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Screening and Identification of Potential Peripheral Blood Biomarkers for Alzheimer’s Disease Based on Bioinformatics Analysis

BACKGROUND: Alzheimer’s disease (AD) is the leading cause of dementia worldwide; however, the molecular mechanisms underlying its pathogenesis remain unclear. The present study aimed to discover some potential peripheral blood biomarkers for early detection of patients with AD. MATERIAL/METHODS: Pub...

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Autores principales: Wang, Xin, Wang, Lantao
Formato: Online Artículo Texto
Lenguaje:English
Publicado: International Scientific Literature, Inc. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7453750/
https://www.ncbi.nlm.nih.gov/pubmed/32812532
http://dx.doi.org/10.12659/MSM.924263
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author Wang, Xin
Wang, Lantao
author_facet Wang, Xin
Wang, Lantao
author_sort Wang, Xin
collection PubMed
description BACKGROUND: Alzheimer’s disease (AD) is the leading cause of dementia worldwide; however, the molecular mechanisms underlying its pathogenesis remain unclear. The present study aimed to discover some potential peripheral blood biomarkers for early detection of patients with AD. MATERIAL/METHODS: Publicly available AD datasets – GSE18309 and GSE97760 – were obtained from the Gene Expression Omnibus database, and limma package from Bioconductor was employed to search for differently expressed genes (DEGs). Weighted correlation network analysis was performed to identify DEGs with highly synergistic changes, and functional annotation of DEGs was performed using gene set enrichment analysis and Metascape. STRING and Cytoscape were used to construct protein-protein interaction networks and analyze the most significant hub genes. Thereafter, the Comparative Toxicogenomics Database (CTD) was used to identify hub genes associated with AD pathology, and Connectivity Map was used to screen small molecule drugs for AD. Finally, hub genes coupled with corresponding predicted miRNAs involved in AD were assessed via TargetScan, and functional annotation of predicted miRNAs was performed using DIANA database. RESULTS: Our analyses revealed 5042 DEGs; based on functional analyses, these DEGs were mainly associated with oligosaccharide lipid intermediate biosynthetic process, cyclin binding, signaling pathways regulating pluripotency of ubiquitin mediated proteolysis, and extracellular matrix-receptor interaction. UBB, UBA52, SRC, MMP9, VWF, GP6, and PF4 were identified as the hub genes. The CTD showed that these hub genes are closely related with AD or cognition impairment. CONCLUSIONS: The identified hub genes and corresponding miRNAs might be useful as potential peripheral blood biomarkers of AD.
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spelling pubmed-74537502020-09-03 Screening and Identification of Potential Peripheral Blood Biomarkers for Alzheimer’s Disease Based on Bioinformatics Analysis Wang, Xin Wang, Lantao Med Sci Monit Database Analysis BACKGROUND: Alzheimer’s disease (AD) is the leading cause of dementia worldwide; however, the molecular mechanisms underlying its pathogenesis remain unclear. The present study aimed to discover some potential peripheral blood biomarkers for early detection of patients with AD. MATERIAL/METHODS: Publicly available AD datasets – GSE18309 and GSE97760 – were obtained from the Gene Expression Omnibus database, and limma package from Bioconductor was employed to search for differently expressed genes (DEGs). Weighted correlation network analysis was performed to identify DEGs with highly synergistic changes, and functional annotation of DEGs was performed using gene set enrichment analysis and Metascape. STRING and Cytoscape were used to construct protein-protein interaction networks and analyze the most significant hub genes. Thereafter, the Comparative Toxicogenomics Database (CTD) was used to identify hub genes associated with AD pathology, and Connectivity Map was used to screen small molecule drugs for AD. Finally, hub genes coupled with corresponding predicted miRNAs involved in AD were assessed via TargetScan, and functional annotation of predicted miRNAs was performed using DIANA database. RESULTS: Our analyses revealed 5042 DEGs; based on functional analyses, these DEGs were mainly associated with oligosaccharide lipid intermediate biosynthetic process, cyclin binding, signaling pathways regulating pluripotency of ubiquitin mediated proteolysis, and extracellular matrix-receptor interaction. UBB, UBA52, SRC, MMP9, VWF, GP6, and PF4 were identified as the hub genes. The CTD showed that these hub genes are closely related with AD or cognition impairment. CONCLUSIONS: The identified hub genes and corresponding miRNAs might be useful as potential peripheral blood biomarkers of AD. International Scientific Literature, Inc. 2020-08-19 /pmc/articles/PMC7453750/ /pubmed/32812532 http://dx.doi.org/10.12659/MSM.924263 Text en © Med Sci Monit, 2020 This work is licensed under Creative Common Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0 (https://creativecommons.org/licenses/by-nc-nd/4.0/) )
spellingShingle Database Analysis
Wang, Xin
Wang, Lantao
Screening and Identification of Potential Peripheral Blood Biomarkers for Alzheimer’s Disease Based on Bioinformatics Analysis
title Screening and Identification of Potential Peripheral Blood Biomarkers for Alzheimer’s Disease Based on Bioinformatics Analysis
title_full Screening and Identification of Potential Peripheral Blood Biomarkers for Alzheimer’s Disease Based on Bioinformatics Analysis
title_fullStr Screening and Identification of Potential Peripheral Blood Biomarkers for Alzheimer’s Disease Based on Bioinformatics Analysis
title_full_unstemmed Screening and Identification of Potential Peripheral Blood Biomarkers for Alzheimer’s Disease Based on Bioinformatics Analysis
title_short Screening and Identification of Potential Peripheral Blood Biomarkers for Alzheimer’s Disease Based on Bioinformatics Analysis
title_sort screening and identification of potential peripheral blood biomarkers for alzheimer’s disease based on bioinformatics analysis
topic Database Analysis
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7453750/
https://www.ncbi.nlm.nih.gov/pubmed/32812532
http://dx.doi.org/10.12659/MSM.924263
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