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Molecular Subtype Classification of Postmenopausal Osteoporosis and Immune Infiltration Microenvironment Based on Bioinformatics Analysis of Osteoclast-Regulatory Genes

Osteoporosis is common in postmenopausal women but is often asymptomatic until a fracture occurs, highlighting the importance of early screening and preventive interventions. This study aimed to develop molecular subtype risk stratification of postmenopausal osteoporosis and analyze the immune infil...

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Autores principales: Gong, Yining, Hao, Dingjun, Zhang, Yong, Tu, Yongyong, He, Baorong, Yan, Liang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10604900/
https://www.ncbi.nlm.nih.gov/pubmed/37893075
http://dx.doi.org/10.3390/biomedicines11102701
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author Gong, Yining
Hao, Dingjun
Zhang, Yong
Tu, Yongyong
He, Baorong
Yan, Liang
author_facet Gong, Yining
Hao, Dingjun
Zhang, Yong
Tu, Yongyong
He, Baorong
Yan, Liang
author_sort Gong, Yining
collection PubMed
description Osteoporosis is common in postmenopausal women but is often asymptomatic until a fracture occurs, highlighting the importance of early screening and preventive interventions. This study aimed to develop molecular subtype risk stratification of postmenopausal osteoporosis and analyze the immune infiltration microenvironment. Microarray data for osteoporosis were downloaded and analyzed. Logistic and least absolute shrinkage and selection operator (LASSO) regression analyses were used to construct the molecular risk model. Circulating blood samples were collected from 10 enrolled participants to validate the key differentially expressed genes, and consistent clustering based on the expression profiles of candidate genes was performed to obtain molecular subtypes. Three key genes, CTNNB1, MITF, and TNFSF11, were obtained as variables and used to construct the risk model. External experimental validation showed substantial differences in the three key genes between patients with osteoporosis and the controls (p < 0.05). Three subtypes were obtained based on dimensionality reduction clustering results. Cluster 3 had significantly more patients with low bone mineral density (BMD), whereas Cluster 2 had significantly more patients with high BMD (p < 0.05). This study introduced a novel molecular risk model and subtype classification system, which is an evidence-based screening strategy that will guide the active prevention, early diagnosis, and treatment of osteoporosis in high-risk postmenopausal women.
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spelling pubmed-106049002023-10-28 Molecular Subtype Classification of Postmenopausal Osteoporosis and Immune Infiltration Microenvironment Based on Bioinformatics Analysis of Osteoclast-Regulatory Genes Gong, Yining Hao, Dingjun Zhang, Yong Tu, Yongyong He, Baorong Yan, Liang Biomedicines Article Osteoporosis is common in postmenopausal women but is often asymptomatic until a fracture occurs, highlighting the importance of early screening and preventive interventions. This study aimed to develop molecular subtype risk stratification of postmenopausal osteoporosis and analyze the immune infiltration microenvironment. Microarray data for osteoporosis were downloaded and analyzed. Logistic and least absolute shrinkage and selection operator (LASSO) regression analyses were used to construct the molecular risk model. Circulating blood samples were collected from 10 enrolled participants to validate the key differentially expressed genes, and consistent clustering based on the expression profiles of candidate genes was performed to obtain molecular subtypes. Three key genes, CTNNB1, MITF, and TNFSF11, were obtained as variables and used to construct the risk model. External experimental validation showed substantial differences in the three key genes between patients with osteoporosis and the controls (p < 0.05). Three subtypes were obtained based on dimensionality reduction clustering results. Cluster 3 had significantly more patients with low bone mineral density (BMD), whereas Cluster 2 had significantly more patients with high BMD (p < 0.05). This study introduced a novel molecular risk model and subtype classification system, which is an evidence-based screening strategy that will guide the active prevention, early diagnosis, and treatment of osteoporosis in high-risk postmenopausal women. MDPI 2023-10-04 /pmc/articles/PMC10604900/ /pubmed/37893075 http://dx.doi.org/10.3390/biomedicines11102701 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Gong, Yining
Hao, Dingjun
Zhang, Yong
Tu, Yongyong
He, Baorong
Yan, Liang
Molecular Subtype Classification of Postmenopausal Osteoporosis and Immune Infiltration Microenvironment Based on Bioinformatics Analysis of Osteoclast-Regulatory Genes
title Molecular Subtype Classification of Postmenopausal Osteoporosis and Immune Infiltration Microenvironment Based on Bioinformatics Analysis of Osteoclast-Regulatory Genes
title_full Molecular Subtype Classification of Postmenopausal Osteoporosis and Immune Infiltration Microenvironment Based on Bioinformatics Analysis of Osteoclast-Regulatory Genes
title_fullStr Molecular Subtype Classification of Postmenopausal Osteoporosis and Immune Infiltration Microenvironment Based on Bioinformatics Analysis of Osteoclast-Regulatory Genes
title_full_unstemmed Molecular Subtype Classification of Postmenopausal Osteoporosis and Immune Infiltration Microenvironment Based on Bioinformatics Analysis of Osteoclast-Regulatory Genes
title_short Molecular Subtype Classification of Postmenopausal Osteoporosis and Immune Infiltration Microenvironment Based on Bioinformatics Analysis of Osteoclast-Regulatory Genes
title_sort molecular subtype classification of postmenopausal osteoporosis and immune infiltration microenvironment based on bioinformatics analysis of osteoclast-regulatory genes
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10604900/
https://www.ncbi.nlm.nih.gov/pubmed/37893075
http://dx.doi.org/10.3390/biomedicines11102701
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