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 ∣log2FoldChange | >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 = 3e − 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.
Highlights
Alzheimer’s disease (AD), a chronic and progressive neurodegenerative disease, is one of the leading causes of dementia worldwide, accounting for an estimated prevalence of 60%– 80% of all cases [1]
The results indicate that with the biological process (BP) class mediator as a reference, 53 gene sets were significantly enriched in apoptotic processes involved in morphogenesis, enteric nervous system development, notochord development, mesodermal cell differentiation, and head morphogenesis in the AD samples (Figure 2(a) and Supplementary Table 1)
Compared with the control group, cellular component (CC) terms such as complex of collagen trimers, connexin complex, gap junction, lamellipodium membrane, and protein complex involved in cell adhesion were markedly enriched in the AD group (Figure 2(b))
Summary
Alzheimer’s disease (AD), a chronic and progressive neurodegenerative disease, is one of the leading causes of dementia worldwide, accounting for an estimated prevalence of 60%– 80% of all cases [1]. 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 = 3e − 20), and it was visualized by establishing a protein–protein interaction network. This module was 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. 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
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