Related Experiment Video
Updated: Oct 16, 2025
A Component-resolved Diagnostic Approach for a Study on Grass Pollen Allergens in Chinese Southerners with Allergic Rhinitis and/or Asthma
Published on: June 4, 2017
IFNG, FCER1A, PCDHB10 expression as a new potential marker of efficacy in grass pollen allergen-specific
Jan Romantowski1, Agnieszka Maciejewska2, Joanna Polanska3
1Department of Allergology, Medical University of Gdansk, Gdansk, Poland.
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.
