阿尔茨海默病患者外周血和海马组织共表达差异基因的生物信息学分析

Bioinformatics analysis of differentially co-expressed genes in peripheral blood and hippocampal tissue of patients with Alzheimer’s disease

摘要:

目的 基于生物信息学筛选阿尔茨海默病（Alzheimer's disease，AD）患者外周血和海马组织中有共同表达趋势的差异基因，为AD的诊断、治疗靶点的筛选提供新的思路。方法 从基因表达综合数据库（Gene Expression Omnibus，GEO）下载关于AD的数据集GSE97760（9例AD、10例健康对照）、GSE5281（10例AD、13例健康对照）进行生物信息学分析。分别筛选AD患者外周血和海马组织的差异表达基因（differentially expressed genes，DEGs），并确定2个数据集中有共同表达趋势的DEGs。对共表达DEGs进行基因本体论（Gene Ontology，GO）富集分析和京都基因与基因组百科全书（Kyoto Encyclopedia of Genes and Genomes，KEGG）通路分析，构建蛋白互作（protein-protein interaction，PPI）网络进一步筛选关键基因，并对关键基因进行验证。结果 基于对2个GEO数据集的综合分析，共筛选出669个共表达DEGs，包括64个上调DEGs、605个下调DEGs，GO和KEGG分析分别揭示了174个关键条目、40个关键通路，主要在RNA聚合酶的转录调控、神经胶质瘤通路等方面显著富集。通过PPI网络进一步筛选出10个与AD相关的关键基因TP53、PTEN、HNRNPC、EIF4G1、SF3B1、SRSF11、PIK3R1、RBM39、LUC7L3、RBM25，其中PTEN、HNRNPC、SF3B1、PIK3R1、LUC7L3在数据集GSE48350中得到了验证。结论 本研究筛选得出的5个值得进一步研究的关键基因（PTEN、HNRNPC、SF3B1、PIK3R1、LUC7L3），可作为AD诊断的潜在生物标志物。

Abstract:

Objective To determine differentially co-expressed genes in the peripheral blood and hippocampal tissue of patients with Alzheimer’s disease（AD） by bioinformatics analysis，and to provide new ideas for AD diagnosis and therapeutic target selection.Methods We downloaded the AD-associated datasets GSE97760（9 cases of AD and 10 healthy controls） and GSE5281（10 cases of AD and 13 healthy controls） from the Gene Expression Omnibus（GEO） database for bioinformatics analysis. We selected differentially expressed genes（DEGs） in the peripheral blood and hippocampal tissues of patients with AD separately；identified the co-expressed DEGs in the two datasets； then performed Gene Ontology（GO） analysis and Kyoto Encyclopedia of Genes and Genomes（KEGG） pathway analysis on the co-expressed DEGs；and constructed a protein-protein interaction（PPI） network to further determine the key genes，followed by validation.Results Through the comprehensive analysis of the two GEO datasets，a total of 669 co-expressed DEGs were selected，including 64 up-regulated DEGs and 605 down-regulated DEGs. The GO and KEGG analyses selected 174 key items and 40 key pathways，respectively，which were significantly enriched mainly in the transcriptional regulation of RNA polymerase and glioma pathway. The PPI network determined 10 key AD-related genes：TP53，PTEN，HNRNPC，EIF4G1，SF3B1，SRSF11，PIK3R1，RBM39，LUC7L3，and RBM25，of which PTEN，HNRNPC，SF3B1，PIK3R1，and LUC7L3 were validated in the dataset GSE48350.Conclusion The five key genes（PTEN，HNRNPC，SF3B1，PIK3R1，and LUC7L3） selected in this study may serve as potential biomarkers for AD diagnosis，which deserve further research.