Identification of hub genes and immune cell infiltration characteristics in chronic rhinosinusitis with nasal polyps: Bioinformatics analysis and experimental validation.

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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 FPR2, ITGAM, C3AR1, FCER1G, CYBB in DEG-MEgreen and GNG4, NMUR2, and GNG7 in DEG-MEturquoise were confirmed to be related to the pathogenesis of CRSwNP. NP immune cell infiltration analysis revealed a significant difference in the proportion of immune cells between the NP group and control group. Finally, correlation analysis between target hub genes and immune cells indicated that FPR2 and GNG7 had a positive or negative correlation with some specific immune cells. In summary, the discoveries of these new hub genes and their association with immune cell infiltration are of great significance for uncovering the specific pathogenesis of CRSwNP and searching for disease biomarkers and potential therapeutic targets.", "claims": [{"id": "claim-method", "statement": "As a systematic bioinformatics analysis method, we used the Rstudio weighted gene co-expression network analysis (WGCNA) package (https://cran.r-project.org/web/packages/WGCNA/) in WGCNA.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36060258", "locator": "sections[5] \"Weighted gene co-expression network analysis\" paragraph 2", "quote": "As a systematic bioinformatics analysis method, we used the Rstudio weighted gene co-expression network analysis (WGCNA) package (https://cran.r-project.org/web/packages/WGCNA/) in WGCNA."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Publicly available dataset was analyzed in this study. 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