Chinese Medical Sciences Journal ›› 2020, Vol. 35 ›› Issue (4): 330-341.doi: 10.24920/003695

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  1. 1广西中医药大学药学院,南宁 530021, 中国
    2徐州医科大学药学院,江苏省新药研究与临床药学重点实验室,江苏 徐州 221004,中国
  • 收稿日期:2019-12-09 接受日期:2020-05-06 出版日期:2020-12-31 发布日期:2020-09-28
  • 通讯作者: 钟振国

Identification of Potential Therapeutic Targets of Alzheimer’s Disease By Weighted Gene Co-Expression Network Analysis

Fan Zhang1,Siran Zhong1,Siman Yang1,Yuting Wei1,Jingjing Wang1,Jinlan Huang2,Dengpan Wu2,Zhenguo Zhong1,*()   

  1. 1Pharmacy School, Guangxi University of Chinese Medicine, Nanning 530200, China
    2Jiangsu Key Laboratory of New Drug Research and Clinical Pharmacy, Pharmacy School, Xuzhou Medical University, Xuzhou, Jiangsu 221004, China
  • Received:2019-12-09 Accepted:2020-05-06 Published:2020-12-31 Online:2020-09-28
  • Contact: Zhenguo Zhong


目的 阿尔茨海默病 (Alzheimer‘s disease,AD)是痴呆症最常见的病因,但其发病机制尚不明确。本研究拟通过加权基因共表达网络探究AD发病相关的核心靶点。
方法 GSE36980数据集下载于高通量基因表达数据库(Gene Expression Omnibus, GEO)。首先进行数据标准化,质控和过滤,以及计算软阈值,然后根据基因表达的相关性,聚类划分为不同的模块,通过计算各模块与临床特征的相关系数,确定关键基因模块。我们通过基因富集分析(Gene Ontology,GO)和通路富集分析(Kyoto Encyclopedia of Genes and Genomes, KEGG)探究关键基因模块中的基因功能。随后通过STRING数据库构建蛋白-蛋白相互作用网络,并使用Cytoscape软件MCODE插件进行网络拓扑分析筛选核心调控基因,最后使用GEO外部数据集GSE1297和GSE28146对核心基因进行验证。
结果 共表达基因共聚类27个模块,其中6个模块与AD发病显著相关,以此作为关键模块用于下游分析。通过基因功能富集分析发现关键模块与神经递质传递(GO:0007268)、三羧酸循环和电子传递链有关(R-HSA-1428517)。结合共表达网络和蛋白-蛋白相互作用网络的分析结果发现,基因WDR47,OXCT1,C3orf14,ATP6V1A,SLC25A14,NAPB可能与AD发病相关。外部数据集进一步证实了其差异表达。
结论 通过加权基因共表达网络分析和蛋白互作用网络分析,我们发现AD的核心基因WDR47OXCT1C3orf14ATP6V1ASLC25A14NAPB,其中ATP6V1A,SLC25A14,OXCT1可能通过影响三羧酸循环而影响AD的发病。

关键词: 生物信息学分析, 阿尔茨海默病, 三羧酸循环, 加权基因共表达网络分析, OXCT1, ATP6V1A


Objective Alzheimer’s disease (AD) is the most common cause of dementia. The pathophysiology of the disease mostly remains unearthed, thereby challenging drug development for AD. This study aims to screen high throughput gene expression data using weighted co-expression network analysis (WGCNA) to explore the potential therapeutic targets.
Methods The dataset of GSE36980 was obtained from the Gene Expression Omnibus (GEO) database. Normalization, quality control, filtration, and soft-threshold calculation were carried out before clustering the co-expressed genes into different modules. Furthermore, the correlation coefficients between the modules and clinical traits were computed to identify the key modules. Gene ontology and pathway enrichment analyses were performed on the key module genes. The STRING database was used to construct the protein-protein interaction (PPI) networks, which were further analyzed by Cytoscape app (MCODE). Finally, validation of hub genes was conducted by external GEO datasets of GSE 1297 and GSE 28146.
Results Co-expressed genes were clustered into 27 modules, among which 6 modules were identified as the key module relating to AD occurrence. These key modules are primarily involved in chemical synaptic transmission (GO:0007268), the tricarboxylic acid (TCA) cycle and respiratory electron transport (R-HSA-1428517). WDR47, OXCT1, C3orf14, ATP6V1A, SLC25A14, NAPB were found as the hub genes and their expression were validated by external datasets.
Conclusions Through modules co-expression network analyses and PPI network analyses, we identified the hub genes of AD, including WDR47, OXCT1, C3orf14, ATP6V1A, SLC25A14 and NAPB. Among them, three hub genes (ATP6V1A, SLC25A14, OXCT1) might contribute to AD pathogenesis through pathway of TCA cycle.

Key words: bioinformatics analysis, Alzheimer’s disease, Tricarboxylic acid (TCA) cycle, weighted gene co-expression network analysis, OXCT1, ATP6V1A

基金资助: 国家自然科学基金(81460598);国家自然科学基金(81660644);江苏省自然科学基金青年基金项目(BK20170267);广西一流学科专项经费项目(05019038)

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