Identification of Potential Key Genes in Anaplastic Thyroid Cancer Using Bioinformatics AnalysisIdentification of Potential Key Genes in Anaplastic Thyroid Cancer using Bioinformatics Analysis

Abstract BackgroundAnaplastic thyroid cancer (ATC) has a high degree of malignancy and a poor prognosis. Its incidence accounts for approximately 10-15% of all thyroid cancers. The purpose of this study was to determine the differentially expressed genes (DEGs) of ATC through biometric analysis technology, clarify the potential interactions between them, and screen genes related to the prognosis of ATC.MethodsThe GSE29265, GSE65144, GSE33630, and GSE85457 expression profiles downloaded from the Gene Expression Omnibus database (GEO) contained a total of 117 tissue samples (81 normal thyroid tissue samples and 36 ATC samples). The four datasets were integrated and analyzed by the limma packages to obtain DEGs. With these DEGs, we performed gene ontology functional annotation and Kyoto Encyclopedia of Genes and Genomes pathway analyses using the Database for Annotation, Visualization and Integrated Discovery, protein-protein interaction (PPI) analysis using Cytoscape, and survival analysis using the Kaplan-Meier (KM) plotter.Results.After R integration analysis of the four datasets, 764 DEGs were obtained, i.e., 314 upregulated and 450 downregulated genes. Among the hub DEGs obtained in the PPI network, the expression levels of thymidylate synthase (TYMS), fibronectin 1, chordin-like 1, syndecan 2, integrin alpha 2, collagen type I alpha 1 chain, collagen type IX alpha 3 chain (COL9A3), and collagen type XXIII alpha 1 chain (COL23A1) were associated with ATC prognosis. These results showed that the overall survival and recurrence-free survival of TYMS, COL9A3, and COL23A1 were statistically significant in our KM plotter survival analysis; thus, these DEGs may be used as potential biomarkers of ATC.ConclusionThis study identified several potential target genes and pathways that may affect the development of ATC. These findings provide new insights for the detection of novel diagnostic and therapeutic biomarkers for ATC.