ArticleBMC medical genomics2021
Identification of biomarkers and pathogenesis in severe asthma by coexpression network analysis.
Article in BMC medical genomics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 21 citations in OpenAlex.
- Alpha-1 Antitrypsin Deficiency Beyond COPD and Emphysema: A Narrative Review.Medical sciences (Basel, Switzerland) · 2026Review
- Multi-omic integration reveals alterations in nasal mucosal biology that mediate air pollutant effects on allergic rhinitis.Allergy · 2024Article
- Co-Expression Network and Machine Learning Analysis of Transcriptomics Data Identifies Distinct Gene Signatures and Pathways in Lesional and Non-Lesional Atopic Dermatitis.Journal of personalized medicine · 2024Article
- CDC167 exhibits potential as a biomarker for airway inflammation in asthma.Mammalian genome : official journal of the International Mammalian Genome Society · 2024Article
- ITGAM-macrophage modulation as a potential strategy for treating neutrophilic Asthma: insights from bioinformatics analysis and in vivo experiments.Apoptosis : an international journal on programmed cell death · 2024Article
- Decreased TLR7 expression was associated with airway eosinophilic inflammation and lung function in asthma: evidence from machine learning approaches and experimental validation.European journal of medical research · 2024Article
- Integrative analysis identifies gene signatures mediating the effect of DNA methylation on asthma severity and lung function.Clinical epigenetics · 2024Article
- Development and validation of asthma risk prediction models using co-expression gene modules and machine learning methods.Scientific reports · 2023Article
- Identifying key genes and functionally enriched pathways in Th2-high asthma by weighted gene co-expression network analysis.BMC medical genomics · 2022Article
- Construction of Severe Eosinophilic Asthma Related Competing Endogenous RNA Network by Weighted Gene Co-Expression Network Analysis.Frontiers in pharmacology · 2022Article
- Perinatal Exposure to Nicotine Alters Sperm RNA Profiles in Rats.Frontiers in endocrinology · 2022Article
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundSevere asthma is a heterogeneous inflammatory disease. The increase in precise immunotherapy for severe asthmatics requires a greater understanding of molecular mechanisms and biomarkers. In this study, we aimed to identify the underlying mechanisms and hub genes that determine asthma severity.
methodsDifferentially expressed genes (DEGs) were identified based on bronchial epithelial brushings from mild and severe asthmatics. Then, weighted gene coexpression network analysis (WGCNA) was used to identify gene networks and the module most significantly associated with asthma severity. Furthermore, hub gene screening and functional enrichment analysis were performed. Replication with another dataset was conducted to validate the hub genes.
resultsDEGs from 14 mild and 11 severe asthmatics were subjected to WGCNA. Six modules associated with asthma severity were identified. Three modules were positively correlated (P < 0.001) with asthma severity and contained genes that were upregulated in severe asthmatics. Functional enrichment analysis showed that genes in the most significant module were mainly enriched in neutrophil activation and degranulation, and cytokine receptor interaction. Hub genes included CXCR1, CXCR2, CCR1, CCR7, TLR2, FPR1, FCGR3B, FCGR2A, ITGAM, and PLEK; CXCR1, CXCR2, and TLR2 were significantly related to asthma severity in the validation dataset. The combination of ten hub genes exhibited a moderate ability to distinguish between severe and mild-moderate asthmatics.
conclusionOur results identified biomarkers and characterized potential pathogenesis of severe asthma, providing insight into treatment targets and prognostic markers.
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