ArticleInflammation research : official journal of the European Histamine Research Society ... [et al.]2026
Comprehensive immunological and molecular analysis revealed inflammation-related diagnostic signatures in chronic rhinosinusitis.
Article in Inflammation research : official journal of the European Histamine Research Society ... [et al.], 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
OBJECTIVE AND
designThis study aimed to identify robust diagnostic biomarkers and characterize molecular subtypes of chronic rhinosinusitis (CRS) by integrating multi-omics data with machine learning, utilizing a case–control design with patient samples. MATERIAL OR SUBJECTS: The analysis incorporated gene expression data from bulk and single-cell RNA sequencing datasets; validation experiments used clinical samples from human CRS patients and control subjects. TREATMENT: Not applicable.
methodsWe analyzed differentially expressed genes and immune cell infiltration from transcriptomic data, employed machine learning algorithms to select diagnostic genes and build a predictive model, and validated key targets using quantitative real-time PCR and dual-fluorescence immunohistochemical staining.
resultsMachine learning identified six inflammation-related genes (SPI1, IFITM3, ITGAM, BCL2A1, HLA-DPB1, PLA2G7) as a diagnostic signature, with predictive models demonstrating strong diagnostic performance. Validation confirmed significant differential expression of BCL2A1 and PLA2G7 in CRS patient samples compared to controls.
conclusionsThis integrative analysis highlights the utility of computational approaches for discovering CRS biomarkers and subtypes, implicating specific genes in inflammation-associated pathways and paving the way for precision diagnostics and targeted therapies.
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