ArticleJournal of inflammation research2024
Identification of Potential Feature Genes in CRSwNP Using Bioinformatics Analysis and Machine Learning Strategies.
Article in Journal of inflammation research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Uncovering the immune-related PCD genes in chronic rhinosinusitis with nasal polyps inflammatory progression: a machine learning and functional validation study.Frontiers in cell and developmental biology · 2026Article
- Double-edged mitophagy: balancing inflammation and resolution in lung disease.Clinical science (London, England : 1979) · 2025Review
- Distinct and Shared Molecular Mechanisms in Pediatric Antrochoanal Polyps and Chronic Rhinosinusitis with Nasal Polyps: A Proteomic and Metabolomic Integrative Analysis.Journal of inflammation research · 2025Article
- Unveiling and validating biomarkers related to the IL-10 family in chronic sinusitis with nasal polyps: insights from transcriptomics and single-cell RNA sequencing analysis.Frontiers in molecular biosciences · 2024Article
- Identification and experimental validation of Alzheimer's disease hub genes via bioinformatics and machine learning.Journal of Alzheimer's disease reportsArticle
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Authors and funding
8 authors.
Funding
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Abstract
Purpose: The pathogenesis of CRSwNP is complex and not yet fully explored, so we aimed to identify the pivotal hub genes and associated pathways of CRSwNP, to facilitate the detection of novel diagnostic or therapeutic targets. Methods: Utilizing two CRSwNP sequencing datasets from GEO, differential expression gene analysis, WGCNA, and three machine learning methods (LASSO, RF and SVM-RFE) were applied to screen for hub genes. A diagnostic model was then formulated utilizing hub genes, and the AUC was generated to evaluate the performance of the prognostic model and candidate genes. Hub genes were validated through the validation set and qPCR performed on normal mice and CRSwNP mouse model. Lastly, the ssGSEA algorithm was employed to assess the differences in immune infiltration levels. Results: A total of 239 DEGs were identified, with 170 upregulated and 69 downregulated in CRSwNP. Enrichment analysis revealed that these DEGs were primarily enriched in pathways related to nucleocytoplasmic transport and HIF-1 signaling pathway. Data yielded by WGCNA analysis contained 183 DEGs. The application of three machine learning algorithms identified 11 hub genes. Following concurrent validation analysis with the validation set and qPCR performed after establishing the mouse model confirmed the overexpression of BTBD10, ERAP1, GIPC1, and PEX6 in CRSwNP. The examination of immune cell infiltration suggested that the infiltration rate of type 2 T helper cell and memory B cell experienced a decline in the CRSwNP group. Conversely, the infiltration rates of Immature dendritic cell and Effector memory CD8 T cell were positive correlation. Conclusion: This study successfully identified and validated BTBD10, ERAP1, GIPC1, and PEX6 as potential novel diagnostic or therapeutic targets for CRSwNP, which offers a fresh perspective and a theoretical foundation for the diagnostic prediction and therapeutic approach to CRSwNP.
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