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

ABSTRACT: 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-?B 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.

SUBMITTER: Liang JW

PROVIDER: S-EPMC6218130 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Project description:ObjectiveTo investigate the differential expression gene modules and hub genes associated with Alzheimer's disease (AD) by weighted gene co-expression network analysis (WGCNA) and annotate the biological functions of these modules.MethodsWe downloaded transcriptome sequencing data from the GEO database, and according to the correlation of the genes, a gene co-expression network was constructed with the parameter setting of β=8 and a correlation coefficient threshold of 0.85. Pearson correlation test was used to calculate the correlation between the module genes and clinical traits to screen the gene modules significantly associated with AD and identify the hub genes according to the connectivity within modules. GO functional enrichment analysis and KEGG pathway analysis were used to annotate the functions of the modules. A cell model of AD was established in SH-SY5Y cells by Aβ1-42 treatment, and the mRNA expression levels of the hub genes were compared between the Aβ1-42-treated cells and the control cells.ResultsTen gene co-expression modules were constructed based on the correlations of gene expression, in which the brown (r=0.66, P < 0.001) and turquoise modules (r=-0.68, P < 0.001) were significantly correlated with the AD group. Forty-eight genes were identified as the hub genes in the co-expression network. Function annotation revealed that the genes in both modules were mainly enriched in DNA damage and repair pathways and metabolism-related pathways. Differential expression analysis of the genes revealed that the genes DNASE1, TEKT2 and MTSS1L were highly expressed while ACP2, LANCL2 and GMPR2 were lowly expressed in AD group. The results of cell experiment confirmed the up-regulation of DNASE1, TEKT2 and MTSS1L genes and the down-regulation of ACP2, LANCL2, and GMPR2 in Aβ1-42-treated SH-SY5Y cells (P < 0.01).ConclusionThe brown and turquoise modules are closely correlated with AD. The hub genes including MTSS1L, GMPR2, ACP2, ACTG1 and LANCL2 selected from the modules may participate in AD pathogenesis by regulating DNA damage and repair.

Project description:Laryngeal squamous cell carcinoma (LSCC) is the most common malignant tumor in the head and neck, and can seriously affect the daily life of patients. To study the mechanisms of LSCC, the microarray of GSE51958 was analyzed in the present study. GSE51958 was downloaded from Gene Expression Omnibus, and included a collection of LSCC tissue samples and matched adjacent non-cancerous tissue samples from 10 patients. Differentially-expressed genes (DEGs) were identified using limma package. Next, a weighted co-expression network was constructed for the DEGs by WGCNA package in R. Modules of the weighted co-expression network were obtained through constructing a hierarchical clustering tree using the hybrid dynamic shear tree method. Using the clusterProfiler package, the potential functions of DEGs in the modules correlated with LSCC were predicted by pathway enrichment analysis. In total, 959 DEGs were screened from the LSCC samples compared with the adjacent non-cancerous samples, including 553 upregulated and 406 downregulated genes. The appointed black, brown, gray, pink and yellow modules were screened for the DEGs in the weighted co-expression network. For the DEGs in the brown and yellow modules, the enriched pathways were cytokine-cytokine receptor interaction and metabolic pathways, respectively. The DEGs in the pink module were involved in the majority of pathways. With high connectivity degrees in the pink module, TPX2, microtubule-associated (TPX2; degree, 25), minichromosome maintenance complex component 2 (MCM2; degree, 25), ubiquitin-like with PHD and ring finger domains 1 (UHRF1; degree, 22), cyclin-dependent kinase 2 (CDK2; degree, 20) and protein regulator of cytokinesis 1 (PRC1; degree, 20) may be involved in LSCC. Overall, In conclusion, from the integrated bioinformatics analysis of genes that may be associated with LSCC, 959 DEGs were obtained from LSCC samples compared with adjacent non-cancerous samples, and TPX2, MCM2, UHRF1, CDK2 and PRC1 were found to hold a possible association with the disease.

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

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