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Unearthing of Key Genes Driving the Pathogenesis of Alzheimer’s Disease via Bioinformatics

Alzheimer’s disease (AD) is a neurodegenerative disease with unelucidated molecular pathogenesis. Herein, we aimed to identify potential hub genes governing the pathogenesis of AD. The AD datasets of GSE118553 and GSE131617 were collected from the NCBI GEO database. The weighted gene coexpression ne...

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Autores principales: Zhao, Xingxing, Yao, Hongmei, Li, Xinyi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8085575/
https://www.ncbi.nlm.nih.gov/pubmed/33936168
http://dx.doi.org/10.3389/fgene.2021.641100
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author Zhao, Xingxing
Yao, Hongmei
Li, Xinyi
author_facet Zhao, Xingxing
Yao, Hongmei
Li, Xinyi
author_sort Zhao, Xingxing
collection PubMed
description Alzheimer’s disease (AD) is a neurodegenerative disease with unelucidated molecular pathogenesis. Herein, we aimed to identify potential hub genes governing the pathogenesis of AD. The AD datasets of GSE118553 and GSE131617 were collected from the NCBI GEO database. The weighted gene coexpression network analysis (WGCNA), differential gene expression analysis, and functional enrichment analysis were performed to reveal the hub genes and verify their role in AD. Hub genes were validated by machine learning algorithms. We identified modules and their corresponding hub genes from the temporal cortex (TC), frontal cortex (FC), entorhinal cortex (EC), and cerebellum (CE). We obtained 33, 42, 42, and 41 hub genes in modules associated with AD in TC, FC, EC, and CE tissues, respectively. Significant differences were recorded in the expression levels of hub genes between AD and the control group in the TC and EC tissues (P < 0.05). The differences in the expressions of FCGRT, SLC1A3, PTN, PTPRZ1, and PON2 in the FC and CE tissues among the AD and control groups were significant (P < 0.05). The expression levels of PLXNB1, GRAMD3, and GJA1 were statistically significant between the Braak NFT stages of AD. Overall, our study uncovered genes that may be involved in AD pathogenesis and revealed their potential for the development of AD biomarkers and appropriate AD therapeutics targets.
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spelling pubmed-80855752021-05-01 Unearthing of Key Genes Driving the Pathogenesis of Alzheimer’s Disease via Bioinformatics Zhao, Xingxing Yao, Hongmei Li, Xinyi Front Genet Genetics Alzheimer’s disease (AD) is a neurodegenerative disease with unelucidated molecular pathogenesis. Herein, we aimed to identify potential hub genes governing the pathogenesis of AD. The AD datasets of GSE118553 and GSE131617 were collected from the NCBI GEO database. The weighted gene coexpression network analysis (WGCNA), differential gene expression analysis, and functional enrichment analysis were performed to reveal the hub genes and verify their role in AD. Hub genes were validated by machine learning algorithms. We identified modules and their corresponding hub genes from the temporal cortex (TC), frontal cortex (FC), entorhinal cortex (EC), and cerebellum (CE). We obtained 33, 42, 42, and 41 hub genes in modules associated with AD in TC, FC, EC, and CE tissues, respectively. Significant differences were recorded in the expression levels of hub genes between AD and the control group in the TC and EC tissues (P < 0.05). The differences in the expressions of FCGRT, SLC1A3, PTN, PTPRZ1, and PON2 in the FC and CE tissues among the AD and control groups were significant (P < 0.05). The expression levels of PLXNB1, GRAMD3, and GJA1 were statistically significant between the Braak NFT stages of AD. Overall, our study uncovered genes that may be involved in AD pathogenesis and revealed their potential for the development of AD biomarkers and appropriate AD therapeutics targets. Frontiers Media S.A. 2021-04-16 /pmc/articles/PMC8085575/ /pubmed/33936168 http://dx.doi.org/10.3389/fgene.2021.641100 Text en Copyright © 2021 Zhao, Yao and Li. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Genetics
Zhao, Xingxing
Yao, Hongmei
Li, Xinyi
Unearthing of Key Genes Driving the Pathogenesis of Alzheimer’s Disease via Bioinformatics
title Unearthing of Key Genes Driving the Pathogenesis of Alzheimer’s Disease via Bioinformatics
title_full Unearthing of Key Genes Driving the Pathogenesis of Alzheimer’s Disease via Bioinformatics
title_fullStr Unearthing of Key Genes Driving the Pathogenesis of Alzheimer’s Disease via Bioinformatics
title_full_unstemmed Unearthing of Key Genes Driving the Pathogenesis of Alzheimer’s Disease via Bioinformatics
title_short Unearthing of Key Genes Driving the Pathogenesis of Alzheimer’s Disease via Bioinformatics
title_sort unearthing of key genes driving the pathogenesis of alzheimer’s disease via bioinformatics
topic Genetics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8085575/
https://www.ncbi.nlm.nih.gov/pubmed/33936168
http://dx.doi.org/10.3389/fgene.2021.641100
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