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Identification of gene biomarkers in patients with postmenopausal osteoporosis

Postmenopausal osteoporosis (PMOP) is a major public health concern worldwide. The present study aimed to provide evidence to assist in the development of specific novel biomarkers for PMOP. Differentially expressed genes (DEGs) were identified between PMOP and normal controls by integrated microarr...

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Autores principales: Yang, Chenggang, Ren, Jing, Li, Bangling, Jin, Chuandi, Ma, Cui, Cheng, Cheng, Sun, Yaolan, Shi, Xiaofeng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: D.A. Spandidos 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6323213/
https://www.ncbi.nlm.nih.gov/pubmed/30569177
http://dx.doi.org/10.3892/mmr.2018.9752
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author Yang, Chenggang
Ren, Jing
Li, Bangling
Jin, Chuandi
Ma, Cui
Cheng, Cheng
Sun, Yaolan
Shi, Xiaofeng
author_facet Yang, Chenggang
Ren, Jing
Li, Bangling
Jin, Chuandi
Ma, Cui
Cheng, Cheng
Sun, Yaolan
Shi, Xiaofeng
author_sort Yang, Chenggang
collection PubMed
description Postmenopausal osteoporosis (PMOP) is a major public health concern worldwide. The present study aimed to provide evidence to assist in the development of specific novel biomarkers for PMOP. Differentially expressed genes (DEGs) were identified between PMOP and normal controls by integrated microarray analyses of the Gene Expression Omnibus (GEO) database, and the optimal diagnostic gene biomarkers for PMOP were identified with LASSO and Boruta algorithms. Classification models, including support vector machine (SVM), decision tree and random forests models, were established to test the diagnostic value of identified gene biomarkers for PMOP. Functional annotations and protein-protein interaction (PPI) network constructions were also conducted. Integrated microarray analyses (GSE56815, GSE13850 and GSE7429) of the GEO database were employed, and 1,320 DEGs were identified between PMOP and normal controls. An 11-gene combination was also identified as an optimal biomarker for PMOP by feature selection and classification methods using SVM, decision tree and random forest models. This combination was comprised of the following genes: Dehydrogenase E1 and transketolase domain containing 1 (DHTKD1), osteoclast stimulating factor 1 (OSTF1), G protein-coupled receptor 116 (GPR116), BCL2 interacting killer, adrenoceptor β1 (ADRB1), neogenin 1 (NEO1), RB binding protein 4 (RBBP4), GPR87, cylicin 2, EF-hand calcium binding domain 1 and DEAH-box helicase 35. RBBP4 (degree=12) was revealed to be the hub gene of this PMOP-specific PPI network. Among these 11 genes, three genes (OSTF1, ADRB1 and NEO1) were speculated to serve roles in PMOP by regulating the balance between bone formation and bone resorption, while two genes (GPR87 and GPR116) may be involved in PMOP by regulating the nuclear factor-κB signaling pathway. Furthermore, DHTKD1 and RBBP4 may be involved in PMOP by regulating mitochondrial dysfunction and interacting with ESR1, respectively. In conclusion, the findings of the current study provided an insight for exploring the mechanism and developing novel biomarkers for PMOP. Further studies are required to test the diagnostic value for PMOP prior to use in a clinical setting.
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spelling pubmed-63232132019-01-15 Identification of gene biomarkers in patients with postmenopausal osteoporosis Yang, Chenggang Ren, Jing Li, Bangling Jin, Chuandi Ma, Cui Cheng, Cheng Sun, Yaolan Shi, Xiaofeng Mol Med Rep Articles Postmenopausal osteoporosis (PMOP) is a major public health concern worldwide. The present study aimed to provide evidence to assist in the development of specific novel biomarkers for PMOP. Differentially expressed genes (DEGs) were identified between PMOP and normal controls by integrated microarray analyses of the Gene Expression Omnibus (GEO) database, and the optimal diagnostic gene biomarkers for PMOP were identified with LASSO and Boruta algorithms. Classification models, including support vector machine (SVM), decision tree and random forests models, were established to test the diagnostic value of identified gene biomarkers for PMOP. Functional annotations and protein-protein interaction (PPI) network constructions were also conducted. Integrated microarray analyses (GSE56815, GSE13850 and GSE7429) of the GEO database were employed, and 1,320 DEGs were identified between PMOP and normal controls. An 11-gene combination was also identified as an optimal biomarker for PMOP by feature selection and classification methods using SVM, decision tree and random forest models. This combination was comprised of the following genes: Dehydrogenase E1 and transketolase domain containing 1 (DHTKD1), osteoclast stimulating factor 1 (OSTF1), G protein-coupled receptor 116 (GPR116), BCL2 interacting killer, adrenoceptor β1 (ADRB1), neogenin 1 (NEO1), RB binding protein 4 (RBBP4), GPR87, cylicin 2, EF-hand calcium binding domain 1 and DEAH-box helicase 35. RBBP4 (degree=12) was revealed to be the hub gene of this PMOP-specific PPI network. Among these 11 genes, three genes (OSTF1, ADRB1 and NEO1) were speculated to serve roles in PMOP by regulating the balance between bone formation and bone resorption, while two genes (GPR87 and GPR116) may be involved in PMOP by regulating the nuclear factor-κB signaling pathway. Furthermore, DHTKD1 and RBBP4 may be involved in PMOP by regulating mitochondrial dysfunction and interacting with ESR1, respectively. In conclusion, the findings of the current study provided an insight for exploring the mechanism and developing novel biomarkers for PMOP. Further studies are required to test the diagnostic value for PMOP prior to use in a clinical setting. D.A. Spandidos 2019-02 2018-12-12 /pmc/articles/PMC6323213/ /pubmed/30569177 http://dx.doi.org/10.3892/mmr.2018.9752 Text en Copyright: © Yang et al. This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License (https://creativecommons.org/licenses/by-nc-nd/4.0/) , which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.
spellingShingle Articles
Yang, Chenggang
Ren, Jing
Li, Bangling
Jin, Chuandi
Ma, Cui
Cheng, Cheng
Sun, Yaolan
Shi, Xiaofeng
Identification of gene biomarkers in patients with postmenopausal osteoporosis
title Identification of gene biomarkers in patients with postmenopausal osteoporosis
title_full Identification of gene biomarkers in patients with postmenopausal osteoporosis
title_fullStr Identification of gene biomarkers in patients with postmenopausal osteoporosis
title_full_unstemmed Identification of gene biomarkers in patients with postmenopausal osteoporosis
title_short Identification of gene biomarkers in patients with postmenopausal osteoporosis
title_sort identification of gene biomarkers in patients with postmenopausal osteoporosis
topic Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6323213/
https://www.ncbi.nlm.nih.gov/pubmed/30569177
http://dx.doi.org/10.3892/mmr.2018.9752
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