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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logn 2(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(n FDR)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n 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(n P 1 and n 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 n 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 (n 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 coefficientn 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 coefficientn 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.n 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 expressed autophagy genes that meet the screening criteria. GO analysis mainly enriched in “regulation of protein serine/threonine kinase activity” , “ErbB 2 signaling pathway” , “protein kinase regulator activity” and “kinase regulator activity” ; KEGG analysis enriched frequently in “EGFR tyrosine kinase inhibitor resistance” , “Hippo signaling pathway” . After integrating and deleting samples with missing survival information, a total of 418 sample expressions were included in the Cox regression analysis. After univariate and multivariate Cox risk regression analysis, the two autophagy genes NRG1 ( n HR=1.5565, 95%n CI: 1.1793-2.0543) and IKBKE (n HR=1.7502, 95%n CI: 1.2093-2.5330) were screened out and a prognostic prediction model was established: (0.44247 × NRG1 expression level) + (0.55977 × IKBKE expression level). The ROC of the prognosis model shows that the AUC of the overall seven-year survival is 0.711.n Conclusion:The prognosis model of HCC based on NRG1 and IKBKE has high predictive value for the long-term survival rate of hepatocellular carcinoma patients.