基于GEO数据库的HCC差异表达基因研究

Yani Zhao, Yongfang Xie
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引用次数: 1

摘要

背景:原发性肝细胞癌(HCC)是一种死亡率高的复杂恶性肿瘤。为探讨HCC的发病机制,本研究基于GEO数据库,进行生物信息学分析。方法:从GEO数据库下载基因表达数据集GSE121248进行生物信息学分析。采用Geo 2R以预定义标准(P < 0.05且| log FC|≥1)鉴定差异表达基因(deg)。STRING构建蛋白-蛋白相互作用网络,利用Cytoscape中的Cyto-Hubba插件鉴定hubgenes,采用MCODE算法检测PPI网络。然后使用基因本体(GO)和京都基因与基因组百科全书(KEGG)分析,使用DAVID和metscape对基因进行分析。结果:从GSE121248数据库中,研究获得1324个deg,其中上调423个,下调901个。通过Cytoscape软件中的Cyto_Hubba插件,我们鉴定出7个枢纽基因:CCNA2、CCNB1、CDK1、MAD2L1、TOP2A、RRM2、NDC80,其中TOP2A是核心基因。富集分析表明,DEGs的主要功能集中在负调控细胞凋亡过程、有丝分裂纺锤体、有丝分裂纺锤体、染色质结合和孕激素介导的卵母细胞成熟。在分析KEGG通路时,主要有富细胞周期、p53信号通路和卵母细胞减数分裂。结论:基于GEO数据库的地理信息学数据挖掘,肝癌的发生是多种基因共同作用的结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Study on Differential Expression Genes in HCC Based on GEO Database
Background: Primary hepatocellular carcinoma (HCC) is is a complex malignant tumor with high mortality. To explore the pathogenesis of HCC, Based on the GEO database, bioinformatics analysis was carried out based on the GEO database in this study. Methods: The gene expression datasets: GSE121248 were downloaded from the GEO database for bioinformatics analysis. Geo 2R was used to identify differentially expressed genes (DEGs) with the predefined criterion (P < 0.05 and | log FC| ≥ 1). STRING construct protein-protein interaction network, and identify hubgenes by Cyto-Hubba plug-in in Cytoscape, MCODE algorithm was employed to detect the PPI network. Then Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were used for DEGs using DAVID and metascape. Results: From the GSE121248 database, the research obtained 1324 DEGs (423 were up-regulated and 901 were down-regulated). Through Cyto_Hubba plug-in in Cytoscape software, we identified 7 hub genes: CCNA2, CCNB1, CDK1, MAD2L1, TOP2A, RRM2, NDC80, of which TOP2A is at the core. Enrichment analysis showed that the main functions of DEGs are concentrated in negative regulation of apoptosis process, mitotic spindle, mitotic spindle, chromatin binding, and progesterone-mediated oocyte maturation. In the analysis of the KEGG pathway, they are mainly rich cell cycle, p53 signal pathway and Oocyte meiosis. Conclusion: The occurrence of hepatocellular carcinoma is the result of the joint action of many genes based on geo-informatics data mining through GEO database.
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