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Application of weighted co-expression network analysis and machine learning to identify the pathological mechanism of Alzheimer's disease
Aberrant deposits of neurofibrillary tangles (NFT), the main characteristic of Alzheimer's disease (AD), are highly related to cognitive impairment. However, the pathological mechanism of NFT formation is still unclear. This study explored differences in gene expression patterns in multiple bra...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2022
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9326231/ https://www.ncbi.nlm.nih.gov/pubmed/35912089 http://dx.doi.org/10.3389/fnagi.2022.837770 |
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author | Chai, Keping Zhang, Xiaolin Chen, Shufang Gu, Huaqian Tang, Huitao Cao, Panlong Wang, Gangqiang Ye, Weiping Wan, Feng Liang, Jiawei Shen, Daojiang |
author_facet | Chai, Keping Zhang, Xiaolin Chen, Shufang Gu, Huaqian Tang, Huitao Cao, Panlong Wang, Gangqiang Ye, Weiping Wan, Feng Liang, Jiawei Shen, Daojiang |
author_sort | Chai, Keping |
collection | PubMed |
description | Aberrant deposits of neurofibrillary tangles (NFT), the main characteristic of Alzheimer's disease (AD), are highly related to cognitive impairment. However, the pathological mechanism of NFT formation is still unclear. This study explored differences in gene expression patterns in multiple brain regions [entorhinal, temporal, and frontal cortex (EC, TC, FC)] with distinct Braak stages (0- VI), and identified the hub genes via weighted gene co-expression network analysis (WGCNA) and machine learning. For WGCNA, consensus modules were detected and correlated with the single sample gene set enrichment analysis (ssGSEA) scores. Overlapping the differentially expressed genes (DEGs, Braak stages 0 vs. I-VI) with that in the interest module, metascape analysis, and Random Forest were conducted to explore the function of overlapping genes and obtain the most significant genes. We found that the three brain regions have high similarities in the gene expression pattern and that oxidative damage plays a vital role in NFT formation via machine learning. Through further filtering of genes from interested modules by Random Forest, we screened out key genes, such as LYN, LAPTM5, and IFI30. These key genes, including LYN, LAPTM5, and ARHGDIB, may play an important role in the development of AD through the inflammatory response pathway mediated by microglia. |
format | Online Article Text |
id | pubmed-9326231 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-93262312022-07-28 Application of weighted co-expression network analysis and machine learning to identify the pathological mechanism of Alzheimer's disease Chai, Keping Zhang, Xiaolin Chen, Shufang Gu, Huaqian Tang, Huitao Cao, Panlong Wang, Gangqiang Ye, Weiping Wan, Feng Liang, Jiawei Shen, Daojiang Front Aging Neurosci Aging Neuroscience Aberrant deposits of neurofibrillary tangles (NFT), the main characteristic of Alzheimer's disease (AD), are highly related to cognitive impairment. However, the pathological mechanism of NFT formation is still unclear. This study explored differences in gene expression patterns in multiple brain regions [entorhinal, temporal, and frontal cortex (EC, TC, FC)] with distinct Braak stages (0- VI), and identified the hub genes via weighted gene co-expression network analysis (WGCNA) and machine learning. For WGCNA, consensus modules were detected and correlated with the single sample gene set enrichment analysis (ssGSEA) scores. Overlapping the differentially expressed genes (DEGs, Braak stages 0 vs. I-VI) with that in the interest module, metascape analysis, and Random Forest were conducted to explore the function of overlapping genes and obtain the most significant genes. We found that the three brain regions have high similarities in the gene expression pattern and that oxidative damage plays a vital role in NFT formation via machine learning. Through further filtering of genes from interested modules by Random Forest, we screened out key genes, such as LYN, LAPTM5, and IFI30. These key genes, including LYN, LAPTM5, and ARHGDIB, may play an important role in the development of AD through the inflammatory response pathway mediated by microglia. Frontiers Media S.A. 2022-07-13 /pmc/articles/PMC9326231/ /pubmed/35912089 http://dx.doi.org/10.3389/fnagi.2022.837770 Text en Copyright © 2022 Chai, Zhang, Chen, Gu, Tang, Cao, Wang, Ye, Wan, Liang and Shen. 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 Chai, Keping Zhang, Xiaolin Chen, Shufang Gu, Huaqian Tang, Huitao Cao, Panlong Wang, Gangqiang Ye, Weiping Wan, Feng Liang, Jiawei Shen, Daojiang Application of weighted co-expression network analysis and machine learning to identify the pathological mechanism of Alzheimer's disease |
title | Application of weighted co-expression network analysis and machine learning to identify the pathological mechanism of Alzheimer's disease |
title_full | Application of weighted co-expression network analysis and machine learning to identify the pathological mechanism of Alzheimer's disease |
title_fullStr | Application of weighted co-expression network analysis and machine learning to identify the pathological mechanism of Alzheimer's disease |
title_full_unstemmed | Application of weighted co-expression network analysis and machine learning to identify the pathological mechanism of Alzheimer's disease |
title_short | Application of weighted co-expression network analysis and machine learning to identify the pathological mechanism of Alzheimer's disease |
title_sort | application of weighted co-expression network analysis and machine learning to identify the pathological mechanism of alzheimer's disease |
topic | Aging Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9326231/ https://www.ncbi.nlm.nih.gov/pubmed/35912089 http://dx.doi.org/10.3389/fnagi.2022.837770 |
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