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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...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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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. |
format | Online Article Text |
id | pubmed-9860906 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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