【摘要】 目的通过生物信息学的方法分析阿尔茨海默病(AD)患者和正常对照之间的差异表达基因以及相应的靶向药物。方法从基因表达数据库(GEO)中获取AD患者和正常对照的相关数据芯片(GSE5281),利用R语言LIMMA程序包进行差异表达基因分析,筛选差异表达基因,使用DAVID数据库对进行基因本体(GO)及京都基因与基因组百科全书(KEGG)信号通路富集分析,再利用STRING数据库进行蛋白相互作用网络(PPI)分析,筛选出核心基因和关键基因,以核心基因为靶点的筛选出现有药物。结果共筛选出863个差异表达基因,包括246个上调基因和617个下调基因,GO富集分析结果示差异基因主要功能为ATP结合等,KEGG通路富集分析为帕金森病、朊蛋白病等多种神经退行性疾病,以及长时程增强作用、轴突导向等,STRING数据库分析得到5个核心基因(PSMA7、PSMA3、PSMB7、PSMC5和PSMC3),和31个关键基因(包括23个上调基因和8个下调基因),以核心基因为靶点筛选出8种现有药物,3组基因芯片两两比较筛选出13个共同的差异表达基因。结论基于GEO数据库的生物信息学分析,AD患者与正常对照之间存在差异表达基因并筛选出现有药物。更多还原

【Abstract】 Objective To investigate differential expression genes(DEGs) between Alzheimer’s disease(AD) and normal controls by bioinformatics analysis.Methods The microarray dataset GSE5281 was download from GEO database,which included brain tissue in AD and normal controls.The DEGs were obtained by R project.Analysis of DEGs based in DAVID database was used to obtain gene ontology(GO) and kyoto encyclopedia of genes and genomes(KEGG) pathway.The protein protein interaction network(PPI) was established using STRING database to identify hub genes.and core genes.Moreover,the existing drugs target to these core genes were screen to explore the therapeutic effect for AD.Results A total 863 DEGs were obtained,of which 246 genes were up-regulated and 617 genes were down-regulated in AD group.GO showed that DEGs were mainly involved in ATP binding,and KEGG pathway involved several neurodegenerative diseases including Parkinson’s disease and prion disease,long-term potentiation and axon guidance.5 core genes(PSMA7,PSMA3,PSMB7,PSMC5 and PSMC3) and 31 hub genes including 23 up-regulated genes and 8 down-regulated genes were obtained by PPI analysis.Several existing drugs have targeted to core genes.Some common differential expression genes were obtained by paired comparison of 3 groups of gene microarrays.Conclusion Bioinformatics analysis based on GEO database showed that there were DEGs between Alzheimer’s disease(AD) and normal controls,and 8 existing drugs were identified.更多还原