BACKGROUND: Chronic rhinosinusitis with nasal polyps (CRSwNP) is known to have 2 phenotypes in East Asia. Eosinophilic CRSwNP (ECRSwNP), defined as tissue eosinophilia and easily recurrent, is distinguished from other non-eosinophilic CRSwNP (NECRSwNP) types. However, the pathogenesis of each remains unclear. METHODS: Nasal polyp tissues from ECRS (ECRSwNP) and NECRS (NECRSwNP) patients were obtained, and their comprehensive gene expression profiles were investigated by microarray analysis. Bioinformatics approaches (eg, Ingenuity Pathway Analysis [IPA]) were used to interrogate the data sets. RESULTS: Hierarchical clustering and principal component analysis (PCA) collectively showed that ECRSwNP and NECRSwNP had distinct gene expression patterns. Of note, these genes could be divided into 8 distinctive clusters having different expression patterns and functions. Upstream Regulator Analysis revealed that not only T-helper 2 (Th2) and the eosinophilia-related molecules (interleukin 4 [IL4], IL5, and colony stimulating factor 2 [CSF2]) reported so far, but also cell cycle regulators (cyclin dependent kinase inhibitor 1A [CDKNA1] and cyclin D1 [CCND1]) and a tissue fibrosis-related molecule (transforming growth factor β [TGFβ]) were identified in ECRSwNP. On the other hand, mainly interferons (IFNs) and acute inflammatory cytokines (IL1 and IL6) were predicted as upstream regulators in NECRSwNP. CONCLUSION: These results are useful for understanding the molecular basis of the mechanisms of CRSwNP and point to new targets for developing specific biomarkers and personalized therapeutic strategies for CRSwNP.
BACKGROUND:Chronic rhinosinusitis with nasal polyps (CRSwNP) is known to have 2 phenotypes in East Asia. Eosinophilic CRSwNP (ECRSwNP), defined as tissue eosinophilia and easily recurrent, is distinguished from other non-eosinophilic CRSwNP (NECRSwNP) types. However, the pathogenesis of each remains unclear. METHODS: Nasal polyp tissues from ECRS (ECRSwNP) and NECRS (NECRSwNP) patients were obtained, and their comprehensive gene expression profiles were investigated by microarray analysis. Bioinformatics approaches (eg, Ingenuity Pathway Analysis [IPA]) were used to interrogate the data sets. RESULTS: Hierarchical clustering and principal component analysis (PCA) collectively showed that ECRSwNP and NECRSwNP had distinct gene expression patterns. Of note, these genes could be divided into 8 distinctive clusters having different expression patterns and functions. Upstream Regulator Analysis revealed that not only T-helper 2 (Th2) and the eosinophilia-related molecules (interleukin 4 [IL4], IL5, and colony stimulating factor 2 [CSF2]) reported so far, but also cell cycle regulators (cyclin dependent kinase inhibitor 1A [CDKNA1] and cyclin D1 [CCND1]) and a tissue fibrosis-related molecule (transforming growth factor β [TGFβ]) were identified in ECRSwNP. On the other hand, mainly interferons (IFNs) and acute inflammatory cytokines (IL1 and IL6) were predicted as upstream regulators in NECRSwNP. CONCLUSION: These results are useful for understanding the molecular basis of the mechanisms of CRSwNP and point to new targets for developing specific biomarkers and personalized therapeutic strategies for CRSwNP.
Authors: Aiko I Klingler; Whitney W Stevens; Bruce K Tan; Anju T Peters; Julie A Poposki; Leslie C Grammer; Kevin C Welch; Stephanie S Smith; David B Conley; Robert C Kern; Robert P Schleimer; Atsushi Kato Journal: J Allergy Clin Immunol Date: 2020-12-14 Impact factor: 10.793
Authors: Atsushi Kato; Anju T Peters; Whitney W Stevens; Robert P Schleimer; Bruce K Tan; Robert C Kern Journal: Allergy Date: 2021-09-15 Impact factor: 14.710
Authors: Erin E Baschal; Eric D Larson; Tori C Bootpetch Roberts; Shivani Pathak; Gretchen Frank; Elyse Handley; Jordyn Dinwiddie; Molly Moloney; Patricia J Yoon; Samuel P Gubbels; Melissa A Scholes; Stephen P Cass; Herman A Jenkins; Daniel N Frank; Ivana V Yang; David A Schwartz; Vijay R Ramakrishnan; Regie Lyn P Santos-Cortez Journal: Front Genet Date: 2020-01-17 Impact factor: 4.599