联合多数据库构建肝细胞癌自噬基因预后模型  

A prognostic model of autophagy gene in hepatocellular carcinoma based on multidatabase

作　　者：李嵘祺 曹亚文 丁可 沈粤春[2] 李君[1] Li Rongqi;Cao Yawen;Ding Ke;Shen Yuechun;Li Jun(Department of Hepatobiliary Surgery,the First Affiliated Hospital of Guangzhou Medical University,Guangzhou 510120,China;Department of Cardiology,the First Affiliated Hospital of Guangzhou Medical University,Guangzhou 510120,China)

机构地区：[1]广州医科大学附属第一医院肝胆外科,510120 [2]广州医科大学附属第一医院心内科,510120

出　　处：《中华肝胆外科杂志》2021年第2期101-105,共5页Chinese Journal of Hepatobiliary Surgery

基　　金：广州市临床特色技术项目(序号26)。

摘　　要：目的构建自噬基因表达特征的肝细胞癌(hepatocellular carcinoma,HCC)患者预后的预测模型。方法从癌症基因组图谱(The Cancer Genome Atlas,TCGA)、基因型-组织表达研究项目(The Genotype-Tissue Expression,GTEx)数据库中分别得到所有HCC和正常肝细胞组织的基因转录表达数据,并将每个样本的基因转录表达数据统一转化为log2(FPKM值+1),消除数据库之间测试平台的数据差异。根据人类自噬基因库中获取的人类自噬基因列表筛选出TCGA-GTEx整合后序列中每个样本对应的自噬基因的表达量。采用R语言limma包以错误发现率(FDR)<0.05及差异倍数|logFC|>1为筛选标准,进行自噬基因差异表达分析。采用R语言clusterProfiler包对差异表达自噬基因以P<0.05为筛选标准,进行基因本体论(Gene Ontology,GO)富集分析及京都基因与基因组百科全书(Kyoto Encyclopedia of Genes and Genomes,KEGG)富集分析。根据自噬基因的差异表达量和患者的临床信息,采用R语言survival包进行单因素的Cox回归分析。进一步将单因素的Cox回归分析中有统计学意义(P<0.05)的自噬基因纳入到多因素Cox回归分析中,以每个差异表达的自噬基因表达量和相对应的回归系数coef值为基础,构建HCC的自噬基因预后模型:expmRNA1×βmRNA1+expmRNA2×βmRNA2+…+expmRNAn×βmRNAn(exp:基因表达量;β:多因素Cox回归分析的回归系数coef)。绘制预测模型的受试者工作特征(receiver operating characteristic,ROC)曲线并计算ROC曲线下面积(area under curve,AUC)评估模型预测价值。结果从TCGA-GTEx数据库中共得到HCC样本374例和正常肝组织样本160例的基因转录表达数据及临床信息。从整合后的样本序列中共筛选出205个自噬基因的表达数据,其中SPNS1、DIRAS3、TMEM74、NRG2、NRG1、IRGM、IKBKE、NKX2-3、BIRC5、CDKN2A、TP73为符合筛选标准的差异表达自噬基因。差异表达自噬基因GO主要富集在丝氨酸/苏氨酸蛋白激酶活�Objective To construct a prognostic model of hepatocellular carcinoma(HCC)with differential expression of autophagy genes.Method Autophagy genes expression data of HCC and normal liver tissues were obtained from The Cancer Genome Atlas(TCGA)database and The Genotype-Tissue Expression(GTEx)database respectively.The gene expression data from different platforms is normalized into log2(FPKM value+1).Differentially expressed autophagy-related genes of HCC were identified by using R program limma package from the TCGA-GTEx combined data set,the criteria of|logFC|>1 and FDR<0.05 was deemed to be of statistically significance.The Gene Ontology(GO)analyses and Kyoto Encyclopedia of Genes and Genomes(KEGG)pathway analyses were performed by using R program clusterProfiler package,as criteria of P<0.05.Univariate and multivariate Cox proportional hazards regression analyses were performed by using R program survival package to identify the HCC potential prognostic differentially expressed autophagy-related genes.Furthermore,the statistically significant(P<0.05)autophagy genes in the univariate Cox regression analysis were included in the multivariate Cox regression analysis,and the expression of each differentially expressed autophagy gene and the corresponding regression coefficient coef value based on this,the autophagy gene prognosis model of HCC was constructed:expmRNA1×βmRNA1+expmRNA2×βmRNA2+…+expmRNAn×βmRNAn(exp:gene expression level;β:regression coefficient coef of multivariate Cox regression analysis).Draw the receiver operating characteristic(ROC)curve of the predictive model and calculate the area under curve(AUC)to evaluate the predictive value of the model.Results The genes expression data and clinical information of 374 HCC samples and 160 normal liver tissue samples were obtained from TCGA and GTEx databases.Total 205 autophagy genes expression data was obtained from the TCGA-GTEx combined sequence.Among them,SPNS1,DIRAS3,TMEM74,NRG2,NRG1,IRGM,IKBKE,NKX2-3,BIRC5,CDKN2A,TP73 are differentially expresse

关 键 词：癌 肝细胞 自噬 预后 

分 类 号：R735.7[医药卫生—肿瘤] Q811.4[医药卫生—临床医学]