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

• Original Article • Previous Articles     Next Articles

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
The author screened high throughput gene expression data using WGCNA, identified 6 co-expression gene sets as the key module relating to AD; the ATP6V1A, SLC25A14 and OXCT1 were concluded as hub genes that contribute to AD pathogenesis through pathway of tricarboxylic acid (TCA) cycle. These results shed a light on the role of TCA cycle in AD pathogenesis and novel therapeutic targets for AD.

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

Funding: National Natural Science Foundation of China(81460598);National Natural Science Foundation of China(81660644);the Natural Science Foundation of Jiangsu Province(BK20170267);Guangxi Special Fund for the First-Class Discipline Construction Project(05019038)

Copyright © 2021 Chinese Academy of Medical Sciences.  京公安备110402430088  京ICP备06002729号-1  Powered by Magtech.

Supervised by National Health Commission of the People's Republic of China

9 Dongdan Santiao, Dongcheng district, Beijing, 100730 China

Tel: 86-10-65105897  Fax:86-10-65133074 


Copyright © 2018 Chinese Academy of Medical Sciences

All right reserved.

京公安备110402430088  京ICP备06002729号-1