ArticleFrontiers in molecular biosciences2022
Identification of hub genes and immune cell infiltration characteristics in chronic rhinosinusitis with nasal polyps: Bioinformatics analysis and experimental validation.
Article in Frontiers in molecular biosciences, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed.
- Integration of transcriptomics and machine learning to explore inflammatory characteristics and key oxidative stress-related molecules in nasal polyps.Frontiers in immunology · 2026Article
- Predicting gene signature in breast cancer patients with multiple machine learning models.Discover oncology · 2024Article
- Diagnostic implications of ubiquitination-related gene signatures in Alzheimer's disease.Scientific reports · 2024Article
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6 authors.
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Abstract
The aim of our study is to reveal the hub genes related to the pathogenesis of chronic rhinosinusitis with nasal polyps (CRSwNP) and their association with immune cell infiltration through bioinformatics analysis combined with experimental validation. In this study, through differential gene expression analysis, 1,516 upregulated and 1,307 downregulated DEG were obtained from dataset GSE136825 of the GEO database. We identified 14 co-expressed modules using weighted gene co-expression network analysis (WGCNA), among which the most significant positive and negative correlations were MEgreen and MEturquoise modules, containing 1,540 and 3,710 genes respectively. After the intersection of the two modules and DEG, two gene sets-DEG-MEgreen and DEG-MEturquoise-were obtained, containing 395 and 1,168 genes respectively. Through GO term analysis, it was found that immune response and signal transduction are the most important biological processes. We found, based on KEGG pathway enrichment analysis, that osteoclast differentiations, cytokine-cytokine receptor interactions, and neuroactive ligand-receptor interactions are the most important in the two gene sets. Through PPI network analysis, we listed the top-ten genes for the concentrated connectivity of the two gene sets. Next, a few genes were verified by qPCR experiments, and
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