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Identification and validation of ferroptosis key genes in bone mesenchymal stromal cells of primary osteoporosis based on bioinformatics analysis

Primary osteoporosis has long been underdiagnosed and undertreated. Currently, ferroptosis may be a promising research direction in the prevention and treatment of primary osteoporosis. However, the specific mechanism of ferroptosis in primary osteoporosis remains a mystery. Differentially expressed...

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Autores principales: Xia, Yu, Zhang, Haifeng, Wang, Heng, Wang, Qiufei, Zhu, Pengfei, Gu, Ye, Yang, Huilin, Geng, Dechun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9452779/
https://www.ncbi.nlm.nih.gov/pubmed/36093072
http://dx.doi.org/10.3389/fendo.2022.980867
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author Xia, Yu
Zhang, Haifeng
Wang, Heng
Wang, Qiufei
Zhu, Pengfei
Gu, Ye
Yang, Huilin
Geng, Dechun
author_facet Xia, Yu
Zhang, Haifeng
Wang, Heng
Wang, Qiufei
Zhu, Pengfei
Gu, Ye
Yang, Huilin
Geng, Dechun
author_sort Xia, Yu
collection PubMed
description Primary osteoporosis has long been underdiagnosed and undertreated. Currently, ferroptosis may be a promising research direction in the prevention and treatment of primary osteoporosis. However, the specific mechanism of ferroptosis in primary osteoporosis remains a mystery. Differentially expressed genes (DEGs) were identified in bone mesenchymal stromal cells (BMSCs) of primary osteoporosis and heathy patients from the GEO databases with the help of bioinformatics analysis. Then, we intersected these DEGs with the ferroptosis dataset and obtained 80 Ferr-DEGs. Several bioinformatics algorithms (PCA, RLE, Limma, BC, MCC, etc.) were adopted to integrate the results. Additionally, we explored the potential functional roles of the Ferr-DEGs via GO and KEGG. Protein–protein interactions (PPI) were used to predict potential interactive networks. Finally, 80 Ferr-DEGs and 5 key Ferr-DEGs were calculated. The 5 key Ferr-DEGs were further verified in the OVX mouse model. In conclusion, through a variety of bioinformatics methods, our research successfully identified 5 key Ferr-DEGs associated with primary osteoporosis and ferroptosis, namely, sirtuin 1(SIRT1), heat shock protein family A (Hsp70) member 5 (HSPA5), mechanistic target of rapamycin kinase (MTOR), hypoxia inducible factor 1 subunit alpha (HIF1A) and beclin 1 (BECN1), which were verified in an animal model.
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spelling pubmed-94527792022-09-09 Identification and validation of ferroptosis key genes in bone mesenchymal stromal cells of primary osteoporosis based on bioinformatics analysis Xia, Yu Zhang, Haifeng Wang, Heng Wang, Qiufei Zhu, Pengfei Gu, Ye Yang, Huilin Geng, Dechun Front Endocrinol (Lausanne) Endocrinology Primary osteoporosis has long been underdiagnosed and undertreated. Currently, ferroptosis may be a promising research direction in the prevention and treatment of primary osteoporosis. However, the specific mechanism of ferroptosis in primary osteoporosis remains a mystery. Differentially expressed genes (DEGs) were identified in bone mesenchymal stromal cells (BMSCs) of primary osteoporosis and heathy patients from the GEO databases with the help of bioinformatics analysis. Then, we intersected these DEGs with the ferroptosis dataset and obtained 80 Ferr-DEGs. Several bioinformatics algorithms (PCA, RLE, Limma, BC, MCC, etc.) were adopted to integrate the results. Additionally, we explored the potential functional roles of the Ferr-DEGs via GO and KEGG. Protein–protein interactions (PPI) were used to predict potential interactive networks. Finally, 80 Ferr-DEGs and 5 key Ferr-DEGs were calculated. The 5 key Ferr-DEGs were further verified in the OVX mouse model. In conclusion, through a variety of bioinformatics methods, our research successfully identified 5 key Ferr-DEGs associated with primary osteoporosis and ferroptosis, namely, sirtuin 1(SIRT1), heat shock protein family A (Hsp70) member 5 (HSPA5), mechanistic target of rapamycin kinase (MTOR), hypoxia inducible factor 1 subunit alpha (HIF1A) and beclin 1 (BECN1), which were verified in an animal model. Frontiers Media S.A. 2022-08-25 /pmc/articles/PMC9452779/ /pubmed/36093072 http://dx.doi.org/10.3389/fendo.2022.980867 Text en Copyright © 2022 Xia, Zhang, Wang, Wang, Zhu, Gu, Yang and Geng 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 Endocrinology
Xia, Yu
Zhang, Haifeng
Wang, Heng
Wang, Qiufei
Zhu, Pengfei
Gu, Ye
Yang, Huilin
Geng, Dechun
Identification and validation of ferroptosis key genes in bone mesenchymal stromal cells of primary osteoporosis based on bioinformatics analysis
title Identification and validation of ferroptosis key genes in bone mesenchymal stromal cells of primary osteoporosis based on bioinformatics analysis
title_full Identification and validation of ferroptosis key genes in bone mesenchymal stromal cells of primary osteoporosis based on bioinformatics analysis
title_fullStr Identification and validation of ferroptosis key genes in bone mesenchymal stromal cells of primary osteoporosis based on bioinformatics analysis
title_full_unstemmed Identification and validation of ferroptosis key genes in bone mesenchymal stromal cells of primary osteoporosis based on bioinformatics analysis
title_short Identification and validation of ferroptosis key genes in bone mesenchymal stromal cells of primary osteoporosis based on bioinformatics analysis
title_sort identification and validation of ferroptosis key genes in bone mesenchymal stromal cells of primary osteoporosis based on bioinformatics analysis
topic Endocrinology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9452779/
https://www.ncbi.nlm.nih.gov/pubmed/36093072
http://dx.doi.org/10.3389/fendo.2022.980867
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