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Integrated bioinformatics-based identification of diagnostic markers in Alzheimer disease

Alzheimer disease (AD) is a progressive neurodegenerative disease resulting from the accumulation of extracellular amyloid beta (Aβ) and intracellular neurofibrillary tangles. There are currently no objective diagnostic measures for AD. The aim of this study was to identify potential diagnostic mark...

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Autores principales: Chen, Danmei, Zhang, Yunpeng, Qiao, Rui, Kong, Xiangyu, Zhong, Hequan, Wang, Xiaokun, Zhu, Jie, Li, Bing
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9686423/
https://www.ncbi.nlm.nih.gov/pubmed/36437991
http://dx.doi.org/10.3389/fnagi.2022.988143
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author Chen, Danmei
Zhang, Yunpeng
Qiao, Rui
Kong, Xiangyu
Zhong, Hequan
Wang, Xiaokun
Zhu, Jie
Li, Bing
author_facet Chen, Danmei
Zhang, Yunpeng
Qiao, Rui
Kong, Xiangyu
Zhong, Hequan
Wang, Xiaokun
Zhu, Jie
Li, Bing
author_sort Chen, Danmei
collection PubMed
description Alzheimer disease (AD) is a progressive neurodegenerative disease resulting from the accumulation of extracellular amyloid beta (Aβ) and intracellular neurofibrillary tangles. There are currently no objective diagnostic measures for AD. The aim of this study was to identify potential diagnostic markers for AD and evaluate the role of immune cell infiltration in disease pathogenesis. AD expression profiling data for human hippocampus tissue (GSE48350 and GSE5281) were downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were identified using R software and the Human Protein Atlas database was used to screen AD-related DEGs. We performed functional enrichment analysis and established a protein–protein interaction (PPI) network to identify disease-related hub DEGs. The fraction of infiltrating immune cells in samples was determined with the Microenvironment Cell Populations-counter method. The random forest algorithm was used to develop a prediction model and receiver operating characteristic (ROC) curve analysis was performed to validate the diagnostic utility of the candidate AD markers. The correlation between expression of the diagnostic markers and immune cell infiltration was also analyzed. A total of 107 AD-related DEGs were screened in this study, including 28 that were upregulated and 79 that were downregulated. The DEGs were enriched in the Gene Ontology terms GABAergic synapse, Morphine addiction, Nicotine addiction, Phagosome, and Synaptic vesicle cycle. We identified 10 disease-related hub genes and 20 candidate diagnostic genes. Synaptophysin (SYP) and regulator of G protein signaling 4 (RGS4) (area under the ROC curve = 0.909) were verified as potential diagnostic markers for AD in the GSE28146 validation dataset. Natural killer cells, B lineage cells, monocytic lineage cells, endothelial cells, and fibroblasts were found to be involved in AD; additionally, the expression levels of both SYP and RGS4 were negatively correlated with the infiltration of these immune cell types. These results suggest that SYP and RGS4 are potential diagnostic markers for AD and that immune cell infiltration plays an important role in AD development and progression.
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spelling pubmed-96864232022-11-25 Integrated bioinformatics-based identification of diagnostic markers in Alzheimer disease Chen, Danmei Zhang, Yunpeng Qiao, Rui Kong, Xiangyu Zhong, Hequan Wang, Xiaokun Zhu, Jie Li, Bing Front Aging Neurosci Aging Neuroscience Alzheimer disease (AD) is a progressive neurodegenerative disease resulting from the accumulation of extracellular amyloid beta (Aβ) and intracellular neurofibrillary tangles. There are currently no objective diagnostic measures for AD. The aim of this study was to identify potential diagnostic markers for AD and evaluate the role of immune cell infiltration in disease pathogenesis. AD expression profiling data for human hippocampus tissue (GSE48350 and GSE5281) were downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were identified using R software and the Human Protein Atlas database was used to screen AD-related DEGs. We performed functional enrichment analysis and established a protein–protein interaction (PPI) network to identify disease-related hub DEGs. The fraction of infiltrating immune cells in samples was determined with the Microenvironment Cell Populations-counter method. The random forest algorithm was used to develop a prediction model and receiver operating characteristic (ROC) curve analysis was performed to validate the diagnostic utility of the candidate AD markers. The correlation between expression of the diagnostic markers and immune cell infiltration was also analyzed. A total of 107 AD-related DEGs were screened in this study, including 28 that were upregulated and 79 that were downregulated. The DEGs were enriched in the Gene Ontology terms GABAergic synapse, Morphine addiction, Nicotine addiction, Phagosome, and Synaptic vesicle cycle. We identified 10 disease-related hub genes and 20 candidate diagnostic genes. Synaptophysin (SYP) and regulator of G protein signaling 4 (RGS4) (area under the ROC curve = 0.909) were verified as potential diagnostic markers for AD in the GSE28146 validation dataset. Natural killer cells, B lineage cells, monocytic lineage cells, endothelial cells, and fibroblasts were found to be involved in AD; additionally, the expression levels of both SYP and RGS4 were negatively correlated with the infiltration of these immune cell types. These results suggest that SYP and RGS4 are potential diagnostic markers for AD and that immune cell infiltration plays an important role in AD development and progression. Frontiers Media S.A. 2022-11-10 /pmc/articles/PMC9686423/ /pubmed/36437991 http://dx.doi.org/10.3389/fnagi.2022.988143 Text en Copyright © 2022 Chen, Zhang, Qiao, Kong, Zhong, Wang, Zhu 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 Aging Neuroscience
Chen, Danmei
Zhang, Yunpeng
Qiao, Rui
Kong, Xiangyu
Zhong, Hequan
Wang, Xiaokun
Zhu, Jie
Li, Bing
Integrated bioinformatics-based identification of diagnostic markers in Alzheimer disease
title Integrated bioinformatics-based identification of diagnostic markers in Alzheimer disease
title_full Integrated bioinformatics-based identification of diagnostic markers in Alzheimer disease
title_fullStr Integrated bioinformatics-based identification of diagnostic markers in Alzheimer disease
title_full_unstemmed Integrated bioinformatics-based identification of diagnostic markers in Alzheimer disease
title_short Integrated bioinformatics-based identification of diagnostic markers in Alzheimer disease
title_sort integrated bioinformatics-based identification of diagnostic markers in alzheimer disease
topic Aging Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9686423/
https://www.ncbi.nlm.nih.gov/pubmed/36437991
http://dx.doi.org/10.3389/fnagi.2022.988143
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