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Identification of Molecular Markers Related to Immune Infiltration in Patients with Severe Asthma: A Comprehensive Bioinformatics Analysis Based on the Human Bronchial Epithelial Transcriptome
BACKGROUND: Severe asthma (SA), a heterogeneous inflammatory disease characterized by immune cell infiltration, is particularly difficult to treat and manage. The airway epithelium is an important tissue in regulating innate and adaptive immunity, and targeting airway epithelial cell may contribute...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9649321/ https://www.ncbi.nlm.nih.gov/pubmed/36393974 http://dx.doi.org/10.1155/2022/8906064 |
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author | Jiang, Yong Yan, Qian Zhang, Miaofen Lin, Xueying Peng, Chenwen Huang, Hui-ting Liao, Gang Liu, Qiong Liao, Huili Zhan, Shao-feng Liu, Xiaohong Huang, Xiufang |
author_facet | Jiang, Yong Yan, Qian Zhang, Miaofen Lin, Xueying Peng, Chenwen Huang, Hui-ting Liao, Gang Liu, Qiong Liao, Huili Zhan, Shao-feng Liu, Xiaohong Huang, Xiufang |
author_sort | Jiang, Yong |
collection | PubMed |
description | BACKGROUND: Severe asthma (SA), a heterogeneous inflammatory disease characterized by immune cell infiltration, is particularly difficult to treat and manage. The airway epithelium is an important tissue in regulating innate and adaptive immunity, and targeting airway epithelial cell may contribute to improving the efficacy of asthma therapy. METHODS: Bioinformatics methods were utilized to identify the hub genes and signaling pathways involved in SA. Experiments were performed to determine whether these hub genes and signaling pathways were affected by the differences in immune cell infiltration. RESULTS: The weighted gene coexpression network analysis identified 14 coexpression modules, among which the blue and salmon modules exhibited the strongest associations with SA. The blue module was mainly enriched in actomyosin structure organization and was associated with regulating stem cell pluripotency signaling pathways. The salmon module was mainly involved in cornification, skin development, and glycosphingolipid biosynthesis-lacto and neolacto series. The protein-protein interaction network and module analysis identified 11 hub genes in the key modules. The CIBERSORTx algorithm revealed statistically significant differences in CD8+ T cells (P = 0.013), T follicular helper cells (P = 0.002), resting mast cells (P = 0.004), and neutrophils (P = 0.002) between patients with SA and mild-moderate asthma patients. Pearson's correlation analysis identified 11 genes that were significantly associated with a variety of immune cells. We further predicted the utility of some potential drugs and validated our results in external datasets. CONCLUSION: Our results may help provide a better understanding of the relationship between the airway epithelial transcriptome and clinical data of SA. And this study will help to guide the development of SA-targeted molecular therapy. |
format | Online Article Text |
id | pubmed-9649321 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-96493212022-11-15 Identification of Molecular Markers Related to Immune Infiltration in Patients with Severe Asthma: A Comprehensive Bioinformatics Analysis Based on the Human Bronchial Epithelial Transcriptome Jiang, Yong Yan, Qian Zhang, Miaofen Lin, Xueying Peng, Chenwen Huang, Hui-ting Liao, Gang Liu, Qiong Liao, Huili Zhan, Shao-feng Liu, Xiaohong Huang, Xiufang Dis Markers Research Article BACKGROUND: Severe asthma (SA), a heterogeneous inflammatory disease characterized by immune cell infiltration, is particularly difficult to treat and manage. The airway epithelium is an important tissue in regulating innate and adaptive immunity, and targeting airway epithelial cell may contribute to improving the efficacy of asthma therapy. METHODS: Bioinformatics methods were utilized to identify the hub genes and signaling pathways involved in SA. Experiments were performed to determine whether these hub genes and signaling pathways were affected by the differences in immune cell infiltration. RESULTS: The weighted gene coexpression network analysis identified 14 coexpression modules, among which the blue and salmon modules exhibited the strongest associations with SA. The blue module was mainly enriched in actomyosin structure organization and was associated with regulating stem cell pluripotency signaling pathways. The salmon module was mainly involved in cornification, skin development, and glycosphingolipid biosynthesis-lacto and neolacto series. The protein-protein interaction network and module analysis identified 11 hub genes in the key modules. The CIBERSORTx algorithm revealed statistically significant differences in CD8+ T cells (P = 0.013), T follicular helper cells (P = 0.002), resting mast cells (P = 0.004), and neutrophils (P = 0.002) between patients with SA and mild-moderate asthma patients. Pearson's correlation analysis identified 11 genes that were significantly associated with a variety of immune cells. We further predicted the utility of some potential drugs and validated our results in external datasets. CONCLUSION: Our results may help provide a better understanding of the relationship between the airway epithelial transcriptome and clinical data of SA. And this study will help to guide the development of SA-targeted molecular therapy. Hindawi 2022-11-03 /pmc/articles/PMC9649321/ /pubmed/36393974 http://dx.doi.org/10.1155/2022/8906064 Text en Copyright © 2022 Yong Jiang et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Jiang, Yong Yan, Qian Zhang, Miaofen Lin, Xueying Peng, Chenwen Huang, Hui-ting Liao, Gang Liu, Qiong Liao, Huili Zhan, Shao-feng Liu, Xiaohong Huang, Xiufang Identification of Molecular Markers Related to Immune Infiltration in Patients with Severe Asthma: A Comprehensive Bioinformatics Analysis Based on the Human Bronchial Epithelial Transcriptome |
title | Identification of Molecular Markers Related to Immune Infiltration in Patients with Severe Asthma: A Comprehensive Bioinformatics Analysis Based on the Human Bronchial Epithelial Transcriptome |
title_full | Identification of Molecular Markers Related to Immune Infiltration in Patients with Severe Asthma: A Comprehensive Bioinformatics Analysis Based on the Human Bronchial Epithelial Transcriptome |
title_fullStr | Identification of Molecular Markers Related to Immune Infiltration in Patients with Severe Asthma: A Comprehensive Bioinformatics Analysis Based on the Human Bronchial Epithelial Transcriptome |
title_full_unstemmed | Identification of Molecular Markers Related to Immune Infiltration in Patients with Severe Asthma: A Comprehensive Bioinformatics Analysis Based on the Human Bronchial Epithelial Transcriptome |
title_short | Identification of Molecular Markers Related to Immune Infiltration in Patients with Severe Asthma: A Comprehensive Bioinformatics Analysis Based on the Human Bronchial Epithelial Transcriptome |
title_sort | identification of molecular markers related to immune infiltration in patients with severe asthma: a comprehensive bioinformatics analysis based on the human bronchial epithelial transcriptome |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9649321/ https://www.ncbi.nlm.nih.gov/pubmed/36393974 http://dx.doi.org/10.1155/2022/8906064 |
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