ArticleBMC medical genomics2022
Identifying key genes and functionally enriched pathways in Th2-high asthma by weighted gene co-expression network analysis.
Article in BMC medical genomics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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10 citing papers in PubMed, 17 citations in OpenAlex.
- Frequent exacerbator-a novel endotype of pediatric asthma.The Journal of allergy and clinical immunology · 2025Review
- Integrated analysis reveals that EGR1 promotes epithelial IL33 production in T2 asthma.Journal of translational medicine · 2025Article
- Extreme temperatures modulate gene expression in the airway epithelium of the lungs in mice and asthma patients.Frontiers in medicine · 2025Article
- Elevated SerpinB2 regulates MUC5AC expression via STAT6 signaling in nasal epithelial cells in allergic rhinitis.Frontiers in immunology · 2025Article
- Multi-omics in nasal epithelium reveals three axes of dysregulation for asthma risk in the African Diaspora populations.Nature communications · 2024Article
- Article
- Development and validation of asthma risk prediction models using co-expression gene modules and machine learning methods.Scientific reports · 2023Article
- Reduced urine volume and changed renal sphingolipid metabolism in P2ry14-deficient mice.Frontiers in cell and developmental biology · 2023Article
- A Methylation Diagnostic Model Based on Random Forests and Neural Networks for Asthma Identification.Computational and mathematical methods in medicine · 2022Article
- Identification of key genes and pathways in chronic rhinosinusitis with nasal polyps and asthma comorbidity using bioinformatics approaches.Frontiers in immunology · 2022Article
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Authors and funding
6 authors at 2 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundAsthma is a chronic lung disease characterized by reversible inflammation of the airways. The imbalance of Th1/Th2 cells plays a significant role in the mechanisms of asthma. The aim of this study was to identify asthma-related key genes and functionally enriched pathways in a Th2-high group by using weighted gene coexpression network analysis (WGCNA).
methodsThe gene expression profiles of GSE4302, which included 42 asthma patients and 28 controls, were selected from the Gene Expression Omnibus (GEO). A gene network was constructed, and genes were classified into different modules using WGCNA. Gene ontology (GO) was performed to further explore the potential function of the genes in the most related module. In addition, the expression profile and diagnostic capacity (ROC curve) of hub genes of interest were verified by dataset GSE67472.
resultsIn dataset GSE4302, subjects with asthma were divided into Th2-high and Th2-low groups according to the expression of the SERPINB2, POSTN and CLCA1 genes. A weighted gene coexpression network was constructed, and genes were classified into 7 modules. Among them, the red module was most closely associated with Th2-high asthma, which contained 60 genes. These genes were significantly enriched in different biological processes and molecular functions. A total of 8 hub genes (TPSB2, CPA3, ITLN1, CST1, SERPINB10, CEACAM5, CHD26 and P2RY14) were identified, and the expression levels of these genes (except TPSB2) were confirmed in dataset GSE67472. ROC curve analysis validated that the expression of these 8 genes exhibited excellent diagnostic efficiency for Th2-high asthma and Th2-low asthma.
conclusionsThe study provides a novel perspective on Th2-high asthma by WGCNA, and the hub genes and potential pathways involved may be beneficial for the diagnosis and management of Th2-high asthma.
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