Abstract:Background and Aims The incidence of thyroid carcinoma has been steadily increasing and the molecular mechanism underlying the tumorigenesis is still unclear. Thus, exploring the underlying mechanism is of great importance for improving the prognosis of PTC patients. Studies demonstrated that m6A methylation regulators are widely involved in the occurrence and development of cancers and have superior prognostic value. Therefore, this study was conducted to investigate the expressions of m6A methylation regulators in thyroid cancer and construct a prognostic risk model for thyroid cancer based on m6A methylation regulators using bioinformatics approaches.Methods The gene expression profiles of m6A RNA methylation regulators and the corresponding clinical information were downloaded from The Cancer Genome Atlas (TCGA). The differential expressions of 20 m6A methylation regulators between tumor and normal tissues were analyzed by Wilcoxon test. The thyroid cancer patients were divided into two clusters by consensus clustering, and the differences in clinicopathologic factors and overall survival rate between the two clusters of patients were compared. Subsequently, the risk model was constructed by Lasso Cox regression analysis and its predictive value was evaluated by the area under the ROC (AUC).Results There were 19 m6A methylation regulators that showed significantly different expressions between thyroid cancer and normal tissues (all P<0.05), in which, the HNRNPC, IGF2BP2, FMR1 were remarkably upregulated, while the remaining were down-regulated in thyroid cancer tissue. The clustering analysis showed that the overall survival of cluster 1 was poorer than that of cluster 2 (P<0.05), while the incidence of cervical lymph node metastasis was significantly higher than that of cluster 2 (P<0.01). A prognostic risk model was constructed based on 4 genes (IGF2BP2, RBM15, YTHDF1 and YTHDF3) that were screened by Lasso Cox regression analysis, in which, the high-risk patients had a worse prognosis than that of low-risk patients (P=0.007). The ROC analysis indicated a reliable prediction performance of the model for patients with thyroid cancer (AUC=0.731).Conclusion There are differential expressions in m6A methylation regulators in thyroid cancer, and the constructed prognostic risk model based on the hub m6A methylation regulators in thyroid cancer has better predictive ability, which will provide certain recommendations for clinical decision-making.