Analysis of potential genetic biomarkers and molecular mechanism of smoking-related postmenopausal osteoporosis using weighted gene co-expression network analysis and machine learning.

<h4>Objectives</h4>Smoking is a significant independent risk factor for postmenopausal osteoporosis, leading to genome variations in postmenopausal smokers. This study investigates potential biomarkers and molecular mechanisms of smoking-related postmenopausal osteoporosis (SRPO).<h4&...

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Autores principales:	Shaoshuo Li, Baixing Chen, Hao Chen, Zhen Hua, Yang Shao, Heng Yin, Jianwei Wang
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/ab212f14ab54413c987dc16367015e88
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spelling	oai:doaj.org-article:ab212f14ab54413c987dc16367015e882021-12-02T20:06:10ZAnalysis of potential genetic biomarkers and molecular mechanism of smoking-related postmenopausal osteoporosis using weighted gene co-expression network analysis and machine learning.1932-620310.1371/journal.pone.0257343https://doaj.org/article/ab212f14ab54413c987dc16367015e882021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0257343https://doaj.org/toc/1932-6203<h4>Objectives</h4>Smoking is a significant independent risk factor for postmenopausal osteoporosis, leading to genome variations in postmenopausal smokers. This study investigates potential biomarkers and molecular mechanisms of smoking-related postmenopausal osteoporosis (SRPO).<h4>Materials and methods</h4>The GSE13850 microarray dataset was downloaded from Gene Expression Omnibus (GEO). Gene modules associated with SRPO were identified using weighted gene co-expression network analysis (WGCNA), protein-protein interaction (PPI) analysis, and pathway and functional enrichment analyses. Feature genes were selected using two machine learning methods: support vector machine-recursive feature elimination (SVM-RFE) and random forest (RF). The diagnostic efficiency of the selected genes was assessed by gene expression analysis and receiver operating characteristic curve.<h4>Results</h4>Eight highly conserved modules were detected in the WGCNA network, and the genes in the module that was strongly correlated with SRPO were used for constructing the PPI network. A total of 113 hub genes were identified in the core network using topological network analysis. Enrichment analysis results showed that hub genes were closely associated with the regulation of RNA transcription and translation, ATPase activity, and immune-related signaling. Six genes (HNRNPC, PFDN2, PSMC5, RPS16, TCEB2, and UBE2V2) were selected as genetic biomarkers for SRPO by integrating the feature selection of SVM-RFE and RF.<h4>Conclusion</h4>The present study identified potential genetic biomarkers and provided a novel insight into the underlying molecular mechanism of SRPO.Shaoshuo LiBaixing ChenHao ChenZhen HuaYang ShaoHeng YinJianwei WangPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 9, p e0257343 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Shaoshuo Li Baixing Chen Hao Chen Zhen Hua Yang Shao Heng Yin Jianwei Wang Analysis of potential genetic biomarkers and molecular mechanism of smoking-related postmenopausal osteoporosis using weighted gene co-expression network analysis and machine learning.
description	<h4>Objectives</h4>Smoking is a significant independent risk factor for postmenopausal osteoporosis, leading to genome variations in postmenopausal smokers. This study investigates potential biomarkers and molecular mechanisms of smoking-related postmenopausal osteoporosis (SRPO).<h4>Materials and methods</h4>The GSE13850 microarray dataset was downloaded from Gene Expression Omnibus (GEO). Gene modules associated with SRPO were identified using weighted gene co-expression network analysis (WGCNA), protein-protein interaction (PPI) analysis, and pathway and functional enrichment analyses. Feature genes were selected using two machine learning methods: support vector machine-recursive feature elimination (SVM-RFE) and random forest (RF). The diagnostic efficiency of the selected genes was assessed by gene expression analysis and receiver operating characteristic curve.<h4>Results</h4>Eight highly conserved modules were detected in the WGCNA network, and the genes in the module that was strongly correlated with SRPO were used for constructing the PPI network. A total of 113 hub genes were identified in the core network using topological network analysis. Enrichment analysis results showed that hub genes were closely associated with the regulation of RNA transcription and translation, ATPase activity, and immune-related signaling. Six genes (HNRNPC, PFDN2, PSMC5, RPS16, TCEB2, and UBE2V2) were selected as genetic biomarkers for SRPO by integrating the feature selection of SVM-RFE and RF.<h4>Conclusion</h4>The present study identified potential genetic biomarkers and provided a novel insight into the underlying molecular mechanism of SRPO.
format	article
author	Shaoshuo Li Baixing Chen Hao Chen Zhen Hua Yang Shao Heng Yin Jianwei Wang
author_facet	Shaoshuo Li Baixing Chen Hao Chen Zhen Hua Yang Shao Heng Yin Jianwei Wang
author_sort	Shaoshuo Li
title	Analysis of potential genetic biomarkers and molecular mechanism of smoking-related postmenopausal osteoporosis using weighted gene co-expression network analysis and machine learning.
title_short	Analysis of potential genetic biomarkers and molecular mechanism of smoking-related postmenopausal osteoporosis using weighted gene co-expression network analysis and machine learning.
title_full	Analysis of potential genetic biomarkers and molecular mechanism of smoking-related postmenopausal osteoporosis using weighted gene co-expression network analysis and machine learning.
title_fullStr	Analysis of potential genetic biomarkers and molecular mechanism of smoking-related postmenopausal osteoporosis using weighted gene co-expression network analysis and machine learning.
title_full_unstemmed	Analysis of potential genetic biomarkers and molecular mechanism of smoking-related postmenopausal osteoporosis using weighted gene co-expression network analysis and machine learning.
title_sort	analysis of potential genetic biomarkers and molecular mechanism of smoking-related postmenopausal osteoporosis using weighted gene co-expression network analysis and machine learning.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/ab212f14ab54413c987dc16367015e88
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Analysis of potential genetic biomarkers and molecular mechanism of smoking-related postmenopausal osteoporosis using weighted gene co-expression network analysis and machine learning.

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