Construction of a Signature Model to Predict the Radioactive Iodine Response of Papillary Thyroid Cancer

Abstract

Papillary thyroid cancer (PTC) accounts for about 90% of thyroid cancer. There are approximately 20%–30% of PTC patients showing disease persistence/recurrence and resistance to radioactive iodine (RAI) treatment. For these PTC patients with RAI refractoriness, the prognosis is poor. In this study, we aimed to establish a comprehensive prognostic model covering multiple signatures to increase the predictive accuracy for progression-free survival (PFS) of PTC patients with RAI treatment. The expression profiles of mRNAs and miRNAs as well as the clinical information of PTC patients were extracted from TCGA and GEO databases. A series of bioinformatics methods were successfully applied to filtrate a two-RNA model (IPCEF1 and hsa-mir-486-5p) associated with the prognosis of RAI-therapy. Finally, the RNA-based risk score was calculated based on the Cox coefficient of the individual RNA, which achieved good performances by the time-dependent receiver operating characteristic (tROC) curve and PFS analyses. Furthermore, the predictive power of the nomogram, integrated with the risk score and clinical parameters (age at diagnosis and tumor stage), was assessed by tROC curves. Collectively, our study demonstrated high precision in predicting the RAI response of PTC patients.