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Identification of the molecular subgroups in asthma by gene expression profiles: airway inflammation implications
BACKGROUND: Asthma is a heterogeneous disease and different phenotypes based on clinical parameters have been identified. However, the molecular subgroups of asthma defined by gene expression profiles of induced sputum have been rarely reported. METHODS: We re-analyzed the asthma transcriptional pro...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8742931/ https://www.ncbi.nlm.nih.gov/pubmed/35000593 http://dx.doi.org/10.1186/s12890-022-01824-3 |
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author | Li, Min Zhu, Wenye Saeed, Ummair Sun, Shibo Fang, Yan Wang, Chu Luo, Zhuang |
author_facet | Li, Min Zhu, Wenye Saeed, Ummair Sun, Shibo Fang, Yan Wang, Chu Luo, Zhuang |
author_sort | Li, Min |
collection | PubMed |
description | BACKGROUND: Asthma is a heterogeneous disease and different phenotypes based on clinical parameters have been identified. However, the molecular subgroups of asthma defined by gene expression profiles of induced sputum have been rarely reported. METHODS: We re-analyzed the asthma transcriptional profiles of the dataset of GSE45111. A deep bioinformatics analysis was performed. We classified 47 asthma cases into different subgroups using unsupervised consensus clustering analysis. Clinical features of the subgroups were characterized, and their biological function and immune status were analyzed using Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) and single sample Gene Set Enrichment Analysis (ssGSEA). Weighted gene co-expression network analysis (WGCNA) and protein–protein interaction (PPI) network were performed to identify key gene modules and hub genes. RESULTS: Unsupervised consensus clustering of gene expression profiles in asthma identified two distinct subgroups (Cluster I/II), which were significantly associated with eosinophilic asthma (EA) and paucigranulocytic asthma (PGA). The differentially expressed genes (DEGs) between the two subgroups were primarily enriched in immune response regulation and signal transduction. The ssGSEA suggested the different immune infiltration and function scores between the two clusters. The WGCNA and PPI analysis identified three hub genes: THBS1, CCL22 and CCR7. ROC analysis further suggested that the three hub genes had a good ability to differentiate the Cluster I from the Cluster II. CONCLUSIONS: Based on the gene expression profiles of the induced sputum, we identified two asthma subgroups, which revealed different clinical characteristics, gene expression patterns, biological functions and immune status. The transcriptional classification confirms the molecular heterogeneity of asthma and provides a framework for more in-depth research on the mechanisms of asthma. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12890-022-01824-3. |
format | Online Article Text |
id | pubmed-8742931 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-87429312022-01-10 Identification of the molecular subgroups in asthma by gene expression profiles: airway inflammation implications Li, Min Zhu, Wenye Saeed, Ummair Sun, Shibo Fang, Yan Wang, Chu Luo, Zhuang BMC Pulm Med Research BACKGROUND: Asthma is a heterogeneous disease and different phenotypes based on clinical parameters have been identified. However, the molecular subgroups of asthma defined by gene expression profiles of induced sputum have been rarely reported. METHODS: We re-analyzed the asthma transcriptional profiles of the dataset of GSE45111. A deep bioinformatics analysis was performed. We classified 47 asthma cases into different subgroups using unsupervised consensus clustering analysis. Clinical features of the subgroups were characterized, and their biological function and immune status were analyzed using Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) and single sample Gene Set Enrichment Analysis (ssGSEA). Weighted gene co-expression network analysis (WGCNA) and protein–protein interaction (PPI) network were performed to identify key gene modules and hub genes. RESULTS: Unsupervised consensus clustering of gene expression profiles in asthma identified two distinct subgroups (Cluster I/II), which were significantly associated with eosinophilic asthma (EA) and paucigranulocytic asthma (PGA). The differentially expressed genes (DEGs) between the two subgroups were primarily enriched in immune response regulation and signal transduction. The ssGSEA suggested the different immune infiltration and function scores between the two clusters. The WGCNA and PPI analysis identified three hub genes: THBS1, CCL22 and CCR7. ROC analysis further suggested that the three hub genes had a good ability to differentiate the Cluster I from the Cluster II. CONCLUSIONS: Based on the gene expression profiles of the induced sputum, we identified two asthma subgroups, which revealed different clinical characteristics, gene expression patterns, biological functions and immune status. The transcriptional classification confirms the molecular heterogeneity of asthma and provides a framework for more in-depth research on the mechanisms of asthma. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12890-022-01824-3. BioMed Central 2022-01-09 /pmc/articles/PMC8742931/ /pubmed/35000593 http://dx.doi.org/10.1186/s12890-022-01824-3 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis 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 Li, Min Zhu, Wenye Saeed, Ummair Sun, Shibo Fang, Yan Wang, Chu Luo, Zhuang Identification of the molecular subgroups in asthma by gene expression profiles: airway inflammation implications |
title | Identification of the molecular subgroups in asthma by gene expression profiles: airway inflammation implications |
title_full | Identification of the molecular subgroups in asthma by gene expression profiles: airway inflammation implications |
title_fullStr | Identification of the molecular subgroups in asthma by gene expression profiles: airway inflammation implications |
title_full_unstemmed | Identification of the molecular subgroups in asthma by gene expression profiles: airway inflammation implications |
title_short | Identification of the molecular subgroups in asthma by gene expression profiles: airway inflammation implications |
title_sort | identification of the molecular subgroups in asthma by gene expression profiles: airway inflammation implications |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8742931/ https://www.ncbi.nlm.nih.gov/pubmed/35000593 http://dx.doi.org/10.1186/s12890-022-01824-3 |
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