Influence of Differentially Expressed Genes on Thyroid CancerThyroid carcinoma poses a heavy disease burden on the global health. The potential influence of some differentially expressed genes on the progression of thyroid carcinoma was still incomplete. In this study, we integrated transcriptome data with clinical data to investigate the relationship between them in thyroid cancer patients. First, the gene expression profile (GSE129562) from Gene Expression Omnibus was used to identify differentially expressed genes. Secondly, gene ontology function and Kyoto encyclopedia of genes and genomes pathway enrichment analyses of the differentially expressed genes were performed. Thirdly, protein-protein interactions of the differentially expressed genes were constructed. Between T1aN1b or T3N1b thyroid carcinoma and its paired thyroid normal tissue, a total of 729 differentially expressed genes were identified through our analysis, of which 405 genes were up-regulated and 324 genes were down-regulated. Of those differentially expressed genes, 138 differentially expressed genes were identified as immune-related genes. Their functions can be classified as antigen processing and presentation, antimicrobials, B cell receptor signaling pathway, chemokine receptors and so on. Gene ontology enrichment analysis illustrated that these differentially expressed genes were mostly enriched into extracellular matrix structural constituent, integrin binding and so on. Kyoto encyclopedia of genes and genomes enrichment analysis illustrated that these differentially expressed genes were mostly enriched into extracellular matrix-receptor interaction, focal adhesion and so on. The top 4 differentially expressed genes with the highest degrees in the protein-protein network are syndecan 1, tyrosine-protein kinase Fyn, decorin and thrombospondin-1. The mutations in the gene solute carrier family 34 member 2 and tenascin C are arguably the most common. Our results demonstrate the potential influence of some differentially expressed genes on the progression of thyroid cancer, provides a comprehensive bioinformatics analysis of the pathogenesis, which may contribute to future investigation into the molecular mechanisms and biomarkers.
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