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IFNG, FCER1A, PCDHB10 expression as a new potential marker of efficacy in grass pollen allergen-specific immunotherapy

  • Jan Romantowski
  • , Agnieszka Maciejewska
  • , Joanna Polanska
  • , Eliza Wasilewska
  • , Krzysztof Specjalski
  • , Marta Chełminska
  • , Ewa Jassem
  • , Marek Niedoszytko
  • Medical University of Gdańsk

Research output: Contribution to journal › Article › peer-review

3 Citations (Scopus)

Abstract

Introduction: Allergen-specific immunotherapy (AIT) is the core treatment in allergic rhinitis and asthma. Although widely used, some patients do not benefit from treatment and there is no efficacy objective marker. Aim: To define the profile of gene transcripts during the build-up phase of AIT and their comparison to the control group and then search for a viable efficacy marker in relation to patient symptoms. Material and methods: AIT was administered in 22 patients allergic to grass pollen. Analysis of 15 selected transcript expression was performed in whole blood samples taken before AIT (sample A) and after reaching the maintenance dose (sample B). The control group included 25 healthy volunteers (sample C). The primary endpoint was Relative Quantification. The gene expression analysis was followed by clinical evaluation with the use of Allergy Control Score (ACS). Results: Comparison between samples A and B of gene expression showed a significant increase in IFNG expression (p = 0.03). In relation to the control group, pretreatment samples from patients showed higher levels of AFAP1L1 (p = 0.006), COMMD8 (p = 0.001), PIK3CD (p = 0.027) and TWIST2 (p = 0.0003) in univariate analysis. A generalized linear regression model was built according to the Bayesian Information Criterion based on the IFNG, FCER1A and PCDHB10 expression pattern for prediction of the AIT outcome. The model showed a correlation in predicted and observed changes in ACS. Conclusions: There is a significant change in the expression of IFNG during the build-up phase of AIT. The authors propose an in vitro model of AIT efficacy prediction for further validation.

Original languageEnglish
Pages (from-to)665-672
Number of pages8
JournalPostepy Dermatologii I Alergologii
Volume38
Issue number4
DOIs
Publication statusPublished - 2021

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

Keywords

  • Allergen
  • Allergic rhinitis
  • Asthma
  • MRNA

ASJC Scopus subject areas

  • Immunology and Allergy
  • Dermatology

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