急性髓系白血病潜在关键基因表达谱的生物信息学分析

Bioinformatics analysis of the expression profiles of acute myeloidleukemia and potential key genes

目的利用GEO数据库通过生物信息学分析方法探讨急性髓系白血病(AML)发病的潜在关键基因和信号通路。方法以“acute myeloid leukemia”为检索关键词在GEO数据库查找符合要求的基因表达微阵列芯片数据,运用R语言Limma程序包进行差异表达基因的筛选,并用DAVID工具对筛选出的基因进行GO富集和KEGG信号通路途径分析,从中选取一些差异有统计学意义的差异表达基因,使用STRING数据库数据处理数据模型构建蛋白质-蛋白质作用网络,通过Cytoscape软件处理蛋白质网络图数据,筛选出核心基因,再进行GO富集和KEGG信号通路途径分析,对核心基因进行注释分析。结果共筛选出269个差异表达基因,其中含192个上调基因,77个下调基因。综合DAVID富集分析的结果,筛选出5个潜在关键基因(GNA11、GNA12、GNAS5、GNAS和PLCB2)。结论GNA11、GNA12、GNAS5、GNAS和PLCB2差异表达基因可能与AML发病机制密切相关,可为AML的发病机制、肿瘤标志物的筛选提供理论基础。

Objective To identify key genes in acute myeloid leukemia(AML)and investigate their potential pathways by using bioinformatics analysis methods through GEO database.Methods Using"acute myeloid leukemia"as the key word,the microarray data of gene expression meeting the requirements were searched in Gene Expression Omnibus(GEO)database.Differentially expressed genes between AML and normal controls were selected using the R language Limma package.GO enrichment and KEGG signaling pathway analysis of the selected genes were performed by the Database for Annotation,Visualization and Integrated Discovery(DAVID).Some differentially expressed genes with statistical differences were selected,and the protein-protein interaction network was constructed by using STRING data processing model.The protein network data was processed by Cytoscape software,and the core genes were selected.Then GO enrichment and KEGG signaling pathway analysis were performed to annotate the core genes.Results A total of 269 differentially expressed genes were selected,including 192 up-regulated genes and 77 down-regulated genes.Based on the results of DAVID enrichment analysis,five potential key genes were screened out(GNA11,GNA12,GNB5,GNAS and PLCB2).Conclusion GNA11,GNA12,GNB5,GNAS and PLCB2 might closely related to the pathogenesis of AML,which could provide a theoretical basis for screening tumor markers of pathogenesis of AML.