Application of Weighted Gene Co-Expression Network Analysis to Explore the Key Genes in Alzheimer’s Disease

• Published in: Journal of Alzheimer's disease Vol. 65; no. 4; pp. 1353 - 1364
• Main Authors: Liang, Jia-Wei, Fang, Zheng-Yu, Huang, Yong, Liuyang, Zhen-yu, Zhang, Xiao-Lin, Wang, Jing-Lin, Wei, Hui, Wang, Jian-Zhi, Wang, Xiao-Chuan, Zeng, Ji, Liu, Rong
• Format: Journal Article
• Language: English
• Published: London, England SAGE Publications 01.01.2018
• Subjects: Alzheimer Disease - genetics; Calmodulin-Binding Proteins - genetics; Female; Gene Expression Profiling; Gene Regulatory Networks; Genetic Predisposition to Disease - genetics; Humans; Male; Mental Status Schedule; Metallothionein - genetics; MSX1 Transcription Factor - genetics; rab GTP-Binding Proteins - genetics; Receptor, Notch2 - genetics; RNA, Messenger - metabolism; Systems Biology; RAB31; Alzheimer’s disease; Notch2; neurofibrillary tangles; metallothionein; MSX1; ADD3; WGCNA
• DOI: 10.3233/JAD-180400
• ISSN: 1387-2877

Background: Weighted co-expression network analysis (WGCNA) is a powerful systems biology method to describe the correlation of gene expression based on the microarray database, which can be used to facilitate the discovery of therapeutic targets or candidate biomarkers in diseases. Objective: To explore the key genes in the development of Alzheimer’s disease (AD) by using WGCNA. Methods: The whole gene expression data GSE1297 from AD and control human hippocampus was obtained from the GEO database in NCBI. Co-expressed genes were clustered into different modules. Modules of interest were identified through calculating the correlation coefficient between the module and phenotypic traits. GO and pathway enrichment analyses were conducted, and the central players (key hub genes) within the modules of interest were identified through network analysis. The expression of the identified key genes was confirmed in AD transgenic mice through using qRT-PCR. Results: Two modules were found to be associated with AD clinical severity, which functioning mainly in mineral absorption, NF-κB signaling, and cGMP-PKG signaling pathways. Through analysis of the two modules, we found that metallothionein (MT), Notch2, MSX1, ADD3, and RAB31 were highly correlated with AD phenotype. Increase in expression of these genes was confirmed in aged AD transgenic mice. Conclusion: WGCNA analysis can be used to analyze and predict the key genes in AD. MT1, MT2, MSX1, NOTCH2, ADD3, and RAB31 are identified to be the most relevant genes, which may be potential targets for AD therapy.