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本研究基于GEO数据库,选取由慢性乙型肝炎诱导的肝细胞癌芯片数据GSE121248为研究对象,利用GEO2R软件分析数据,筛选出差异表达基因,利用DAVID数据库进行GO分析和KEGG pathway富集分析。利用STRING数据库构建PPI网络,分析筛选核心基因。利用GEPIA对核心基因的表达进行验证,Kaplan Meier Plotter在线分析工具对核心基因与患者生存情况的相关性进行验证。通过上述方法筛选出309个DEGs,其中上调基因94个,下调基因215个。差异基因功能分析显示上调的DEGs主要参与细胞周期和卵母细胞减数分裂通路等途径,下调的DEGs则在补体和凝血级联、代谢途径以及咖啡因代谢途径富集。筛选出15个具有高度关联性的核心基因(BUB1, BUB1B, BIRC5, CCNB1, CCNB2, CDC20, CDK1, KIF-20A, MAD2L1, NCAPG, ZWINT, PBK, BTL, TTK和NUSAP1),它们与肝癌患者的总体生存率具有明显相关性,并为其构建了miRNA调控网络。本研究通过生物信息学方法有效分析了肝细胞癌发生、发展相关的差异表达基因,筛选出15个核心基因,分析其生物学相关功能,以期探索肝细胞癌发病机制,并为临床诊断标志物的改进以及筛选提供一定的理论基础。
Abstract：Our study is based on the GEO database, and the hepatocellular carcinoma chip profile GSE121248 induced by chronic hepatitis B was selected as the study subject. The differential expressed genes were screened by using GEO2 R tools. GO analysis and KEGG pathway analysis were performed for differential expressed genes by using DAVID. Then, the PPI network was constructed to pick out hub genes. Finally, the expression of hub genes and the patient survival were verified by the GEPIA and Kaplan Meier Plotter analysis tools. Through the above method, a total of 309 DEGs were screened, consisting of 94 up-regulated genes and 215 down-regulated genes.Functional analysis of DEGs showed that up-regulated DEGs are mainly involved in the cell cycle and oocyte meiosis pathways, down-regulated DEGs are enriched in the complement and coagulation cascades, metabolic pathways, and caffeine metabolic pathways. Moreover, fifteen hub genes with high correlation were screened, including BUB1, BUB1 B, BIRC5, CCNB1, CCNB2, CDC20, CDK1, KIF20 A, MAD2 L1, NCAPG, ZWINT, PBK, DTL, TTK and NUSAP1. They were found to be associated with the overall survival in patients with HCC and co nstructed a miRNA regulatory network. Bioinformatics can effectively analyze the DEGs associated with the occurrence and development of HCC. The identification of the 15 hub genes can provide a theoretical guidance for further research on the molecular mechanism and molecular marker screening of HCC.
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基本信息:
DOI：10.13417/j.gab.040.000867
中图分类号:R735.7;Q811.4
引用信息:
[1]李怡敏,汪洋,袁琳,等.基于生物信息学分析的肝癌核心基因的筛选及验证[J].基因组学与应用生物学,2021,40(02):867-877.DOI:10.13417/j.gab.040.000867.
基金信息:
重庆市基础科学与前沿技术研究专项(CSTC2015jcyjA10006); 国家自然科学基金青年科学基金项目(81501751); 重庆市博士后科学基金项目(Xm2014006)共同资助
2021-02-25
2021-02-25