Mitochondrial Dysfunction and Programmed Cell Death in Allergic Rhinitis: Potential Biomarkers and Therapeutic Targets

Background Allergic rhinitis (AR) is a prevalent chronic inflammatory disease, and its pathological mechanisms have not been fully elucidated. This study aims to identify potential biomarkers of AR and explore its role in disease development through integrated bioinformatics analysis. Methods We downloaded GSE75011 and GSE46171 datasets from public database to screen out differentially expressed genes (DEGs) between AR patients and control samples. Using MitoCarta 3.0 and literature appendices, we identified mitochondria-associated genes (MitoRGs) and programmed cell death-related genes (PCDRGs). Using Spearman correlation analysis, we screened out DE MitoRGs-PCDRGs with significant correlations. Further, we constructed a protein-protein interaction (PPI) network using the search tool for the retrieval of interacting genes/proteins (STRING) database and visualized it by Cytoscape software. Using machine learning algorithms, we identified biomarkers of AR from candidate genes. In addition, we analyzed the biological functions and signaling pathways of these biomarkers by Gene Set Enrichment Analysis (GSEA), and assessed the infiltration of immune cells by immunoinfiltration analysis. Results We identified a total of 50 AR-associated DE MitoRGs-PCDRGs that were strongly associated with apoptosis. Through machine learning algorithms, we identified HPDL, PLEKHF1, PFKFB3, and TNFAIP3 as potential biomarkers for AR. The distribution of these biomarkers on chromosomes and the strong correlation between them suggested that they might play a synergistic role in the pathogenesis of AR. GSEA analysis reveals the potential role of these biomarkers in immune response and cell signaling. Immunoinfiltration analysis revealed significant differences in immune cells between AR and normal control (NC) samples, which were closely related to the expression levels of biomarkers. Conclusion This study reveals potential biomarkers of AR through comprehensive analysis and explores their possible mechanisms in disease development. These findings provide new perspectives for the diagnosis and treatment of AR and lay the foundation for future research and clinical applications.