Bioinformatics analysis of key genes and pathways in Hashimoto thyroiditis tissues

Abstract

Hashimoto thyroiditis (HT) is one of the most common autoimmune diseases, and the incidence of HT continues to increase. Long-term, uncontrollable HT results in thyroid dysfunction and even increases carcinogenesis risks. Since the origin and development of HT involve many complex immune processes, there is no effective therapy for HT on a pathogenesis level. Although bioinformatics analysis has been utilized to seek key genes and pathways of thyroid cancer, few bioinformatics studies that focus on HT pathogenesis and mechanisms have been reported. In the present study, the Gene Expression Omnibus (GEO) dataset (GSE29315) containing 6 HT and 8 thyroid physiological hyperplasia (TPH) samples was downloaded, and differentially expressed gene (DEG) analysis, Gene Ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, protein-protein interaction (PPI) analysis, and gene set enrichment analysis (GSEA) were performed. In total, 85 DEGs, containing 76 upregulated and 9 downregulated DEGS, were identified. The DEGs were mainly enriched in immune and inflammatory response, and the signaling pathways were involved in cytokine interaction and cytotoxicity. Moreover, ten hub genes were identified, and IFN-γ, IFN-α, IL6/JAK/STAT3 and inflammatory pathways may promote the origin and progression of HT. The present studies indicated that exploring DEGs and pathways by bioinformatics analysis has important significance in understanding the molecular mechanisms of HT and providing potential targets for the prevention and treatment of HT.

Integrative Analyses of Genes Associated with Hashimoto’s Thyroiditis

“…When we searched the articles, we found that there was a bioinformatics analysis on HT which used thyroid physiological hyperplasia (TPH) as a control group, not the healthy thyroid tissue, so there is no similarity with our conclusions [ 17 ]. There are no other reports about the discovery of HT hub genes and pathways.…”

“…Until now, several loci have been found to be associated with Hashimoto's thyroiditis, such as HLA-DR, immunoregulatory genes (CD40, FoxP3, CD25, CTLA-4, and PTPN22), and thyroid-specific genes (thyrotropin (TSH) receptors and thyroglobulin) [ 13 – 16 ]. By analyzing the microarray data of GSE29315, Zheng et al reported that 10 hub genes and interferon- γ , IFN- α , IL-6/JAK/STAT3, and inflammatory pathways may promote the occurrence and development of HT [ 17 ]. However, no studies have reported the possible regulatory mechanism of microRNAs related to the occurrence of Hashimoto's thyroiditis.…”

Comparative analysis of thyroid disease and predict them using machine learning techniques

“…• By the help of bioinformatics through using tools and databases, scientists can find cell surface receptor linked signal transduction, leukocyte response, cytokine-cytokine interaction, interferon-gamma actions, different immune responses at various biological state, inflammatory responses, natural killer cell-mediated cytotoxicity, antigen processing and antigen presentation procedure etc (Zheng, 2020). • Through the help of bioinformatics, it can be explored that, if there are any possibilities of graft rejection in hosts.…”

Global view on the pathogenesis of benign thyroid disease based on historical, experimental, biochemical and genetic data, identifying the role of magnesium, selenium, coenzyme Q10 and iron in the context of the unfolded protein response and protein quality control of thyroglobulin

“…Pardo et al [88] reported that genetic changes in Tg, i.e., the p.A2215D thyroglobulin gene mutation, can produce molecule retention in cells, thus involving the endoplasmic reticulum. Using a bioinformatics analysis approach, Zheng et al [89] recently identified key genes associated with Hashimoto's disease. Another player in Hashimoto's thyroiditis involves polymorphisms of the STAT3 gene [90,91] .…”