Identification of two potential immune-related biomarkers of Graves' disease based on integrated bioinformatics analyses.

Abstract

Graves' disease (GD) is an autoimmune disease, the incidence of which is increasing yearly. GD requires long-life therapy. Therefore, the potential immune-related biomarkers of GD need to be studied. In our study, differentially expressed genes (DEGs) were derived from the online Gene Expression Omnibus (GEO) microarray expression dataset GSE71956. Protein‒protein interaction (PPI) network analyses were used to identify hub genes, which were validated by qPCR. GSEA was used to screen potential pathways and related immune cells. Next, CIBERSORT analysis was used to further explore the immune subtype distribution pattern among hub genes. ROC curves were used to analyze the specificity and sensitivity of hub genes. 44 DEGs were screened from the GEO dataset. Two hub genes, EEF1A1 and EIF4B, were obtained from the PPI network and validated by qPCR (p < 0.05). GSEA was conducted to identify potential pathways and immune cells related to these the two hub genes. Immune cell subtype analysis revealed that hub genes had extensive associations with many different types of immune cells, particularly resting memory CD4+ T cells. AUCs of ROC analysis were 0.687 and 0.733 for EEF1A1 and EIF4B, respectively. Our study revealed two hub genes, EEF1A1 and EIF4B, that are associated with resting memory CD4+ T cells and potential immune-related molecular biomarkers and therapeutic targets of GD.