【摘要】 目的通过生物信息学方法筛选出一组可预测肝细胞癌(HCC)预后的特征性基因,作为预测肝细胞癌预后及指导靶向治疗的关键基因。方法首先从GEO数据库下载基因表达数据集GSE41804、GSE19665和GSE101685,从中筛选出HCC与正常组织差异表达的基因。通过GO功能富集分析和KEGG通路分析对上述差异表达基因进行可视化分析。通过在线网站STRING和Cytoscape 3.6.1软件对差异表达基因构建蛋白互作网络图,获得候选基因。从候选基因中通过Kaplan-Meier在线分析网站筛选出与HCC患者生存密切相关的关键基因,分别建立基于关键基因的HCC预后分析模型,同时把有生存意义的关键基因作为一个联合特征性基因集建立与HCC患者预后关系的生存模型。并通过在线网站GEPIA对关键基因表达量分别进行可视化分析。获取我科确诊为HCC的患者的癌组织和对应的正常组织的实物标本,通过实时定量聚合酶链反应(RT-qPCR)测定关键基因表达量,并与数据库中分析获得的关键基因表达量进行对比。结果将GEO数据库中GSE41804、GSE19665以及GSE1016853个基因表达数据集进行数据筛选后,共得到147个HCC与正常组织差异表达基因,通过GO功能富集分析显示这些基因与对无机物的反应、加单氧酶活化、类固醇氢化酶活化等生物进程明显相关,KEGG通路分析显示,差异表达基因主要通过p53信号通路影响HCC的发生。使用STRING和Cytoscape建立蛋白互作网络图,分析得到FAM83D、CYP2C8、MT1M、SLCO1B3、GYS2、FCN3 6个候选基因。对候选基因进行Kaplan-Meier预后分析,发现FAM83D、CYP2C8、SLCO1B3、GYS2和FCN3为具有生存意义的5个关键基因,其中CYP2C8高表达患者的总生存期(OS)显著长于低表达的患者,而FAM83D、SLCO1B3、GYS2和FCN3高表达患者的OS显著短于低表达患者。数据库中资料显示正常组织中CYP2C8、FCN3、GYS2及SLCO1B3表达量高于HCC组织,而正常组织中FAM83D表达量低于HCC组织,RT-qPCR检测实物标本中关键基因的表达量,与在数据库中分析获得的关键基因表达量结果一致。结论筛选出了FAM83D、CYP2C8、SLCO1B3、GYS2、FCN3 5个与HCC预后密切相关的特征性基因,可以作为预测肝细胞癌预后及指导靶向治疗的关键基因。更多还原

【Abstract】 Objective To screen out the genes which can be used to predict the prognosis of hepatocellular carcinoma(HCC) and guide targeted therapy using bioinformatics methods. Methods First, the gene expression datasets GSE41804, GSE19665, and GSE101685 were downloaded from the GEO database to screen out the differentially expressed genes between HCC tissue and normal tissue. The gene ontology(GO) functional enrichment analysis and the KEGG pathway analysis were used to perform a visualized analysis of the differentially expressed genes. The online website STRING and Cytoscape 3.6.1 software were used to establish a protein-protein interaction network for the differentially expressed genes to obtain candidate genes, and the Kaplan-Meier online analysis was performed for these candidate genes to screen out the key genes closely associated with the survival of patients with HCC. A prognostic model was established for each key gene, and a survival model was established based on the combination of key genes with survival significance and their association with prognosis. A visualized analysis was performed for the expression of key genes through GEPIA. HCC tissue samples and normal liver samples were obtained from the patients who were diagnosed with HCC in our department, and RT-qPCR was used to measure the expression of key genes, which was then compared with the data obtained in databases. Results The gene expression datasets GSE41804, GSE19665, and GSE1016853 in the GEO database were screened and 147 differentially expressed genes between HCC tissue and normal tissue were obtained. The GO functional enrichment analysis showed that these genes were significantly associated with the biological processes including inorganic reaction, monooxygenase activation, and steroid hydrogenase activation, and the KEGG pathway analysis showed that these differentially expressed genes mainly affected the development of HCC through the p53 signaling pathway. The analysis of the protein-protein interaction network established by STRING and Cytoscape obtained six candidate genes, i.e., FAM83D, CYP2C8, MT1M, SLCO1B3, GYS2, and FCN3. The Kaplan-Meier prognostic analysis of the candidate genes showed that FAM83D, CYP2C8, SLCO1B3, GYS2, and FCN3 had survival significance; the patients with high expression of CYP2C8 had a significantly longer overall survival time than those with low expression, while the patients with high expression of FAM83D, SLCO1B3, GYS2, and FCN3 had a significantly shorter overall survival time than those with low expression. The data in the database showed that the expression of CYP2C8, FCN3, GYS2, and SLCO1B3 in normal tissue was higher than that in HCC tissue, while the expression of FAM83D in normal tissue was lower than that in HCC tissue; RT-qPCR was used to measure the expression of key genes in samples and obtained consistent results with the data in the database. Conclusion Five characteristic genes, i.e., FAM83D, CYP2C8, SLCO1B3, GYS2, and FCN3, are screened out and are closely associated with the prognosis of HCC, and therefore, they can used as the key genes for predicting the prognosis of HCC and guiding targeted therapy.更多还原