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In Silico Network Analysis of Ingredients of Cornus officinalis in Osteoporosis
BACKGROUND: Cornus officinalis (CO), also known as ‘Shanzhuyu’, is one of the most common traditional Chinese herbs used against osteoporosis. Although previous studies have found that CO has beneficial effects in alleviating osteoporosis, its mechanisms remain unclear. MATERIAL/METHODS: In this stu...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
International Scientific Literature, Inc.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8023278/ https://www.ncbi.nlm.nih.gov/pubmed/33795629 http://dx.doi.org/10.12659/MSM.929219 |
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author | Huang, Feiqi Guo, Huizhi Wei, Yuanbiao Zhao, Xiao Chen, Yangsheng Lin, Zhan Zhou, Yanhui Sun, Ping |
author_facet | Huang, Feiqi Guo, Huizhi Wei, Yuanbiao Zhao, Xiao Chen, Yangsheng Lin, Zhan Zhou, Yanhui Sun, Ping |
author_sort | Huang, Feiqi |
collection | PubMed |
description | BACKGROUND: Cornus officinalis (CO), also known as ‘Shanzhuyu’, is one of the most common traditional Chinese herbs used against osteoporosis. Although previous studies have found that CO has beneficial effects in alleviating osteoporosis, its mechanisms remain unclear. MATERIAL/METHODS: In this study, we applied system bioinformatic approaches to investigate the possible therapeutic mechanisms of CO against osteoporosis. We collected the active ingredients of CO and their targets from the TCMSP, BATMAN-TCM, and ETCM databases. Next, we obtained the osteoporosis targets from differentially expressed mRNAs from the Gene Expression Omnibus (GEO) gene series (GSE35958). Next, the shared genes of the CO pharmacological targets and osteoporosis-related targets were selected to construct the protein-protein interaction network, based on the results from the STRING database. Subsequently, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were carried out by using the clusterProfiler package in R software. RESULTS: In all, there were 58 unique CO compounds and 518 therapeutic targets. Based on the GO and KEGG enrichment results of 98 common genes, we selected the top 25 terms, based on the terms’ P values. We found that the anti-osteoporotic effect of CO may mostly involve the regulation of calcium metabolism and reactive oxygen species, and the estrogen signaling pathway and osteoclast differentiation pathway. CONCLUSIONS: We found the possible mechanisms of CO in treating osteoporosis may be based on multiple targets and pathways. We also provided a theoretical basis and promising direction for investigating the exact anti-osteoporotic mechanisms of CO. |
format | Online Article Text |
id | pubmed-8023278 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | International Scientific Literature, Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-80232782021-04-08 In Silico Network Analysis of Ingredients of Cornus officinalis in Osteoporosis Huang, Feiqi Guo, Huizhi Wei, Yuanbiao Zhao, Xiao Chen, Yangsheng Lin, Zhan Zhou, Yanhui Sun, Ping Med Sci Monit Database Analysis BACKGROUND: Cornus officinalis (CO), also known as ‘Shanzhuyu’, is one of the most common traditional Chinese herbs used against osteoporosis. Although previous studies have found that CO has beneficial effects in alleviating osteoporosis, its mechanisms remain unclear. MATERIAL/METHODS: In this study, we applied system bioinformatic approaches to investigate the possible therapeutic mechanisms of CO against osteoporosis. We collected the active ingredients of CO and their targets from the TCMSP, BATMAN-TCM, and ETCM databases. Next, we obtained the osteoporosis targets from differentially expressed mRNAs from the Gene Expression Omnibus (GEO) gene series (GSE35958). Next, the shared genes of the CO pharmacological targets and osteoporosis-related targets were selected to construct the protein-protein interaction network, based on the results from the STRING database. Subsequently, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were carried out by using the clusterProfiler package in R software. RESULTS: In all, there were 58 unique CO compounds and 518 therapeutic targets. Based on the GO and KEGG enrichment results of 98 common genes, we selected the top 25 terms, based on the terms’ P values. We found that the anti-osteoporotic effect of CO may mostly involve the regulation of calcium metabolism and reactive oxygen species, and the estrogen signaling pathway and osteoclast differentiation pathway. CONCLUSIONS: We found the possible mechanisms of CO in treating osteoporosis may be based on multiple targets and pathways. We also provided a theoretical basis and promising direction for investigating the exact anti-osteoporotic mechanisms of CO. International Scientific Literature, Inc. 2021-04-02 /pmc/articles/PMC8023278/ /pubmed/33795629 http://dx.doi.org/10.12659/MSM.929219 Text en © Med Sci Monit, 2021 This work is licensed under Creative Common Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0 (https://creativecommons.org/licenses/by-nc-nd/4.0/) ) |
spellingShingle | Database Analysis Huang, Feiqi Guo, Huizhi Wei, Yuanbiao Zhao, Xiao Chen, Yangsheng Lin, Zhan Zhou, Yanhui Sun, Ping In Silico Network Analysis of Ingredients of Cornus officinalis in Osteoporosis |
title | In Silico Network Analysis of Ingredients of Cornus officinalis in Osteoporosis |
title_full | In Silico Network Analysis of Ingredients of Cornus officinalis in Osteoporosis |
title_fullStr | In Silico Network Analysis of Ingredients of Cornus officinalis in Osteoporosis |
title_full_unstemmed | In Silico Network Analysis of Ingredients of Cornus officinalis in Osteoporosis |
title_short | In Silico Network Analysis of Ingredients of Cornus officinalis in Osteoporosis |
title_sort | in silico network analysis of ingredients of cornus officinalis in osteoporosis |
topic | Database Analysis |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8023278/ https://www.ncbi.nlm.nih.gov/pubmed/33795629 http://dx.doi.org/10.12659/MSM.929219 |
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