ArticleFrontiers in medicine2026
Identification of endoplasmic reticulum stress-related biomarkers in asthma and asthma exacerbation.
Article in Frontiers in medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: Endoplasmic reticulum (ER) stress plays a crucial role in airway inflammation and other asthma-related processes. This study aims to identify potential ER stress-related biomarkers in asthma. Methods: Sputum transcriptome data (GSE76262) from 118 moderate-to-severe asthma patients and 21 healthy controls were analyzed to identify ER stress-related differentially expressed genes (DEGs). Functional enrichment, weighted gene co-expression network analysis (WGCNA), and protein-protein interaction (PPI) network construction were performed. Machine learning algorithms (LASSO logistic regression and random forest) were applied to screen key gene signatures. The diagnostic performance was evaluated by ROC curve analysis. An OVA-induced murine asthma model with LPS or poly(I:C) co-administration was established to validate the Expression of the ER stress-related biomarkers. Results: By integrating bioinformatic analysis of the GEO dataset GSE76262 with machine learning approaches, we identified two ER stress-related key genes, Conclusion: We identify
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