Cargando…
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...
Autores principales: | , , , , , , , , , , , |
---|---|
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 |
_version_ | 1784592508727066624 |
---|---|
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. |
format | Online Article Text |
id | pubmed-8558222 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT chaikeping applicationofmachinelearningandweightedgenecoexpressionnetworkalgorithmtoexplorethehubgenesintheagingbrain AT liangjiawei applicationofmachinelearningandweightedgenecoexpressionnetworkalgorithmtoexplorethehubgenesintheagingbrain AT zhangxiaolin applicationofmachinelearningandweightedgenecoexpressionnetworkalgorithmtoexplorethehubgenesintheagingbrain AT caopanlong applicationofmachinelearningandweightedgenecoexpressionnetworkalgorithmtoexplorethehubgenesintheagingbrain AT chenshufang applicationofmachinelearningandweightedgenecoexpressionnetworkalgorithmtoexplorethehubgenesintheagingbrain AT guhuaqian applicationofmachinelearningandweightedgenecoexpressionnetworkalgorithmtoexplorethehubgenesintheagingbrain AT yeweiping applicationofmachinelearningandweightedgenecoexpressionnetworkalgorithmtoexplorethehubgenesintheagingbrain AT liurong applicationofmachinelearningandweightedgenecoexpressionnetworkalgorithmtoexplorethehubgenesintheagingbrain AT huwenjun applicationofmachinelearningandweightedgenecoexpressionnetworkalgorithmtoexplorethehubgenesintheagingbrain AT pengcaixia applicationofmachinelearningandweightedgenecoexpressionnetworkalgorithmtoexplorethehubgenesintheagingbrain AT liuganglogan applicationofmachinelearningandweightedgenecoexpressionnetworkalgorithmtoexplorethehubgenesintheagingbrain AT shendaojiang applicationofmachinelearningandweightedgenecoexpressionnetworkalgorithmtoexplorethehubgenesintheagingbrain |