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Integrated bioinformatics analysis for the identification of idiopathic pulmonary fibrosis–related genes and potential therapeutic drugs
OBJECTIVE: The pathogenesis of idiopathic pulmonary fibrosis (IPF) remains unclear. We sought to identify IPF-related genes that may participate in the pathogenesis and predict potential targeted traditional Chinese medicines (TCMs). METHODS: Using IPF gene-expression data, Wilcoxon rank-sum tests w...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10552267/ https://www.ncbi.nlm.nih.gov/pubmed/37794454 http://dx.doi.org/10.1186/s12890-023-02678-z |
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author | Zhang, Zhenzhen Guan, Qingzhou Tian, Yange Shao, Xuejie Zhao, Peng Huang, Lidong Li, Jiansheng |
author_facet | Zhang, Zhenzhen Guan, Qingzhou Tian, Yange Shao, Xuejie Zhao, Peng Huang, Lidong Li, Jiansheng |
author_sort | Zhang, Zhenzhen |
collection | PubMed |
description | OBJECTIVE: The pathogenesis of idiopathic pulmonary fibrosis (IPF) remains unclear. We sought to identify IPF-related genes that may participate in the pathogenesis and predict potential targeted traditional Chinese medicines (TCMs). METHODS: Using IPF gene-expression data, Wilcoxon rank-sum tests were performed to identify differentially expressed genes (DEGs). Protein–protein interaction (PPI) networks, hub genes, and competitive endogenous RNA (ceRNA) networks were constructed or identified by Cytoscape. Quantitative polymerase chain reaction (qPCR) experiments in TGF-β1-induced human fetal lung (HFL) fibroblast cells and a pulmonary fibrosis mouse model verified gene reliability. The SymMap database predicted potential TCMs targeting IPF. The reliability of TCMs was verified in TGF-β1-induced MRC-5 cells. MATERIALS: Multiple gene-expression profile data of normal lung and IPF tissues were downloaded from the Gene Expression Omnibus database. HFL fibroblast cells and MRC-5 cells were purchased from Wuhan Procell Life Science and Technology Co., Ltd. (Wuhan, China). C57BL/12 mice were purchased from Beijing Vital River Laboratory Animal Technology Co., Ltd. (Beijing, China). RESULTS: In datasets GSE134692 and GSE15197, DEGs were identified using Wilcoxon rank-sum tests (both p < 0.05). Among them, 1885 DEGs were commonly identified, and 87% (1640 genes) had identical dysregulation directions (binomial test, p < 1.00E-16). A PPI network with 1623 nodes and 8159 edges was constructed, and 18 hub genes were identified using the Analyze Network plugin in Cytoscape. Of 18 genes, CAV1, PECAM1, BMP4, VEGFA, FYN, SPP1, and COL1A1 were further validated in the GeneCards database and independent dataset GSE24206. ceRNA networks of VEGFA, SPP1, and COL1A1 were constructed. The genes were verified by qPCR in samples of TGF-β1-induced HFL fibroblast cells and pulmonary fibrosis mice. Finally, Sea Buckthorn and Gnaphalium Affine were predicted as potential TCMs for IPF. The TCMs were verified by qPCR in TGF-β1-induced MRC-5 cells. CONCLUSION: This analysis strategy may be useful for elucidating novel mechanisms underlying IPF at the transcriptome level. The identified hub genes may play key roles in IPF pathogenesis and therapy. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12890-023-02678-z. |
format | Online Article Text |
id | pubmed-10552267 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-105522672023-10-06 Integrated bioinformatics analysis for the identification of idiopathic pulmonary fibrosis–related genes and potential therapeutic drugs Zhang, Zhenzhen Guan, Qingzhou Tian, Yange Shao, Xuejie Zhao, Peng Huang, Lidong Li, Jiansheng BMC Pulm Med Research OBJECTIVE: The pathogenesis of idiopathic pulmonary fibrosis (IPF) remains unclear. We sought to identify IPF-related genes that may participate in the pathogenesis and predict potential targeted traditional Chinese medicines (TCMs). METHODS: Using IPF gene-expression data, Wilcoxon rank-sum tests were performed to identify differentially expressed genes (DEGs). Protein–protein interaction (PPI) networks, hub genes, and competitive endogenous RNA (ceRNA) networks were constructed or identified by Cytoscape. Quantitative polymerase chain reaction (qPCR) experiments in TGF-β1-induced human fetal lung (HFL) fibroblast cells and a pulmonary fibrosis mouse model verified gene reliability. The SymMap database predicted potential TCMs targeting IPF. The reliability of TCMs was verified in TGF-β1-induced MRC-5 cells. MATERIALS: Multiple gene-expression profile data of normal lung and IPF tissues were downloaded from the Gene Expression Omnibus database. HFL fibroblast cells and MRC-5 cells were purchased from Wuhan Procell Life Science and Technology Co., Ltd. (Wuhan, China). C57BL/12 mice were purchased from Beijing Vital River Laboratory Animal Technology Co., Ltd. (Beijing, China). RESULTS: In datasets GSE134692 and GSE15197, DEGs were identified using Wilcoxon rank-sum tests (both p < 0.05). Among them, 1885 DEGs were commonly identified, and 87% (1640 genes) had identical dysregulation directions (binomial test, p < 1.00E-16). A PPI network with 1623 nodes and 8159 edges was constructed, and 18 hub genes were identified using the Analyze Network plugin in Cytoscape. Of 18 genes, CAV1, PECAM1, BMP4, VEGFA, FYN, SPP1, and COL1A1 were further validated in the GeneCards database and independent dataset GSE24206. ceRNA networks of VEGFA, SPP1, and COL1A1 were constructed. The genes were verified by qPCR in samples of TGF-β1-induced HFL fibroblast cells and pulmonary fibrosis mice. Finally, Sea Buckthorn and Gnaphalium Affine were predicted as potential TCMs for IPF. The TCMs were verified by qPCR in TGF-β1-induced MRC-5 cells. CONCLUSION: This analysis strategy may be useful for elucidating novel mechanisms underlying IPF at the transcriptome level. The identified hub genes may play key roles in IPF pathogenesis and therapy. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12890-023-02678-z. BioMed Central 2023-10-04 /pmc/articles/PMC10552267/ /pubmed/37794454 http://dx.doi.org/10.1186/s12890-023-02678-z Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This 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 Zhang, Zhenzhen Guan, Qingzhou Tian, Yange Shao, Xuejie Zhao, Peng Huang, Lidong Li, Jiansheng Integrated bioinformatics analysis for the identification of idiopathic pulmonary fibrosis–related genes and potential therapeutic drugs |
title | Integrated bioinformatics analysis for the identification of idiopathic pulmonary fibrosis–related genes and potential therapeutic drugs |
title_full | Integrated bioinformatics analysis for the identification of idiopathic pulmonary fibrosis–related genes and potential therapeutic drugs |
title_fullStr | Integrated bioinformatics analysis for the identification of idiopathic pulmonary fibrosis–related genes and potential therapeutic drugs |
title_full_unstemmed | Integrated bioinformatics analysis for the identification of idiopathic pulmonary fibrosis–related genes and potential therapeutic drugs |
title_short | Integrated bioinformatics analysis for the identification of idiopathic pulmonary fibrosis–related genes and potential therapeutic drugs |
title_sort | integrated bioinformatics analysis for the identification of idiopathic pulmonary fibrosis–related genes and potential therapeutic drugs |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10552267/ https://www.ncbi.nlm.nih.gov/pubmed/37794454 http://dx.doi.org/10.1186/s12890-023-02678-z |
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