Identification and analysis of lipid metabolism-related genes in allergic rhinitis

Abstract

Abstract Background Studies have shown that the lipid metabolism mediator leukotriene and prostaglandins are associated with the pathogenesis of allergic rhinitis (AR). The aim of this study was to identify key lipid metabolism-related genes (LMRGs) related to the diagnosis and treatment of AR. Materials and methods AR-related expression datasets (GSE75011, GSE46171) were downloaded through the Gene Expression Omnibus (GEO) database. First, weighted gene co-expression network analysis (WGCNA) was used to get AR-related genes (ARRGs). Next, between control and AR groups in GSE75011, differentially expressed genes (DEGs) were screened, and DEGs were intersected with LMRGs to obtain lipid metabolism-related differentially expressed genes (LMR DEGs). Protein-protein interaction (PPI) networks were constructed for these LMR DEGs. Hub genes were then identified through stress, radiality, closeness and edge percolated component (EPC) analysis and intersected with the ARRGs to obtain candidate genes. Biomarkers with diagnostic value were screened via receiver operating characteristic (ROC) curves. Differential immune cells screened between control and AR groups were then assessed for correlation with the diagnostic genes, and clinical correlation analysis and enrichment analysis were performed. Finally, real-time fluorescence quantitative polymerase chain reaction (RT-qPCR) was made on blood samples from control and AR patients to validate these identified diagnostic genes. Results 73 LMR DEGs were obtained, which were involved in biological processes such as metabolism of lipids and lipid biosynthetic processes. 66 ARRGs and 22 hub genes were intersected to obtain four candidate genes. Three diagnostic genes (LPCAT1, SGPP1, SMARCD3) with diagnostic value were screened according to the AUC > 0.7, with markedly variant between control and AR groups. In addition, two immune cells, regulatory T cells (Treg) and T follicular helper cells (TFH), were marked variations between control and AR groups, and SMARCD3 was significantly associated with TFH. Moreover, SMARCD3 was relevant to immune-related pathways, and correlated significantly with clinical characteristics (age and sex). Finally, RT-qPCR results indicated that changes in the expression of LPCAT1 and SMARCD3 between control and AR groups were consistent with the GSE75011 and GSE46171. Conclusion LPCAT1, SGPP1 and SMARCD3 might be used as biomarkers for AR.