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乙型肝炎病毒相关性肝细胞癌关键候选基因的筛选与功能预测。

Screening and Functional Prediction of Key Candidate Genes in Hepatitis B Virus-Associated Hepatocellular Carcinoma.

机构信息

Department of Laboratory Medicine, Daping Hospital, Army Medical University (Third Military Medical University), Chongqing 400042, China.

出版信息

Biomed Res Int. 2020 Oct 9;2020:7653506. doi: 10.1155/2020/7653506. eCollection 2020.

Abstract

BACKGROUND

The molecular mechanism by which hepatitis B virus (HBV) induces hepatocellular carcinoma (HCC) is still unknown. The genomic expression profile and bioinformatics methods were used to investigate the potential pathogenesis and therapeutic targets for HBV-associated HCC (HBV-HCC).

METHODS

The microarray dataset GSE55092 was downloaded from the Gene Expression Omnibus (GEO) database. The data was analyzed by the bioinformatics software to find differentially expressed genes (DEGs). Gene Ontology (GO) enrichment analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, ingenuity pathway analysis (IPA), and protein-protein interaction (PPI) network analysis were then performed on DEGs. The hub genes were identified using Centiscape2.2 and Molecular Complex Detection (MCODE) in the Cytoscape software (Cytoscape_v3.7.2). The survival data of these hub genes was downloaded from the Gene Expression Profiling Interactive Analysis (GEPIA).

RESULTS

A total of 2264 mRNA transcripts were differentially expressed, including 764 upregulated and 1500 downregulated in tumor tissues. GO analysis revealed that these DEGs were related to the small-molecule metabolic process, xenobiotic metabolic process, and cellular nitrogen compound metabolic process. KEGG pathway analysis revealed that metabolic pathways, complement and coagulation cascades, and chemical carcinogenesis were involved. Diseases and biofunctions showed that DEGs were mainly associated with the following diseases or biological function abnormalities: cancer, organismal injury and abnormalities, gastrointestinal disease, and hepatic system disease. The top 10 upstream regulators were predicted to be activated or inhibited by -score and identified 25 networks. The 10 genes with the highest degree of connectivity were defined as the hub genes. Cox regression revealed that all the 10 genes (CDC20, BUB1B, KIF11, TTK, EZH2, ZWINT, NDC80, TPX2, MELK, and KIF20A) were related to the overall survival.

CONCLUSION

Our study provided a registry of genes that play important roles in regulating the development of HBV-HCC, assisting us in understanding the molecular mechanisms that underlie the carcinogenesis and progression of HCC.

摘要

背景

乙型肝炎病毒(HBV)诱导肝细胞癌(HCC)的分子机制尚不清楚。本研究采用基因组表达谱和生物信息学方法,探讨 HBV 相关 HCC(HBV-HCC)的潜在发病机制和治疗靶点。

方法

从基因表达综合数据库(GEO)下载 GSE55092 芯片数据集,采用生物信息学软件对数据进行分析,寻找差异表达基因(DEGs)。对 DEGs 进行基因本体论(GO)富集分析、京都基因与基因组百科全书(KEGG)通路分析、Ingenuity 通路分析(IPA)和蛋白质-蛋白质相互作用(PPI)网络分析。采用 Centiscape2.2 和 Cytoscape 软件中的 Molecular Complex Detection(MCODE)识别关键基因。从基因表达谱交互式分析(GEPIA)下载这些关键基因的生存数据。

结果

共筛选出 2264 个差异表达的 mRNA 转录本,其中肿瘤组织中上调 764 个,下调 1500 个。GO 分析显示,这些 DEGs 与小分子代谢过程、异生物质代谢过程和细胞氮化合物代谢过程有关。KEGG 通路分析显示,代谢途径、补体和凝血级联、化学致癌作用等通路参与其中。疾病和生物功能分析表明,DEGs 主要与以下疾病或生物功能异常有关:癌症、机体损伤和异常、胃肠道疾病和肝脏系统疾病。预测前 10 位的上游调节因子的-Score 值为正值或负值,鉴定出 25 个网络。连接度最高的 10 个基因被定义为关键基因。Cox 回归分析显示,所有 10 个基因(CDC20、BUB1B、KIF11、TTK、EZH2、ZWINT、NDC80、TPX2、MELK 和 KIF20A)均与总生存期相关。

结论

本研究为 HBV-HCC 发展中发挥重要作用的基因提供了一个基因谱,有助于我们了解 HCC 发生和发展的分子机制。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c866/7568806/2c230cd27cc4/BMRI2020-7653506.001.jpg

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