A network-based approach to identify key genes between follicular thyroid cancer and follicular thyroid adenoma

Abstract

It is difficult to differentiate follicular thyroid cancer (FTC) from follicular thyroid adenoma (FTA) using fine-needle aspiration and imaging. This study aims to identify potential biomarkers to distinguish FTC from FTA. In recent years, microarray technology had a significant impact on our understanding of gene expression patterns in various diseases. Through analyzing microarray data along with bioinformatics, potential biomarkers can be identified in a query disease. To analyze the differentially expressed genes (DEGs), a gene expression profile of follicular thyroid tumors (GSE82208) was downloaded from gene expression omnibus (GEO). GO term and KEGG pathway enrichment analysis were carried out using Enrichr. To visualize the interaction of DEGs, we constructed a protein-protein interaction (PPI) network. Betweenness centrality was considered as the main criteria to identify hub genes. Based on GO term results DEGs are related to the cellular response to cadmium ion, metal ion binding, and focal adhesion. Moreover, KEGG pathway enrichment analysis demonstrated that DEGs are associated with Mineral absorption, Fluid shear stress, and atherosclerosis. Finally, CDH1, CDC42, JUN, FOS, KLF4, CTGF, and PAX8 genes were considered as potential biomarkers to differentiate FTC from FTA.