Network-based meta-analysis in the identification of biomarkers for papillary thyroid cancer.

Abstract Papillary thyroid carcinoma (PTC) has been increasing across the world with incomplete understanding of its pathogenesis. We aimed to investigate gene alterations and biomarkers contributing to PTC development. A total of five eligible microarray datasets including 94 PTC and 81 normal thyroid samples were included to identify gene expression signatures. Using integrative meta-analysis of expression data (INMEX) program, we identified a total of 2699 differentially expressed genes (DEGs) (1333 overexpressed and 1366 underexpressed genes) in PTC relative to normal thyroid samples. The top 100 upregualted and downregulated DEGs identified in the meta-analysis were further validated in The Cancer Genome Atlas (TCGA) dataset for PTC with high consistency. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis revealed pathways in cancer, proteoglycans in cancer, focal adhesion, axon guidance, and ECM-receptor interaction among the top 5 most enriched pathways. Network-based meta-analysis identified FN1 and TRAF6 to be the most highly ranked hub genes among the overexpressed and underexpressed genes, respectively, both of which are involved in pathways in cancer. The most enriched terms for Gene Ontology (GO) of biological processes, cellular component, and molecular function were signal transduction, cytoplasm, and protein binding, respectively. Our meta-analysis comprehensively investigated DEGs, hub genes, enriched pathways and GO terms for PTC, which might provide additional approaches to explore the molecular mechanisms underlying the pathophysiology of PTC, and identify biomarkers and therapeutic targets toward PTC.