基于GEO数据库对儿童急性淋巴细胞白血病差异基因的筛选和生物信息学分析

doi:10.3969/j.issn.1673-8640.2022.08.008

摘要/Abstract

摘要：

目的对急性淋巴细胞白血病（ALL）患儿和健康儿童间的差异表达基因进行生物信息学分析,寻找新的可用于ALL患儿辅助诊断的分子标志物。方法从基因表达综合数据库（GEO）下载新诊断的ALL患儿和健康儿童的基因芯片,利用R语言筛选出两者差异表达基因。利用DAVID数据库对差异表达基因进行基因本体（GO）分析和京都基因与基因组百科全书（KEGG）通路富集分析。基于STRING数据库构建差异基因蛋白质互作网络,通过Cytoscape软件对其结果进行可视化分析,并使用CytoHubba插件筛选关键基因。结果共纳入2个平台的新诊断ALL患儿和健康儿童基因芯片,筛选出2个平台共同差异表达基因245个,包括88个上调基因和157个下调基因。富集分析结果显示,共同差异表达基因主要集中在免疫反应（生物学过程）,整合在细胞外空间（细胞组成）,参与蛋白质结合（分子功能）,并富集在造血细胞谱系和细胞周期等信号通路（KEGG通路分析）。最终筛选出10个关键基因,分别为CDK1、TOP2A、TYMS、MCM2、MCM4、TTK、CCNB2、BUB1B、KIF4A和MAD2L1。结论通过对ALL患儿相关基因芯片数据的生物信息学分析发现的10个关键基因或可成为辅助诊断ALL新的生物标志物。

关键词: 急性淋巴细胞白血病, 差异表达基因, 关键基因, 生物信息学

Abstract:

Objective To analyze the differential expression genes between children with acute lymphoblastic leukemia（ALL） and healthy children by bioinformatics,and to find new molecular markers for the auxiliary diagnosis of ALL in children. Methods The gene chips of newly diagnosed children with ALL and healthy children was downloaded from the Gene Expression Omnibus（GEO）,and the differential expression genes were screened by R language. The Database for Annotation,Visualization and Integrated Discovery（DIVID） was employed for Gene Ontology（GO） and Kyoto Encyclopedia of Genes and Genomes（KEGG） pathway enrichment analysis. The protein interaction network of differential expression genes was constructed on STRING database,the results were visualized by Cytoscape software,and the hub genes were screened by CytoHubba plug-in. Results A total of 2 platforms' gene chips of newly diagnosed ALL children and healthy children were included,and 245 common differential expression genes were selected,which included 88 up-regulated genes and 157 down-regulated genes. Enrichment analysis showed that the common differential expression genes were mainly concentrated in the immune response（biological process）,integrating in the extracellular space（cell composition）,participating protein binding（molecular function）,and enriching hematopoietic cell lineage and cell cycle signal pathways（KEGG pathway analysis）. Totally,10 hub genes were screened out,namely CDK1,TOP2A,TYMS,MCM2,MCM4,TTK,CCNB2,BUB1B,KIF4A and MAD2L1. Conclusions The bioinformatics analysis of children with ALL reveals that 10 hub genes may be new markers for assisting the diagnosis of children with ALL.

Key words: Acute lymphoblastic leukemia, Differential expression gene, Hub gene, Bioinformatics

中图分类号:

R446.1

虞莉莎, 张盈盈. 基于GEO数据库对儿童急性淋巴细胞白血病差异基因的筛选和生物信息学分析[J]. 检验医学, 2022, 37(8): 745-750.

YU Lisha, ZHANG Yingying. Screening and bioinformatics analysis of differential genes in children with acute lymphoblastic leukemia based on GEO database[J]. Laboratory Medicine, 2022, 37(8): 745-750.

图/表 9

表1 新诊断ALL患儿和健康儿童基因芯片基本信息

GEO数据集	芯片平台号	ALL患儿例数	健康儿童例数	样本类型
GSE71935	GPL570		9	骨髓
GSE116486	GPL570		18	外周血
GSE26713	GPL570		7	骨髓
GSE41831	GPL570		15	外周血
GSE67684	GPL570	191		骨髓/外周血
	GPL96	19		骨髓/外周血
GSE8650	GPL96		21	外周血
GSE9006	GPL96		20	外周血

图1 GPL570平台ALL患儿与健康儿童差异表达基因

图2 GPL96平台ALL患儿与健康儿童差异表达基因火山图

图3 ALL患儿与健康儿童差异表达基因GO富集（生物学过程）

图4 ALL患儿与健康儿童差异表达基因GO富集（分子功能）

图5 ALL患儿与健康儿童差异表达基因GO富集（细胞组成）

图6 ALL患儿与健康儿童差异表达基因KEGG通路分析结果

图7 蛋白质互作网络关键基因簇分析

图8 关键基因

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