Targeted Sequencing of DNA/RNA Combined with Radiomics Predicts Lymph Node Metastasis of Papillary Thyroid Carcinoma
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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 (ATM) was frequently accompanied by BRAF, TERT or gene fusions. ATM alone or ATM co-mutations were significantly positively correlated with lymph node metastasis. Accordingly, ATM knock-down PTC cells bearing BRAF, KRAS 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.
Association of radiomic features with genomic signatures in thyroid cancer: a systematic review.
Luciano N, Orlandella F, Braile M, Cavaliere C, Aiello M, Franzese M J Transl Med. 2024; 22(1):1088.
PMID: 39616372 PMC: 11608493. DOI: 10.1186/s12967-024-05896-z.