目的利用生物信息学方法对中枢神经系统性原始神经外胚层瘤(central nervous system primitive neuroectodermal tumors,CNS-PNETs)基因表达谱芯片进行分析,从分子水平探讨CNS-PNETs可能的发病机制。方法从GEO数据库下载CNS-PNETs的基因表达谱芯片数据集GSE35493和GSE74195,利用GEO2R在线分析工具以及Venn软件筛选出差异表达基因(differentially expressed genes,DEGs),并利用DAVID数据库在线分析工具对DEGs进行基因本体论(Gene ontology,GO)和通路富集(Kyoto Encyclopedia of Genes and Genomes,KEGG)分析,通过STRING在线分析工具、Cytoscape软件及其插件cytoHubba对CNS-PNETs的DEGs进行蛋白相互作用(proteinprotein interaction,PPI)网络分析,寻找关键基因。结果本研究共获得262个DEGs,包括49个上调基因和213个下调基因。GO功能和KEGG信号通路富集分析结果显示,DEGs涉及了DNA转录和有丝分裂核分裂、细胞分裂、运动行为、学习记忆和突触信号传递等生物过程,参与了细胞周期、肿瘤相关通路及p53信号通路、突触相关信号通路、cAMP信号通路及钙离子信号通路等。通过STRING分析筛选出10个关键基因:CDK1,CDC20,MAD2L1,KIF11,ASPM,TOP2A,TTK,NDC80,NUSAP1,DLGAP5。结论包括CDK1在内的10个关键基因可能在CNS-PNETs发生发展中起重要作用。本研究为探索CNS-PNETs的发病机制提供新的线索。
When gene expression profile is used for gene detection,the probe on the chip can emit fluorescence with different wavelengths.Under the action of confocal laser scanner,a clear gene change image can be obtained,by which the gene changes of the sample to be tested can be observed directly.First,the knee osteoarthritis(KOA)models of mice are established by the method of collateral ligament and meniscus resection(MLI-OA).Then,Bushen Huoxue formula is given by gavage,and ribonucleic acid(RNA)is routinely extracted and purified.Finally,the gene expression changes of KOA tissues of mice are detected by Agilent SurePrint G3 Mouse GE V2.0 gene expression profile.The results show that Bushen Huoxue formula has significant regulation effect on gene expression of KOA tissue.Among the genes with significant up-regulation effect of Bushen Huoxue formula,there are 56 genes of traditional Chinese medicine(TCM)groups up-regulated more than twice compared with model groups.Among the genes with significant down-regulation effect,there are 119 genes of TCM groups down-regulated more than twice compared with model groups.The experimental results indicate that Bushen Huoxue formula may promote the metabolism of arthritic factors and delay cartilage degeneration to treat KOA by regulating genes that are currently unknown in the pathological process of KOA.
TAN SihangSHI JixiangZHOU QiangZHOU JunjieHAO ShengkunWANG WenyanZHUANG Weikang
目的:应用生物信息学方法对哮喘患者上气道基因表达谱芯片进行分析,进一步探讨其可能的发病机制。方法:从GEO(Gene expressionomnibus)数据库下载哮喘表达谱的芯片数据(GSE41861),利用R语言Limma软件包筛选哮喘患者与非哮喘患者鼻黏膜的差异表达基因(differentially expressed genes,DEGs),并对差异基因作基因本体论(gene ontology,GO)和京都基因与基因组百科全书(kyoto encyclopedia of genes and genomes,KEGG)通路富集分析。利用STRING在线数据库进行蛋白-蛋白相互作用(protein protein interaction,PPI)分析,并用Cytoscape软件及其插件NetworkAnalyzer进行可视化,寻找关键(Hub)基因。结果:共筛选出118个差异基因,其中包括93个上调基因和25个下调基因。对差异基因进行生物信息学分析,上调DEGs主要参与唾液分泌相关通路和结核相关通路,下调DEGs主要参与TGF-β相关信号通路。蛋白网络分析筛选出CDC5L、AR和ACTR23个degree得分>10的关键基因。结论:CDC5L、AR、ACTR2可能在哮喘的发生发展中起着重要作用,为研究哮喘的发病机制提供了新的策略。
