Abstract
Background: We aimed to build a novel model with metastasis-related genes (MTGs) signature and relevant clinical parameters for predicting progression-free interval (PFI) after surgery for papillary thyroid carcinoma (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 novel MTGs based signature. After that, we validated the signature on multiple datasets and PTC cell lines. Further, we carried out uni- and multivariate analysis to identify independent prognostic characters. Finally, we established a signature and clinical parameters-based nomogram for predicting the PFI of PTC. Results: We identified 155 DE-MTGs related to PFI in PTC. The functional enrichment analysis showed that the DE-MTGs were associated with an essential oncogenic process. Consequently, we found a novel 10-gene signature and could distinguish patients with poorer prognoses and predicted PFI accurately. The novel signature had a C-index of 0.76 and the relevant nomogram had a C-index of 0.80. Also, it was closely related to pivotal clinical characters of datasets and invasiveness of cell lines. And the signature was confirmed 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 satisfying in predicting PTC’s PFI. Conclusions: The MTG 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.
A Novel Metastasis-related Genes Based Signature for Predicting the Progression-free Interval of Patients With Papillary Thyroid Carcinoma
