Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Chai, Keping, Zhang, Xiaolin, Chen, Shufang, Gu, Huaqian, Tang, Huitao, Cao, Panlong, Wang, Gangqiang, Ye, Weiping, Wan, Feng, Liang, Jiawei, Shen, Daojiang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
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
_version_ 1784757236579434496
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
work_keys_str_mv AT chaikeping applicationofweightedcoexpressionnetworkanalysisandmachinelearningtoidentifythepathologicalmechanismofalzheimersdisease
AT zhangxiaolin applicationofweightedcoexpressionnetworkanalysisandmachinelearningtoidentifythepathologicalmechanismofalzheimersdisease
AT chenshufang applicationofweightedcoexpressionnetworkanalysisandmachinelearningtoidentifythepathologicalmechanismofalzheimersdisease
AT guhuaqian applicationofweightedcoexpressionnetworkanalysisandmachinelearningtoidentifythepathologicalmechanismofalzheimersdisease
AT tanghuitao applicationofweightedcoexpressionnetworkanalysisandmachinelearningtoidentifythepathologicalmechanismofalzheimersdisease
AT caopanlong applicationofweightedcoexpressionnetworkanalysisandmachinelearningtoidentifythepathologicalmechanismofalzheimersdisease
AT wanggangqiang applicationofweightedcoexpressionnetworkanalysisandmachinelearningtoidentifythepathologicalmechanismofalzheimersdisease
AT yeweiping applicationofweightedcoexpressionnetworkanalysisandmachinelearningtoidentifythepathologicalmechanismofalzheimersdisease
AT wanfeng applicationofweightedcoexpressionnetworkanalysisandmachinelearningtoidentifythepathologicalmechanismofalzheimersdisease
AT liangjiawei applicationofweightedcoexpressionnetworkanalysisandmachinelearningtoidentifythepathologicalmechanismofalzheimersdisease
AT shendaojiang applicationofweightedcoexpressionnetworkanalysisandmachinelearningtoidentifythepathologicalmechanismofalzheimersdisease