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Development of software enabling Chinese medicine-based precision treatment for osteoporosis at the gene and pathway levels

BACKGROUND: Precision medicine aims to address the demand for precise therapy at the gene and pathway levels. We aimed to design software to allow precise treatment of osteoporosis (OP) with Chinese medicines (CMs) at the gene and pathway levels. METHODS: PubMed, EMBASE, Cochrane Library, China Nati...

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Autores principales: Li, Jinyu, Feng, Guiyu, He, Haoyang, Wang, Haolin, Tang, Jia, Han, Aiqing, Mu, Xiaohong, Zhu, Weifeng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9013124/
https://www.ncbi.nlm.nih.gov/pubmed/35428337
http://dx.doi.org/10.1186/s13020-022-00596-6
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author Li, Jinyu
Feng, Guiyu
He, Haoyang
Wang, Haolin
Tang, Jia
Han, Aiqing
Mu, Xiaohong
Zhu, Weifeng
author_facet Li, Jinyu
Feng, Guiyu
He, Haoyang
Wang, Haolin
Tang, Jia
Han, Aiqing
Mu, Xiaohong
Zhu, Weifeng
author_sort Li, Jinyu
collection PubMed
description BACKGROUND: Precision medicine aims to address the demand for precise therapy at the gene and pathway levels. We aimed to design software to allow precise treatment of osteoporosis (OP) with Chinese medicines (CMs) at the gene and pathway levels. METHODS: PubMed, EMBASE, Cochrane Library, China National Knowledge Infrastructure (CNKI), China Science and Technology Journal Database (VIP database), and the Wanfang database were searched to identify studies treating osteoporosis with CMs. The TCMSP was used to identify bioactive ingredients and related genes for each CM. Gene expression omnibus (GEO) database and the limma package were used to identify differentially expressed genes in osteoporosis. Perl software was used to identify the shared genes between the bioactive components in CM and osteoporosis. R packages and bioconductor packages were used to define the target relationship between shared genes and their related pathways. Third-party Python libraries were used to write program codes. Pyinstaller library was used to create an executable program file. RESULTS: Data mining: a total of 164 CMs were included, but Drynariae Rhizoma (gusuibu) was used to present this process. We obtained 44 precise relationships among the bioactive ingredients of Drynariae Rhizoma, shared genes, and pathways. Python programming: we developed the software to show the precise relationship among bioactive ingredients, shared genes, and pathways for each CM, including Drynariae Rhizoma. CONCLUSIONS: This study could increase the precision of CM, and could provide a valuable and convenient software for searching precise relationships among bioactive ingredients, shared genes, and pathways. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13020-022-00596-6.
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spelling pubmed-90131242022-04-17 Development of software enabling Chinese medicine-based precision treatment for osteoporosis at the gene and pathway levels Li, Jinyu Feng, Guiyu He, Haoyang Wang, Haolin Tang, Jia Han, Aiqing Mu, Xiaohong Zhu, Weifeng Chin Med Research BACKGROUND: Precision medicine aims to address the demand for precise therapy at the gene and pathway levels. We aimed to design software to allow precise treatment of osteoporosis (OP) with Chinese medicines (CMs) at the gene and pathway levels. METHODS: PubMed, EMBASE, Cochrane Library, China National Knowledge Infrastructure (CNKI), China Science and Technology Journal Database (VIP database), and the Wanfang database were searched to identify studies treating osteoporosis with CMs. The TCMSP was used to identify bioactive ingredients and related genes for each CM. Gene expression omnibus (GEO) database and the limma package were used to identify differentially expressed genes in osteoporosis. Perl software was used to identify the shared genes between the bioactive components in CM and osteoporosis. R packages and bioconductor packages were used to define the target relationship between shared genes and their related pathways. Third-party Python libraries were used to write program codes. Pyinstaller library was used to create an executable program file. RESULTS: Data mining: a total of 164 CMs were included, but Drynariae Rhizoma (gusuibu) was used to present this process. We obtained 44 precise relationships among the bioactive ingredients of Drynariae Rhizoma, shared genes, and pathways. Python programming: we developed the software to show the precise relationship among bioactive ingredients, shared genes, and pathways for each CM, including Drynariae Rhizoma. CONCLUSIONS: This study could increase the precision of CM, and could provide a valuable and convenient software for searching precise relationships among bioactive ingredients, shared genes, and pathways. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13020-022-00596-6. BioMed Central 2022-04-15 /pmc/articles/PMC9013124/ /pubmed/35428337 http://dx.doi.org/10.1186/s13020-022-00596-6 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Li, Jinyu
Feng, Guiyu
He, Haoyang
Wang, Haolin
Tang, Jia
Han, Aiqing
Mu, Xiaohong
Zhu, Weifeng
Development of software enabling Chinese medicine-based precision treatment for osteoporosis at the gene and pathway levels
title Development of software enabling Chinese medicine-based precision treatment for osteoporosis at the gene and pathway levels
title_full Development of software enabling Chinese medicine-based precision treatment for osteoporosis at the gene and pathway levels
title_fullStr Development of software enabling Chinese medicine-based precision treatment for osteoporosis at the gene and pathway levels
title_full_unstemmed Development of software enabling Chinese medicine-based precision treatment for osteoporosis at the gene and pathway levels
title_short Development of software enabling Chinese medicine-based precision treatment for osteoporosis at the gene and pathway levels
title_sort development of software enabling chinese medicine-based precision treatment for osteoporosis at the gene and pathway levels
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9013124/
https://www.ncbi.nlm.nih.gov/pubmed/35428337
http://dx.doi.org/10.1186/s13020-022-00596-6
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