Abstract: Objective To screen the differentially expressed genes（DEGs）between hepatoblastoma tissues and normal fetal liver tissues, to explore the mechanism of Hepatoblastoma, and to provide new gene diagnosis and gene therapy methods. Methods Data of hepatoblastoma tissues and the normal controls were extracted from GEO database. The R software RSTUDIO was used to explore the DEGs. DAVID Online （http：// david.ncifcrf.gov/） was used to perform the Gene ontology （GO） analysis. STRING Online （http ：//string-db.org） was used to integrate the protein-protein interaction network. Results A total of 290 DEGs（99 up-regulated genes and 191 down-regulated genes）were identified （P<0.05）. GO analysis indicated that Cell division, Extracellular exosome, as well as Metal ion binding were significantly enriched in up-regulated DEGs. And lipoprotein metabolic process, extracellular exosome, as well as heme binding were enriched in down-regulated DEGs. PPI network identified 12 hub-genes, including IMPDH2、AGXT、ALDH1A1、ALDH2、PFAS、SERPINC1、AGXT2、KNG1、 APOA1 、MAT1A 、APOC3 and HSD17B6. Conclusion With a variety of bioinformatics methods, a series of DEGs between Hepatoblastoma tissues and their normal control were explored. The might functions and expression of these genes in hepatoblastoma tumor were explained in different perspectives, which could provide theoretical basics of gene diagnosis and therapy in hepatoblastoma.

Key words: microarray, bioinformatics analysis, hepatoblastoma, diffrentially expressed genes

摘要： 目的　筛选肝母细胞瘤（hepatoblastoma）组织与小儿正常肝脏组织中的差异表达基因，探究肝母细胞瘤的发病机制，为其诊断和治疗提供新方向。方法　从GEO数据库中检索获取肝母细胞瘤组织和小儿正常肝脏组织的芯片数据，通过R语言软件RSTUDIO筛选芯片中的差异表达基因，使用DAVID数据库对筛选所得的差异表达基因进行功能注释，通过STRING数据库构建蛋白质相互作用网络，并进行中心性分析。结果　经筛选共获得肝母细胞瘤组织中290个差异表达基因,其中上调基因99个,下调基因191个（P<0.05）。GO（GeneOntology）功能注释分析显示，上调差异基因主要涉及细胞分裂、细胞外外泌体、金属离子结合等94个功能簇，下调差异基因主要涉及脂蛋白代谢、细胞外外泌体、血红素结合等100个功能簇。蛋白质相互作用网络分析示IMPDH2、AGXT、ALDH1A1、ALDH2、PFAS、SERPINC1、AGXT2、KNG1、APOA1、MAT1A、APOC3和HSD17B612个基因为与其他节点关系最密切的核心调控基因。结论　通过多种生物信息学方法联合分析三组高通量基因芯片，获得了肝母细胞瘤组织与正常小儿肝脏组织间的差异表达基因，并进一步从不同角度分析肝母细胞瘤异常增殖、转移等恶性生物学过程的发生机制，为肝母细胞瘤的诊断和治疗提供新方向。

关键词: 肝母细胞瘤, 生物信息学, 基因芯片, 差异表达基因