Integrated Analysis of Weighted Gene Coexpression Network Analysis Identifying Six Genes as Novel Biomarkers for Alzheimer’s Disease

Abstract

Background. Alzheimer’s disease (AD) is a chronic progressive neurodegenerative disease; however, there are no comprehensive therapeutic interventions. Therefore, this study is aimed at identifying novel molecular targets that may improve the diagnosis and treatment of patients with AD. Methods. In our study, GSE5281 microarray dataset from the GEO database was collected and screened for differential expression analysis. Genes with a P value of <0.05 and ∣ log 2 FoldChange ∣ > 0.5 were considered differentially expressed genes (DEGs). We further profiled and identified AD-related coexpression genes using weighted gene coexpression network analysis (WGCNA). Functional enrichment analysis was performed to determine the characteristics and pathways of the key modules. We constructed an AD-related model based on hub genes by logistic regression and least absolute shrinkage and selection operator (LASSO) analyses, which was also verified by the receiver operating characteristic (ROC) curve. Results. In total, 4674 DEGs were identified. Nine distinct coexpression modules were identified via WGCNA; among these modules, the blue module showed the highest positive correlation with AD ( r = 0.64 , P = 3 e − 20 ), and it was visualized by establishing a protein–protein interaction network. Moreover, this module was particularly enriched in “pathways of neurodegeneration—multiple diseases,” “Alzheimer disease,” “oxidative phosphorylation,” and “proteasome.” Sixteen genes were identified as hub genes and further submitted to a LASSO regression model, and six genes (EIF3H, RAD51C, FAM162A, BLVRA, ATP6V1H, and BRAF) were identified based on the model index. Additionally, we assessed the accuracy of the LASSO model by plotting an ROC curve ( AUC = 0.940 ). Conclusions. Using the WGCNA and LASSO models, our findings provide a better understanding of the role of biomarkers EIF3H, RAD51C, FAM162A, BLVRA, ATP6V1H, and BRAF and provide a basis for further studies on AD progression.

Identification of Diagnostic Markers for Breast Cancer Based on Differential Gene Expression and Pathway Network

“…Previously, the use of bioinformatics methods to explore differentially expressed genes has made significant progress in pancreatic cancer ( Xu et al, 2020 ), gastric cancer ( Cheng et al, 2020 ; Gu et al, 2021 ; Yuan et al, 2021 ), colorectal cancer ( Cheng et al, 2021 ; Zhang and Zhang, 2021 ), prostate cancer ( Rode et al, 2021 ) and other diseases ( Ao et al, 2021 ; Yu et al, 2021b ; Laudisi et al, 2021 ; Zhang et al, 2021 ; Zhao et al, 2021 ). The occurrence and development of breast cancer is a process involving and synergistic action of multiple genes in multiple stages ( Kretschmer et al, 2011 ).…”

A Plasma Circular RNA Profile Differentiates Subjects with Alzheimer’s Disease and Mild Cognitive Impairment from Healthy Controls

“…ATP13A1, encoding for a lysosomal ATPase was correlated with Parkinson’s Disease [ 17 , 18 ]. BRAF mutations represent a diagnostic criterion for melanoma [ 19 ], but recently it was associated with AD [ 20 ]. PITRM1 has a role in mitochondrial dysfunction and neurodegeneration.…”

Comparative transcriptional analysis and identification of hub genes associated with wing differentiation of male in Aphis gossypii

“…The R package and its source code and additional material is freely available at https://horvath.genetics.ucla.edu/html/CoexpressionNetwork/Rpackages/WGCNA/. The experimental procedures refer to the previous method (Zhang et al, 2021). The resulting modules were represented and visualised using Cytoscape_v3.8.0 (http://www.cytoscape.org/).…”