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Analysis of expression profiling data suggests explanation for difficulties in finding biomarkers for nasal polyps.
Lee, E J; Gawel, D R; Lilja, S; Li, X; Schäfer, S; Sysoev, O; Zhang, H; Benson, M.
Afiliação
  • Lee EJ; Centre for Personalized Medicine, Linkoping University, Linkoping, Sweden.
  • Gawel DR; Centre for Personalized Medicine, Linkoping University, Linkoping, Sweden.
  • Lilja S; Centre for Personalized Medicine, Linkoping University, Linkoping, Sweden.
  • Li X; Centre for Personalized Medicine, Linkoping University, Linkoping, Sweden.
  • Schäfer S; Centre for Personalized Medicine, Linkoping University, Linkoping, Sweden.
  • Sysoev O; Division of Statistics and Machine Learning, Department of Computer and Information Science, Linkoping University, Sweden.
  • Zhang H; Division of Statistics and Machine Learning, Department of Computer and Information Science, Linkoping University, Sweden.
  • Benson M; Division of Statistics and Machine Learning, Department of Computer and Information Science, Linkoping University, Sweden.
Rhinology ; 58(4): 360-367, 2020 Aug 01.
Article em En | MEDLINE | ID: mdl-32812533
BACKGROUND: Identification of clinically useful biomarkers for Nasal Polyposis in chronic rhinosinusitis (CRSwNP) has proven dif-ficult. We analyzed gene expression profiling data to find explanations for this. METHODS: We analyzed mRNA expression profiling data, GSE36830, of six uncinate tissues from healthy controls and six NP from CRSwNP patients. We performed Ingenuity Pathway Analysis (IPA) of differentially expressed genes to identify pathways and predicted upstream regulators. RESULTS: We identified 1,608 differentially expressed genes and 177 significant pathways, of which Th1 and Th2 activation pathway and leukocyte extravasation signaling were most significant. We identified 75 upstream regulators whose activity was predicted to be upregulated. These included regulators of known pathogenic and therapeutic relevance, like IL-4. However, only seven of the 75 regulators were actually differentially expressed in NP, namely CSF1, TYROBP, CCL2, CCL11, SELP, ADORA3, ICAM1. Interes-tingly, these did not include IL-4, and four of the seven were receptors. This suggested a potential explanation for the discrepancy between the predicted and observed expression levels of the regulators, namely that the receptors, and not their ligands, were upregulated. Indeed, we found that 10 receptors of key predicted upstream regulators were upregulated, including IL4R. CONCLUSION: Our findings indicate that the difficulties in finding specific biomarkers for CRSwNP depend on the complex underly-ing mechanisms, which include multiple pathways and regulators, each of which may be subdivided into multiple components such as ligands, soluble and membrane-bound receptors. This suggests that combinations of biomarkers may be needed for CRSwNP diagnostics.
Assuntos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Diagnostic_studies / Prognostic_studies Limite: Humans Idioma: En Ano de publicação: 2020 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Diagnostic_studies / Prognostic_studies Limite: Humans Idioma: En Ano de publicação: 2020 Tipo de documento: Article