Thyroid cancer (TC) is one of the most common endocrine malignancies, and the incidence of TC has almost tripled over the past three decades. This increase may partially own to overdiagnosis and approximately 15-30% of cytological indeterminate thyroid nodules cannot be evaluated by means of fine-needle aspiration. The present study aimed to identify potential crucial genes of PTC and provide new sights into improving the diagnosis of thyroid lesions for future study. We adopted an integrated analysis of Gene expression profiles of PTC patients and adjacent normal controls and data from The Cancer Genome Atlas databases (TCGA). The differentially expressed genes (DEGs) were screened using the Limma package in R software. Connectivity Map (CMap) was used to predict potential drugs for PTC. STRING and Cytoscape software were employed to perform GO, KEGG pathway enrichment analysis and module analysis for DEGs. RT-qPCR was used to validate hub genes screened using module analysis. A total of 218 DEGs were screened, including 55 down-regulated and 163 up-regulated DEGs. GO analysis showed that these DEGs were primary enriched in cell adhesion, extracellular region and glycosaminoglycan binding. KEGG pathway analysis revealed that DEGs primarily participated in ECM-receptor interaction. PPI network and module analysis identified seven-hub genes, including FN1, SERPINA1, ECM1, MMRN1, PROS1, CFD, TIMP1. RT-qPCR results validated that the expression levels of seven-hub genes were consistent with the bioinformatics analysis. These findings have identified seven-hub genes which may helpful for the development of gene panel for thyroid nodules diagnosis.
Keywords: Bioinformatics; Differentially-expressed genes; Papillary thyroid cancer.
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