Nasal IFN and UPR Transcriptomic Signatures Complement Clinical Predictors of Frequent Exacerbations in Pediatric Asthma

Background: Frequent exacerbations in pediatric asthma cause substantial morbidity, yet tools for prospective risk stratification remain limited. We aimed to identify nasal transcriptomic signatures associated with frequent exacerbations (FE), test their virus responsiveness, and compare their predictive performance with a model based on routine clinical variables. Methods: Nasal RNA-seq data from GSE211158 were analyzed to compare FE and non-frequent exacerbators (NFE) and to derive an Elastic Net transcriptomic model, evaluated by stratified 5-fold cross-validation and an internal hold-out test set. External validation of interferon (IFN) and unfolded protein response/endoplasmic reticulum (UPR/ER) stress modules was performed in the experimental human rhinovirus (HRV-A16) cohort GSE97668. An independent hospital cohort of 134 children with asthma was used to build a multivariable “clinical-only” logistic regression model using age, sex, lung function, inhaled corticosteroid dose, blood eosinophils, total IgE and bedside variables; prior exacerbation count defined FE but was not entered as a predictor. Results: FE children showed upregulation of IFN-stimulated and UPR/ER stress gene sets and attenuation of epithelial barrier programs versus NFE. An Elastic Net–derived transcriptomic signature showed modest discrimination, with cross-validated AUCs clustered around ~0.47 and a hold-out ROC AUC of 0.628. IFN/IED modules were directionally increased in the HRV challenge dataset, with a significant increase in the IFN module (t-test p=0.00474) and a similar but non-significant trend for the IED module (p=0.077). In contrast, the clinical-only model performed poorly (repeated 5-fold CV mean/median AUC = 0.44; hold-out AUC = 0.38). Conclusion: Nasal IFN and UPR transcriptomic signatures define a frequent exacerbator endotype and provide moderate discrimination where clinical-only models perform poorly, suggesting that airway transcriptomics may complement conventional clinical assessment for identifying high-risk children.