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The Combination of Bioinformatics Analysis and Untargeted Metabolomics Reveals Potential Biomarkers and Key Metabolic Pathways in Asthma

Asthma is a complex chronic airway inflammatory disease that seriously impacts patients’ quality of life. As a novel approach to exploring the pathogenesis of diseases, metabolomics provides the potential to identify biomarkers of asthma host susceptibility and elucidate biological pathways. The aim...

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Detalles Bibliográficos
Autores principales: Huang, Fangfang, Yu, Jinjin, Lai, Tianwen, Luo, Lianxiang, Zhang, Weizhen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9860906/
https://www.ncbi.nlm.nih.gov/pubmed/36676950
http://dx.doi.org/10.3390/metabo13010025
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author Huang, Fangfang
Yu, Jinjin
Lai, Tianwen
Luo, Lianxiang
Zhang, Weizhen
author_facet Huang, Fangfang
Yu, Jinjin
Lai, Tianwen
Luo, Lianxiang
Zhang, Weizhen
author_sort Huang, Fangfang
collection PubMed
description Asthma is a complex chronic airway inflammatory disease that seriously impacts patients’ quality of life. As a novel approach to exploring the pathogenesis of diseases, metabolomics provides the potential to identify biomarkers of asthma host susceptibility and elucidate biological pathways. The aim of this study was to screen potential biomarkers and biological pathways so as to provide possible pharmacological therapeutic targets for asthma. In the present study, we merged the differentially expressed genes (DEGs) of asthma in the GEO database with the metabolic genes obtained by Genecard for bioinformatics analysis and successfully screened out the metabolism-related hub genes (HIF1A, OCRL, NNMT, and PER1). Then, untargeted metabolic techniques were utilized to reveal HDM-induced metabolite alterations in 16HBE cells. A total of 45 significant differential metabolites and 5 differential metabolic pathways between the control group and HDM group were identified based on the OPLS-DA model. Finally, three key metabolic pathways, including glycerophospholipid metabolism, galactose metabolism, and alanine, aspartate, and glutamate metabolism, were screened through the integrated analysis of bioinformatics data and untargeted metabolomics data. Taken together, these findings provide valuable insights into the pathophysiology and targeted therapy of asthma and lay a foundation for further research.
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spelling pubmed-98609062023-01-22 The Combination of Bioinformatics Analysis and Untargeted Metabolomics Reveals Potential Biomarkers and Key Metabolic Pathways in Asthma Huang, Fangfang Yu, Jinjin Lai, Tianwen Luo, Lianxiang Zhang, Weizhen Metabolites Article Asthma is a complex chronic airway inflammatory disease that seriously impacts patients’ quality of life. As a novel approach to exploring the pathogenesis of diseases, metabolomics provides the potential to identify biomarkers of asthma host susceptibility and elucidate biological pathways. The aim of this study was to screen potential biomarkers and biological pathways so as to provide possible pharmacological therapeutic targets for asthma. In the present study, we merged the differentially expressed genes (DEGs) of asthma in the GEO database with the metabolic genes obtained by Genecard for bioinformatics analysis and successfully screened out the metabolism-related hub genes (HIF1A, OCRL, NNMT, and PER1). Then, untargeted metabolic techniques were utilized to reveal HDM-induced metabolite alterations in 16HBE cells. A total of 45 significant differential metabolites and 5 differential metabolic pathways between the control group and HDM group were identified based on the OPLS-DA model. Finally, three key metabolic pathways, including glycerophospholipid metabolism, galactose metabolism, and alanine, aspartate, and glutamate metabolism, were screened through the integrated analysis of bioinformatics data and untargeted metabolomics data. Taken together, these findings provide valuable insights into the pathophysiology and targeted therapy of asthma and lay a foundation for further research. MDPI 2022-12-23 /pmc/articles/PMC9860906/ /pubmed/36676950 http://dx.doi.org/10.3390/metabo13010025 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Huang, Fangfang
Yu, Jinjin
Lai, Tianwen
Luo, Lianxiang
Zhang, Weizhen
The Combination of Bioinformatics Analysis and Untargeted Metabolomics Reveals Potential Biomarkers and Key Metabolic Pathways in Asthma
title The Combination of Bioinformatics Analysis and Untargeted Metabolomics Reveals Potential Biomarkers and Key Metabolic Pathways in Asthma
title_full The Combination of Bioinformatics Analysis and Untargeted Metabolomics Reveals Potential Biomarkers and Key Metabolic Pathways in Asthma
title_fullStr The Combination of Bioinformatics Analysis and Untargeted Metabolomics Reveals Potential Biomarkers and Key Metabolic Pathways in Asthma
title_full_unstemmed The Combination of Bioinformatics Analysis and Untargeted Metabolomics Reveals Potential Biomarkers and Key Metabolic Pathways in Asthma
title_short The Combination of Bioinformatics Analysis and Untargeted Metabolomics Reveals Potential Biomarkers and Key Metabolic Pathways in Asthma
title_sort combination of bioinformatics analysis and untargeted metabolomics reveals potential biomarkers and key metabolic pathways in asthma
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9860906/
https://www.ncbi.nlm.nih.gov/pubmed/36676950
http://dx.doi.org/10.3390/metabo13010025
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