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Application of Machine Learning and Weighted Gene Co-expression Network Algorithm to Explore the Hub Genes in the Aging Brain

Aging is a major risk factor contributing to neurodegeneration and dementia. However, it remains unclarified how aging promotes these diseases. Here, we use machine learning and weighted gene co-expression network (WGCNA) to explore the relationship between aging and gene expression in the human fro...

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Autores principales: Chai, Keping, Liang, Jiawei, Zhang, Xiaolin, Cao, Panlong, Chen, Shufang, Gu, Huaqian, Ye, Weiping, Liu, Rong, Hu, Wenjun, Peng, Caixia, Liu, Gang Logan, Shen, Daojiang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8558222/
https://www.ncbi.nlm.nih.gov/pubmed/34733151
http://dx.doi.org/10.3389/fnagi.2021.707165
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author Chai, Keping
Liang, Jiawei
Zhang, Xiaolin
Cao, Panlong
Chen, Shufang
Gu, Huaqian
Ye, Weiping
Liu, Rong
Hu, Wenjun
Peng, Caixia
Liu, Gang Logan
Shen, Daojiang
author_facet Chai, Keping
Liang, Jiawei
Zhang, Xiaolin
Cao, Panlong
Chen, Shufang
Gu, Huaqian
Ye, Weiping
Liu, Rong
Hu, Wenjun
Peng, Caixia
Liu, Gang Logan
Shen, Daojiang
author_sort Chai, Keping
collection PubMed
description Aging is a major risk factor contributing to neurodegeneration and dementia. However, it remains unclarified how aging promotes these diseases. Here, we use machine learning and weighted gene co-expression network (WGCNA) to explore the relationship between aging and gene expression in the human frontal cortex and reveal potential biomarkers and therapeutic targets of neurodegeneration and dementia related to aging. The transcriptional profiling data of the human frontal cortex from individuals ranging from 26 to 106 years old was obtained from the GEO database in NCBI. Self-Organizing Feature Map (SOM) was conducted to find the clusters in which gene expressions downregulate with aging. For WGCNA analysis, first, co-expressed genes were clustered into different modules, and modules of interest were identified through calculating the correlation coefficient between the module and phenotypic trait (age). Next, the overlapping genes between differentially expressed genes (DEG, between young and aged group) and genes in the module of interest were discovered. Random Forest classifier was performed to obtain the most significant genes in the overlapping genes. The disclosed significant genes were further identified through network analysis. Through WGCNA analysis, the greenyellow module is found to be highly negatively correlated with age, and functions mainly in long-term potentiation and calcium signaling pathways. Through step-by-step filtering of the module genes by overlapping with downregulated DEGs in aged group and Random Forest classifier analysis, we found that MAPT, KLHDC3, RAP2A, RAP2B, ELAVL2, and SYN1 were co-expressed and highly correlated with aging.
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spelling pubmed-85582222021-11-02 Application of Machine Learning and Weighted Gene Co-expression Network Algorithm to Explore the Hub Genes in the Aging Brain Chai, Keping Liang, Jiawei Zhang, Xiaolin Cao, Panlong Chen, Shufang Gu, Huaqian Ye, Weiping Liu, Rong Hu, Wenjun Peng, Caixia Liu, Gang Logan Shen, Daojiang Front Aging Neurosci Neuroscience Aging is a major risk factor contributing to neurodegeneration and dementia. However, it remains unclarified how aging promotes these diseases. Here, we use machine learning and weighted gene co-expression network (WGCNA) to explore the relationship between aging and gene expression in the human frontal cortex and reveal potential biomarkers and therapeutic targets of neurodegeneration and dementia related to aging. The transcriptional profiling data of the human frontal cortex from individuals ranging from 26 to 106 years old was obtained from the GEO database in NCBI. Self-Organizing Feature Map (SOM) was conducted to find the clusters in which gene expressions downregulate with aging. For WGCNA analysis, first, co-expressed genes were clustered into different modules, and modules of interest were identified through calculating the correlation coefficient between the module and phenotypic trait (age). Next, the overlapping genes between differentially expressed genes (DEG, between young and aged group) and genes in the module of interest were discovered. Random Forest classifier was performed to obtain the most significant genes in the overlapping genes. The disclosed significant genes were further identified through network analysis. Through WGCNA analysis, the greenyellow module is found to be highly negatively correlated with age, and functions mainly in long-term potentiation and calcium signaling pathways. Through step-by-step filtering of the module genes by overlapping with downregulated DEGs in aged group and Random Forest classifier analysis, we found that MAPT, KLHDC3, RAP2A, RAP2B, ELAVL2, and SYN1 were co-expressed and highly correlated with aging. Frontiers Media S.A. 2021-10-18 /pmc/articles/PMC8558222/ /pubmed/34733151 http://dx.doi.org/10.3389/fnagi.2021.707165 Text en Copyright © 2021 Chai, Liang, Zhang, Cao, Chen, Gu, Ye, Liu, Hu, Peng, Liu 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 Neuroscience
Chai, Keping
Liang, Jiawei
Zhang, Xiaolin
Cao, Panlong
Chen, Shufang
Gu, Huaqian
Ye, Weiping
Liu, Rong
Hu, Wenjun
Peng, Caixia
Liu, Gang Logan
Shen, Daojiang
Application of Machine Learning and Weighted Gene Co-expression Network Algorithm to Explore the Hub Genes in the Aging Brain
title Application of Machine Learning and Weighted Gene Co-expression Network Algorithm to Explore the Hub Genes in the Aging Brain
title_full Application of Machine Learning and Weighted Gene Co-expression Network Algorithm to Explore the Hub Genes in the Aging Brain
title_fullStr Application of Machine Learning and Weighted Gene Co-expression Network Algorithm to Explore the Hub Genes in the Aging Brain
title_full_unstemmed Application of Machine Learning and Weighted Gene Co-expression Network Algorithm to Explore the Hub Genes in the Aging Brain
title_short Application of Machine Learning and Weighted Gene Co-expression Network Algorithm to Explore the Hub Genes in the Aging Brain
title_sort application of machine learning and weighted gene co-expression network algorithm to explore the hub genes in the aging brain
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8558222/
https://www.ncbi.nlm.nih.gov/pubmed/34733151
http://dx.doi.org/10.3389/fnagi.2021.707165
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