Immunity, inflammation and disease
Integrative Network Analysis and Machine Learning Identify Cell Adhesion-Related Biomarkers and Immune Heterogeneity in Allergic Rhinitis
By He G and Zhang L.
Abstract
Background
Allergic rhinitis (AR) represents a highly prevalent inflammatory disorder of the nasal mucosa, but molecular indicators capable of capturing its disease-specific transcriptional features remain insufficiently defined. This study aimed to identify cell adhesion-related diagnostic biomarkers for AR and to investigate their immune relevance and molecular heterogeneity.
Methods
Publicly available AR microarray datasets were analyzed, with GSE75011 serving as the discovery dataset and GSE46171 and GSE40037 used to test the reproducibility of the diagnostic model. A predefined adhesion-related gene set was curated from MSigDB to provide the biological search space for biomarker screening. Genes showing disease-associated expression changes were overlapped with this adhesion gene set to obtain AR-associated adhesion genes. Their biological functions and interaction structure were examined through enrichment analysis, protein interaction mapping, and module extraction. Diagnostic candidates were prioritized by integrating three complementary feature-selection strategies: least absolute shrinkage and selection operator regression, random forest modeling, and support vector machine-recursive feature elimination. The selected markers were then assessed for diagnostic discrimination, individualized prediction, calibration performance, and potential clinical net benefit. In addition, immune infiltration, upstream regulatory networks, candidate compounds, and non-negative matrix factorization-based molecular subtypes were explored.