ArticleBioMed research international2020
Identification of Key Modules, Hub Genes, and Noncoding RNAs in Chronic Rhinosinusitis with Nasal Polyps by Weighted Gene Coexpression Network Analysis.
Article in BioMed research international, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed, 16 citations in OpenAlex.
- Identification of DIO2 as a Molecular Therapeutic Target for Depression in Chronic Rhinosinusitis: A Comprehensive Bioinformatics and Experimental Study.Biochemical genetics · 2026Article
- Dysregulated glycosaminoglycan biosynthesis and retinoid metabolism in chronic rhinosinusitis with nasal polyps: insights from a comprehensive transcriptomic analysis.Frontiers in immunology · 2026Article
- Immuno-transcriptomic analysis based on machine learning identifies immunity signature genes of chronic rhinosinusitis with nasal polyps.Scientific reports · 2025Article
- Identification of Potential Feature Genes in CRSwNP Using Bioinformatics Analysis and Machine Learning Strategies.Journal of inflammation research · 2024Article
- Long non-coding RNA MALAT1 promotes Th2 differentiation by regulating microRNA-135b-5p/GATA-3 axis in children with allergic rhinitis.The Kaohsiung journal of medical sciences · 2022Article
- Construction and analysis of a ceRNA network and patterns of immune infiltration in chronic rhinosinusitis with nasal polyps: based on data mining and experimental verification.Scientific reports · 2022Article
- Identification of key genes and pathways in chronic rhinosinusitis with nasal polyps and asthma comorbidity using bioinformatics approaches.Frontiers in immunology · 2022Article
- Identification of lncRNA Biomarkers and LINC01198 Promotes Progression of Chronic Rhinosinusitis with Nasal Polyps through Sponge miR-6776-5p.BioMed research international · 2022Article
- Article
- Gene co-expression network analysis reveals immune cell infiltration as a favorable prognostic marker in non-uterine leiomyosarcoma.Scientific reports · 2021Article
- Identification of key genes associated with sepsis patients infected by staphylococcus aureus through weighted gene co-expression network analysis.American journal of translational research · 2021Article
- Transcriptomic Signatures and Functional Network Analysis of Chronic Rhinosinusitis With Nasal Polyps.Frontiers in genetics · 2021Article
- Correlations between stemness indices for hepatocellular carcinoma, clinical characteristics, and prognosis.American journal of translational research · 2020Article
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Chronic rhinosinusitis with nasal polyps (CRSwNP) is a chronic inflammatory disease with relatively easy recurrence. However, the precise molecular mechanisms of this disease are poorly known. Based on gene sequencing data obtained from the Gene Expression Omnibus (GEO) database, we constructed coexpression networks by weighted gene coexpression network analysis (WGCNA). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed by the Database for Annotation, Visualization, and Integrated Discovery (DAVID). The core gene of pathogenesis, CRSwNP, was screened by protein-protein interaction data (PPI) from the HPRD database. Unsupervised clustering was applied to screen hub genes related to the phenotype of CRSwNP. Blue and turquoise modules were found to be most significantly related to the pathogenicity of CRSwNP. Functional enrichment analysis showed that cell proliferation in the blue modules, the apoptotic process in the turquoise module, and the cancer pathway in both modules were mostly significantly correlated with the development of CRSwNP. The noncoding RNAs (long noncoding RNA and microRNA) and the top 10 core genes in each module were found to be associated with the pathogenesis of CRSwNP. A total of nine hub genes were identified to be related to the CRSwNP phenotype. By qRT-PCR analysis,
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