中国血吸虫病防治杂志 ›› 2022, Vol. 34 ›› Issue (5): 507-.

• 论著 • 上一篇    下一篇

乙型肝炎病毒相关肝细胞癌核心差异 表达基因生物信息学分析

余雨1,成军1,2,梅传忠1,戴玉柱1,2*   

  1. 1 蚌埠医学院检验医学院(安徽 蚌埠 233000);2 中国人民解放军联勤保障部队第九〇三医院检验科(浙江 杭州 310013)
  • 出版日期:2022-11-23 发布日期:2022-11-23
  • 作者简介:余雨,男,硕士研究生。研究方向:肿瘤分子生物学
  • 基金资助:
    蚌埠医学院研究生科研创新计划项目(Byycxz21008)

Bioinformatics analysis of core differentially expressed genes in hepatitis B virus⁃related hepatocellular carcinoma

YU Yu1, CHENG Jun1, 2, MEI Chuan⁃Zhong1, DAI Yu⁃Zhu1, 2*   

  1. 1 School of Laboratory Medicine, Bengbu Medical College, Bengbu, Anhui 233000, China; 2 Department of Clinical Research, The 903rd Hospital of Joint Logistics Support Forces of Chinese People’s Liberation Army, Hangzhou, Zhejiang 310013, China
  • Online:2022-11-23 Published:2022-11-23

摘要: 目的 探索与乙型肝炎病毒(HBV)相关肝细胞癌(HCC)发生发展相关的核心基因,为进一步揭示HBV相关HCC发病机制提供参考。方法 从高通量基因表达数据库(GEO)中下载GSE55092、GSE121248两个数据集,采用R语言筛选HCC组织和癌旁组织间差异表达基因(DEGs),并绘制可视化火山图。对DEGs基因进行基因本体论(GO)和京都基因和基因组百科全书(KEGG)富集分析,构建蛋白质相互作用(PPI)网络,并用Cytoscape 3.9.0开源平台中分子复合物检测(MCODE)和cytoHubba插件筛选核心DEGs。利用UALCAN和Kaplan Meier⁃plotter数据库中临床样本数据对筛选出的核心DEGs进行差异表达和生存分析验证。结果 从GSE55092数据集和GSE121248数据集中分别筛选出1 148个和686个DEGs,其中下调表达基因分别为703个和477个、上调表达基因分别为445个和209个;两个数据集共筛选出557个共同表达的DEGs,其中下调表达基因384个、上调表达基因173个。GO富集分析显示,DEGs主要参与细胞分裂、细胞增殖、氧化还原、免疫应答、蛋白质水解等生物学过程,细胞核、细胞质、胞外囊泡、内质网膜等细胞成分,与钙离子、蛋白激酶、DNA、血红素等结合分子功能;KEGG通路分析显示,DEGs主要参与细胞周期、卵母细胞减数分裂、代谢途径、抗生素生物合成、p53信号通路等通路。PPI网络分析发现10个核心DEGs,包括CDK1、CCNB1、CCNA2、TOP2A、AURKA、CCNB2、KIF11、CDC20、KIF20A、BUB1B;经临床样本数据验证,CDK1、KIF11、KIF20A这3个DEGs在HBV相关HCC患者中差异表达,且与患者预后不良相关。结论 CDK1、KIF11、KIF20A可能在HBV相关HCC发生发展中发挥重要作用,有望成为HBV相关HCC潜在诊断标志物和治疗靶标。  

关键词: 乙型肝炎病毒, 肝细胞癌, 差异表达基因, 生物标志物, 生物信息学分析

Abstract: Objective To identify the core genes associated with the development and progression of hepatitis B virus (HBV)⁃related hepatocellular carcinoma (HCC), so as to provide insights into the elucidation of pathogenesis of HBV⁃related HCC. Methods GSE55092 and GSE121248 datasets were downloaded from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) between HCC and peri⁃cancer tissues were screened using the R package, and the volcano map of DEGs were plotted. The DEGs were subjected to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses, and a protein⁃protein interaction (PPI) network was created. The hub DEGs were screened using Molecular Complex Detection (MCODE) and cytoHubba plugins in the open⁃access platform Cytoscape 3.9.0. Then, the screened hub DEGs were validated for differential expression and survival analysis using clinical sample data captured from the UALCAN and Kaplan Meier⁃plotter databases. Results A total of 1 148 and 686 DEGs were screened between HCC and peri⁃cancer tissues in GSE55092 and GSE121248 datasets, including 703 and 477 down⁃regulated genes and 445 and 209 up⁃regulated genes, respectively. A total of 557 common DEGs were screened between GSE55092 and GSE121248 datasets, including 384 down⁃regulated genes and 173 up⁃regulated genes. GO enrichment analysis showed that these DEGs were significantly enriched in biological processes of cell division, cell proliferation, redox process, immune response and proteolysis, cellular components of cell nucleus, cytoplasm, extracellular vesicle and endoplasmic reticulum membrane, and molecular functions of binding to calcium ion, protein kinase, DNA and heme. KEGG pathway analysis revealed that these DEGs were significantly enriched in pathways of cell cycle, oocyte meiosis, metabolic pathway, antibiotic biosynthesis and p53 signaling. PPI network analysis identified 10 DEGs, including CDK1, CCNB1, CCNA2, TOP2A, AURKA, CCNB2, KIF11, CDC20, KIF20A and BUB1B, and CDK1, KIF11 and KIF20A were found to be differentially expressed and correlate with poor prognosis among HBV⁃related HCC patients following clinical sample data validation. Conclusion CDK1, KIF11 and KIF20A may play a critical role in the development and progression of HBV⁃related HCC, which may be potential diagnostic biomarkers and therapeutic targets of HBV⁃related HCC.  

Key words: Hepatitis B virus, Hepatocellular carcinoma, Differentially expressed gene, Biomarker, Bioinformatics analysis

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