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Identification of biomarkers in common chronic lung diseases by co-expression networks and drug-target interactions analysis

BACKGROUND: asthma, chronic obstructive pulmonary disease (COPD), and idiopathic pulmonary fibrosis (IPF) are three serious pulmonary diseases that contain common and unique characteristics. Therefore, the identification of biomarkers that differentiate these diseases is of importance for preventing...

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Autores principales: Maghsoudloo, Mazaher, Azimzadeh Jamalkandi, Sadegh, Najafi, Ali, Masoudi-Nejad, Ali
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6969427/
https://www.ncbi.nlm.nih.gov/pubmed/31952466
http://dx.doi.org/10.1186/s10020-019-0135-9
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author Maghsoudloo, Mazaher
Azimzadeh Jamalkandi, Sadegh
Najafi, Ali
Masoudi-Nejad, Ali
author_facet Maghsoudloo, Mazaher
Azimzadeh Jamalkandi, Sadegh
Najafi, Ali
Masoudi-Nejad, Ali
author_sort Maghsoudloo, Mazaher
collection PubMed
description BACKGROUND: asthma, chronic obstructive pulmonary disease (COPD), and idiopathic pulmonary fibrosis (IPF) are three serious pulmonary diseases that contain common and unique characteristics. Therefore, the identification of biomarkers that differentiate these diseases is of importance for preventing misdiagnosis. In this regard, the present study aimed to identify the disorders at the early stages, based on lung transcriptomics data and drug-target interactions. METHODS: To this end, the differentially expressed genes were found in each disease. Then, WGCNA was utilized to find specific and consensus gene modules among the three diseases. Finally, the disease-disease similarity was analyzed, followed by determining candidate drug-target interactions. RESULTS: The results confirmed that the asthma lung transcriptome was more similar to COPD than IPF. In addition, the biomarkers were found in each disease and thus were proposed for further clinical validations. These genes included RBM42, STX5, and TRIM41 in asthma, CYP27A1, GM2A, LGALS9, SPI1, and NLRC4 in COPD, ATF3, PPP1R15A, ZFP36, SOCS3, NAMPT, and GADD45B in IPF, LRRC48 and CETN2 in asthma-COPD, COL15A1, GIMAP6, and JAM2 in asthma-IPF and LMO7, TSPAN13, LAMA3, and ANXA3 in COPD-IPF. Finally, analyzing drug-target networks suggested anti-inflammatory candidate drugs for treating the above mentioned diseases. CONCLUSION: In general, the results revealed the unique and common biomarkers among three chronic lung diseases. Eventually, some drugs were suggested for treatment purposes.
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spelling pubmed-69694272020-01-27 Identification of biomarkers in common chronic lung diseases by co-expression networks and drug-target interactions analysis Maghsoudloo, Mazaher Azimzadeh Jamalkandi, Sadegh Najafi, Ali Masoudi-Nejad, Ali Mol Med Research Article BACKGROUND: asthma, chronic obstructive pulmonary disease (COPD), and idiopathic pulmonary fibrosis (IPF) are three serious pulmonary diseases that contain common and unique characteristics. Therefore, the identification of biomarkers that differentiate these diseases is of importance for preventing misdiagnosis. In this regard, the present study aimed to identify the disorders at the early stages, based on lung transcriptomics data and drug-target interactions. METHODS: To this end, the differentially expressed genes were found in each disease. Then, WGCNA was utilized to find specific and consensus gene modules among the three diseases. Finally, the disease-disease similarity was analyzed, followed by determining candidate drug-target interactions. RESULTS: The results confirmed that the asthma lung transcriptome was more similar to COPD than IPF. In addition, the biomarkers were found in each disease and thus were proposed for further clinical validations. These genes included RBM42, STX5, and TRIM41 in asthma, CYP27A1, GM2A, LGALS9, SPI1, and NLRC4 in COPD, ATF3, PPP1R15A, ZFP36, SOCS3, NAMPT, and GADD45B in IPF, LRRC48 and CETN2 in asthma-COPD, COL15A1, GIMAP6, and JAM2 in asthma-IPF and LMO7, TSPAN13, LAMA3, and ANXA3 in COPD-IPF. Finally, analyzing drug-target networks suggested anti-inflammatory candidate drugs for treating the above mentioned diseases. CONCLUSION: In general, the results revealed the unique and common biomarkers among three chronic lung diseases. Eventually, some drugs were suggested for treatment purposes. BioMed Central 2020-01-17 /pmc/articles/PMC6969427/ /pubmed/31952466 http://dx.doi.org/10.1186/s10020-019-0135-9 Text en © The Author(s) 2020 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
Maghsoudloo, Mazaher
Azimzadeh Jamalkandi, Sadegh
Najafi, Ali
Masoudi-Nejad, Ali
Identification of biomarkers in common chronic lung diseases by co-expression networks and drug-target interactions analysis
title Identification of biomarkers in common chronic lung diseases by co-expression networks and drug-target interactions analysis
title_full Identification of biomarkers in common chronic lung diseases by co-expression networks and drug-target interactions analysis
title_fullStr Identification of biomarkers in common chronic lung diseases by co-expression networks and drug-target interactions analysis
title_full_unstemmed Identification of biomarkers in common chronic lung diseases by co-expression networks and drug-target interactions analysis
title_short Identification of biomarkers in common chronic lung diseases by co-expression networks and drug-target interactions analysis
title_sort identification of biomarkers in common chronic lung diseases by co-expression networks and drug-target interactions analysis
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6969427/
https://www.ncbi.nlm.nih.gov/pubmed/31952466
http://dx.doi.org/10.1186/s10020-019-0135-9
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