Abstract
Background: The recurrence rate for papillary thyroid carcinoma (PTC) after surgery is high, which is a significant issue for patients regarding with low-grade malignancy. We built a novel predictive model with metastasis-related genes (MTGs) and relevant clinical parameters for predicting progression-free interval (PFI) after surgery for PTC.Methods: We performed a bioinformatic analysis of integrated PTC datasets with the MTGs to identify differentially expressed MTGs (DE-MTGs). Then we generated PFI-related DE-MTGs and established a 14-gene signature using Lasso-Penalty regression. Finally, we established a signature and clinical parameters-based nomogram for predicting the PFI of PTC . We then validated the efficacy of the signature in marking off high risk patients; the nomogram's performance in predicting PFI was also evaluated with receiver operating characteristic (ROC) curve and Harrell's concordance index (C-index).Results: We identified 155 DE-MTGs related to PFI in PTC. The functional enrichment analysis showed that the DE-MTGs were associated with important oncogenic process. Consequently, we found a novel 14-gene signature. The 14-gene signature could distinguish patients with poorer prognosis and predicted PFI accurately. The signature was a significant independent prognostic factor in PTC. Finally, we built a nomogram by including the signature and relevant clinical factors. Validation analysis showed that the nomogram’s efficacy was superior to the current clinical risk evaluating system in predicting the recurrence of PTC. Conclusions: The 14-gene signature and nomogram were closely associated with PTC prognosis and may help clinicians improve the individualized prediction of PFI, especially for high-risk patients after surgery.
Development and validation of a novel 14-gene signature for predicting lymph node metastasis in papillary thyroid carcinoma
