Bioinformatics analysis to screen key genes in papillary thyroid carcinoma.

摘要: Papillary thyroid carcinoma (PTC) is the most common type of carcinoma, and its incidence has been on increase in recent years. However, molecular mechanism PTC unclear misdiagnosis remains a major issue. Therefore, present study aimed to investigate this mechanism, identify key prognostic biomarkers. Integrated analysis was used explore differentially expressed genes (DEGs) between healthy tissue. To functions pathways associated with DEGs, Gene Ontology, pathway protein-protein interaction (PPI) network analyses were performed. The predictive accuracy DEGs evaluated using receiver operating characteristic (ROC) curve. Based four microarray datasets obtained from Expression Omnibus database, namely GSE33630, GSE27155, GSE3467 GSE3678, total 153 identified, including 66 upregulated 87 downregulated compared controls. These significantly enriched cancer-related phosphoinositide 3-kinase-AKT signaling pathway. PPI screened out genes, acetyl-CoA carboxylase beta, cyclin D1, BCL2, serpin peptidase inhibitor clade A member 1, which may serve important roles pathogenesis. ROC revealed that these had excellent performance, thus verifying their potential for clinical diagnosis. Taken together, findings suggest related are involved events progression facilitate identification