Abstract
Abstract
Objective
The aim of our study is to find a better way to identify a group of papillary thyroid carcinoma (PTC) with more aggressive behaviors and to provide a prediction model for lymph node metastasis to assist in clinic practice.
Methods
Targeted sequencing of DNA/RNA was used to detect genetic alterations. Gene expression level was measured by quantitative real-time PCR, western blotting or immunohistochemistry. CCK8, transwell assay and flow cytometry were used to investigate the effects of concomitant gene alterations in PTC. LASSO-logistics regression algorithm was used to construct a nomogram model integrating radiomic features, mutated genes and clinical characteristics.
Results
172 high-risk variants and 7 fusion types were detected. The mutation frequencies in BRAF, TERT, RET, ATM and GGT1 were significantly higher in cancer tissues than benign nodules. Gene fusions were detected in 16 samples (2 at the DNA level and 14 at the RNA level). ATM mutation (ATMMUT) was frequently accompanied by BRAFMUT, TERTMUT or gene fusions. ATMMUT alone or ATM co-mutations were significantly positively correlated with lymph node metastasis. Accordingly, ATM knock-down PTC cells bearing BRAFV600E, KRASG12R or CCDC6-RET had higher proliferative ability and more aggressive potency than cells without ATM knock-down in vitro. Furthermore, combining gene alterations and clinical features significantly improved the predictive efficacy for lymph node metastasis of radiomic features, from 71.5 to 87.0%.
Conclusions
Targeted sequencing of comprehensive genetic alterations in PTC has high prognostic value. These alterations, in combination with clinical and radiomic features, may aid in predicting invasive PTC with higher accuracy.
Funder
National Natural Science Foundation of China
National Science and Technology Support Program of China
Publisher
Springer Science and Business Media LLC
Reference58 articles.
1. Siegel RL, Miller KD, Jemal A. Cancer statistics, 2019. CA Cancer J Clin. 2019;69:7–34.
2. Siegel RL, Miller KD, Jemal A. Cancer statistics, 2020. CA Cancer J Clin. 2020;70:7–30.
3. Zheng RS, Sun KX, Zhang SW, Zeng HM, Zou XN, Chen R, et al. [Report of cancer epidemiology in China, 2015]. Zhonghua Zhong Liu Za Zhi. 2019;41:19–28.
4. Cabanillas ME, McFadden DG, Durante C. Thyroid cancer. Lancet. 2016;388:2783–95.
5. Nikiforova MN, Mercurio S, Wald AI, Barbi de Moura M, Callenberg K, Santana-Santos L, et al. Analytical performance of the ThyroSeq v3 genomic classifier for cancer diagnosis in thyroid nodules. Cancer. 2018;124:1682–90.