Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Zhang, Zhenzhen, Guan, Qingzhou, Tian, Yange, Shao, Xuejie, Zhao, Peng, Huang, Lidong, Li, Jiansheng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
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
_version_ 1785115924825636864
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
work_keys_str_mv AT zhangzhenzhen integratedbioinformaticsanalysisfortheidentificationofidiopathicpulmonaryfibrosisrelatedgenesandpotentialtherapeuticdrugs
AT guanqingzhou integratedbioinformaticsanalysisfortheidentificationofidiopathicpulmonaryfibrosisrelatedgenesandpotentialtherapeuticdrugs
AT tianyange integratedbioinformaticsanalysisfortheidentificationofidiopathicpulmonaryfibrosisrelatedgenesandpotentialtherapeuticdrugs
AT shaoxuejie integratedbioinformaticsanalysisfortheidentificationofidiopathicpulmonaryfibrosisrelatedgenesandpotentialtherapeuticdrugs
AT zhaopeng integratedbioinformaticsanalysisfortheidentificationofidiopathicpulmonaryfibrosisrelatedgenesandpotentialtherapeuticdrugs
AT huanglidong integratedbioinformaticsanalysisfortheidentificationofidiopathicpulmonaryfibrosisrelatedgenesandpotentialtherapeuticdrugs
AT lijiansheng integratedbioinformaticsanalysisfortheidentificationofidiopathicpulmonaryfibrosisrelatedgenesandpotentialtherapeuticdrugs