基于生物信息学方法的阿尔茨海默病相关差异表达基因分析
Analysis of Differentially Expressed Genes Associated With Alzheimer's Disease Based on Bioinformatics Methods.
作者信息
Feng Bo, Hu Peng, Chen Jinbo, Liu Qingxin, Li Xizhi, Du Yifeng
机构信息
Department of Neurology, Provincial Hospital Affiliated to Shandong University, Jinan, Shandong PR China Department of Neurology, Hospital Affiliated to Binzhou medical University, Binzhou, PR China.
Department of Spine, Hospital Affiliated to Binzhou medical University, Binzhou, PR China.
出版信息
Am J Alzheimers Dis Other Demen. 2015 Dec;30(8):746-51. doi: 10.1177/1533317514537548. Epub 2014 Jun 24.
OBJECTIVE
To screen differentially expressed genes (DEGs) of Alzheimer's disease (AD).
METHODS
The gene expression profile (GSE26972) of AD was downloaded from Gene Expression Omnibus database. The DEGs were mapped to protein-protein interaction (PPI) data for acquiring the potential PPI relationship. The coexpressed significance of a gene pair in AD was determined. Then significantly enriched Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways of DEGs were analyzed based on database for annotation visualization and integrated discovery tool.
RESULTS
The PPI network showed 7 upregulated genes and 4 downregulated genes that might play meaningful functional roles in AD. Meanwhile, 3 significantly enriched KEGG pathways as well as several significant GO terms (included α-actinin binding, interleukin 33 receptor activity, and telethonin binding) were identified.
CONCLUSIONS
The screened DEGs have the potential to become candidate target molecules to monitor, diagnose, and treat AD.
目的
筛选阿尔茨海默病（AD）的差异表达基因（DEGs）。
方法
从基因表达综合数据库下载AD的基因表达谱（GSE26972）。将DEGs映射到蛋白质-蛋白质相互作用（PPI）数据以获取潜在的PPI关系。确定AD中基因对的共表达显著性。然后基于注释可视化和综合发现工具数据库分析DEGs显著富集的基因本体（GO）术语和京都基因与基因组百科全书（KEGG）通路。
结果
PPI网络显示7个上调基因和4个下调基因可能在AD中发挥有意义的功能作用。同时，鉴定出3条显著富集的KEGG通路以及几个显著的GO术语（包括α-辅肌动蛋白结合、白细胞介素33受体活性和肌联蛋白结合）。
结论
筛选出的DEGs有潜力成为监测、诊断和治疗AD的候选靶分子。
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