获得的风险评分公式为:-0.059×CYP2C 9+0.123×ME1+0.262×MMP1+0.111×UQCRH+0.306×UQCRHL。经TCGA及ICGC数据库训练和验证后显示,ROC曲线中患者1、2、3年生存率的曲线下面积均>0.65,生存分析显示低风险组的预后显著优于高风险组,单因素及多因素Cox分析显示风险评分可作为独立的预后预测因子,校准曲线表明模型校准度较优,DCA曲线表明风险评分用于预测预后临床适应性更佳,准确性更好。高风险组和低风险组的风险差异基因主要富集于蛋白代谢生物学功能上,且两组间免疫相关功能差异显著。结论成功构建了基于CYP 2 C 9、ME 1、MMP 1、UQCRH、UQCRHL的预后预测模型,且该模型可以有效预测HCC患者的预后,同时可对HCC患者的精准治疗提供指导。

Objective To construct a prognostic model for patients with hepatocellular carcinoma(HCC)based on metabolism-related genes in liver cirrhosis.Methods The Cancer Genome Atlas(TCGA)database was used to obtain the expression profile and clinical data of HCC tissue and adjacent tissue,and the Wilcoxon test and a univariate analysis were used to identify the differentially expressed genes associated with prognosis.The LASSO Cox regression model was used to construct a prognostic model in the TCGA database,and the International Cancer Genome Consortium(ICGC)database was used for validation.The receiver operating characteristic(ROC)curve,survival analysis,and multivariate Cox analysis were used to assess the prognostic value of the model,and calibration curve analysis and decision curve analysis(DCA)were used to validate the calibration and clinical adaptability of the model.GO,KEGG,and ssGSEA analyses were performed for the differentially expressed risk genes between the high-risk group and the low-risk group.Results The screening and analysis results of the TCGA database showed that five risk genes were included in the prognostic model,namely CYP2C9,ME1,MMP1,UQCRH,and UQCRHL,and the risk scoring formula obtained was-0.059×CYP2C9+0.123×ME1+0.262×MMP1+0.111×UQCRH+0.306×UQCRHL.After training and validation by the TCGA and ICGC databases,the ROC curves of 1-,2-,and 3 year survival rates had an area under the ROC curve of>0.65.Survival analysis showed that the low-risk group had a significantly better prognosis than the high-risk group,and the univariate and multivariate Cox analyses showed that the risk score could be used as an independent predictive factor.The calibration curve showed that the model had good calibration,and the DCA curve showed that the risk score had good clinical adaptability and accuracy in predicting prognosis.The differentially expressed risk genes between the high-risk group and the low-risk group were mainly enriched in the biological function of protein metabolism,and there were significant differences in immune-rela-ted functions between the two groups.Conclusion A prognostic model is successfully constructed based on CYP 2 C 9,ME 1,MMP 1,UQCRH,and UQCRHL,which can effectively predict the prognosis of HCC patients and provide guidance for the precise treatment of HCC patients.