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

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...

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Autores principales: Zhang, Tingting, Liu, Nanyang, Wei, Wei, Zhang, Zhen, Li, Hao
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8339876/
https://www.ncbi.nlm.nih.gov/pubmed/34367470
http://dx.doi.org/10.1155/2021/9918498
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author Zhang, Tingting
Liu, Nanyang
Wei, Wei
Zhang, Zhen
Li, Hao
author_facet Zhang, Tingting
Liu, Nanyang
Wei, Wei
Zhang, Zhen
Li, Hao
author_sort Zhang, Tingting
collection PubMed
description 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.
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spelling pubmed-83398762021-08-06 Integrated Analysis of Weighted Gene Coexpression Network Analysis Identifying Six Genes as Novel Biomarkers for Alzheimer's Disease Zhang, Tingting Liu, Nanyang Wei, Wei Zhang, Zhen Li, Hao Oxid Med Cell Longev Research Article 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. Hindawi 2021-07-26 /pmc/articles/PMC8339876/ /pubmed/34367470 http://dx.doi.org/10.1155/2021/9918498 Text en Copyright © 2021 Tingting Zhang et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Zhang, Tingting
Liu, Nanyang
Wei, Wei
Zhang, Zhen
Li, Hao
Integrated Analysis of Weighted Gene Coexpression Network Analysis Identifying Six Genes as Novel Biomarkers for Alzheimer's Disease
title Integrated Analysis of Weighted Gene Coexpression Network Analysis Identifying Six Genes as Novel Biomarkers for Alzheimer's Disease
title_full Integrated Analysis of Weighted Gene Coexpression Network Analysis Identifying Six Genes as Novel Biomarkers for Alzheimer's Disease
title_fullStr Integrated Analysis of Weighted Gene Coexpression Network Analysis Identifying Six Genes as Novel Biomarkers for Alzheimer's Disease
title_full_unstemmed Integrated Analysis of Weighted Gene Coexpression Network Analysis Identifying Six Genes as Novel Biomarkers for Alzheimer's Disease
title_short Integrated Analysis of Weighted Gene Coexpression Network Analysis Identifying Six Genes as Novel Biomarkers for Alzheimer's Disease
title_sort integrated analysis of weighted gene coexpression network analysis identifying six genes as novel biomarkers for alzheimer's disease
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8339876/
https://www.ncbi.nlm.nih.gov/pubmed/34367470
http://dx.doi.org/10.1155/2021/9918498
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