Integrated bioinformatics analysis of shared biomarkers and mechanisms in united airway allergic disease and Alzheimer's disease

Abstract

Alzheimer's disease (AD) is a leading cause of dementia, whereas united airway allergic disease (UAAD) affects both the upper and lower airways. Epidemiological studies suggest a higher risk of AD in individuals with allergic diseases, hinting at shared molecular mechanisms. This study aims to identify key molecular biomarkers for both diseases and uncover potential mechanisms. Data for AD and UAAD were obtained from the GEO database. Weighted gene co-expression network analysis (WGCNA) identified differentially expressed disease-related genes (DEDRGs). Functional enrichment analysis was subsequently performed for gene functions. Single-sample GSEA (ssGSEA) was used to calculate gene set scores. LASSO regression was used to establish a diagnostic model. Immune infiltration was predicted via the CIBERSORT and ESTIMATE algorithms. Machine learning and protein-protein interactions identified key genes, which were validated through RT-qPCR in airway and brain tissues from mouse models. This study identified GATA2, SLC39A8, and AR as key genes, which were strongly correlated with both diseases. The diagnostic models demonstrated high performance, and RT-qPCR results confirmed elevated co-expression levels of these genes in the disease groups. GATA2 has emerged as a crucial gene, involved in immune system activation and the JAK-STAT pathway. This study developed a diagnostic model incorporating GATA2, SLC39A8, and AR, highlighting their role in immune infiltration and disease progression, especially GATA2. These findings suggest a shared mechanism between AD and UAAD, offering new targets for future research and therapy.