Integration of multisource transcriptomics data to identify potential biomarkers of asthmatic epithelial cells.
- Author:
Lianhua XIE
1
;
Shuxian LU
2
;
Fangyang GUO
1
;
Yifeng ZHANG
3
,
4
;
Qian LIU
4
,
5
Author Information
1. Discipline of Chinese and Western Integrative Medicine, Jiangxi University of Traditional Chinese Medicine, Integrated Chinese and Western Medicine Institute for Children Health & Drug Innovation, Jiangxi University of Traditional Chinese Medicine, Nanchang 330004, China.
2. Medical Transformation Center, Jiangxi University of Traditional Chinese Medicine, Nanchang 330004, China.
3. Discipline of Chinese and Western Integrative Medicine, Jiangxi University of Traditional Chinese Medicine, Medical Transformation Center, Jiangxi University of Traditional Chinese Medicine, Nanchang 330004, China. *Corresponding authors, E-mail: zyf489662913@
4. com.
5. Discipline of Chinese and Western Integrative Medicine, Jiangxi University of Traditional Chinese Medicine, Integrated Chinese and Western Medicine Institute for Children Health & Drug Innovation, Jiangxi University of Traditional Chinese Medicine, Nanchang 330004, China. *Corresponding authors, E-mail: liuqianjxzyydx@
- Publication Type:Journal Article
- MeSH:
Asthma/metabolism*;
Humans;
Epithelial Cells/metabolism*;
Animals;
Biomarkers/metabolism*;
Gene Expression Profiling;
Transcriptome;
Gene Regulatory Networks;
Rats;
Computational Biology
- From:
Chinese Journal of Cellular and Molecular Immunology
2025;41(8):695-705
- CountryChina
- Language:Chinese
-
Abstract:
Objective Through integrative bioinformatics analysis of multi-source transcriptomic data, potential biomarkers to asthma epithelial cells were identified. The expression of these candidate target was subsequently validated in lung tissues and epithelial cells from asthma models. Methods The gene expression profile data of epithelial cells from three asthma patient cohorts and corresponding healthy controls were integrated from the Gene Expression Omnibus (GEO) database. Differential expression analysis and gene co-expression network analysis were performed to identify key genes and biological pathways associated with asthma. The key genes were validated in lung tissues and epithelial cells in asthma animal models. Results Differential gene expression analysis revealed 1121 upregulated and 1484 downregulated genes in epithelial cells from asthma patients compared with healthy controls. The biological pathway enrichment analysis revealed that the upregulated genes were mainly involved in glycosylation processes, whereas the downregulated genes were mainly associated with immune cell differentiation process. The gene co-expression network analysis revealed that module 9, enriched in glycosylation-related pathways, was significantly positively correlated with asthma, whereas module 17, associated with insulin and other signaling pathways, showed a significant negative correlation with asthma. We identified the genes of polypeptide N-acetylgalactosaminyltransferase 5 (GALNT5), pyrroline-5-carboxylate reductase 1 (PYCR1), and carcinoembryonic antigen-related cell adhesion molecule 5 (CEACAM5) as key genes within module 9, all of which were significantly upregulated in asthma. Finally, we validated that the expression levels of GALNT5, PYCR1, and CEACAM5 were significantly upregulated in epithelial cells from asthmatic lung tissue. Additionally, using a rat asthma model, we further confirmed that the protein levels of these three genes were significantly upregulated in lung tissues of the model group. Conclusion Through data integration and experimental validation, this study identified key genes and biological pathways closely associated with asthma pathogenesis. These findings provide a novel theoretical basis and potential targets for the diagnosis and treatment of asthma.
