Dataset Information

Familiar Papillary Thyroid Carcinoma in a Large Brazilian Family Is Not Associated with Succinate Dehydrogenase Defects.

ABSTRACT:

Background

Thyroid cancer is the most common endocrine gland malignancy. Advances in understanding the genetic basis for thyroid cancer revealed the potential involvement of several genes in the formation of thyroid tumors. Mutations in the gene coding for succinate dehydrogenase subtype B (SDHB) have been implicated in papillary thyroid cancer (PTC). Succinate dehydrogenase (SDH) is a heterotetrameric protein composed of four subunits, SDHA, SDHB, SDHC, and SDHD, and participates in both the electron transport chain and the tricarboxylic acid cycle. The aim of the study was to evaluate the association between variants in the SDHA, SDHB, SDHC, and SDHD genes and familiar PTC in a large Brazilian family.

Method

Four patients with PTC, 1 patient with PTC and gastrointestinal stromal tumor (GIST), 1 patient with GIST, and their relatives - several of them with different thyroid problems - from a large Brazilian family were screened for genetic variations of SDHx genes with the use of polymerase chain reaction-single-stranded conformational polymorphism and direct sequencing.

Results

Only one rare variation in SDHA was found in some of the family members, but not segregating with the disease. No other genetic variants of these genes were detected in the family members that presented with PTC and/or GIST.

Conclusion

Familiar PTC and a GIST were not associated with SDHx mutations; additional genetic defects, yet unknown, may be responsible for the development of tumor.

SUBMITTER: Accordi ED

PROVIDER: S-EPMC4949364 | biostudies-literature | 2016 Jul

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Familiar Papillary Thyroid Carcinoma in a Large Brazilian Family Is Not Associated with Succinate Dehydrogenase Defects.

Accordi Elen Dias ED Xekouki Paraskevi P Azevedo Bruna B de Alexandre Rodrigo Bertollo RB Frasson Carla C Gantzel Siliane Marie SM Papadakis Georgios Z GZ Angelousi Anna A Stratakis Constantine A CA Sotomaior Vanessa Santos VS Faucz Fabio R FR

European thyroid journal 20160310 2

<h4>Background</h4>Thyroid cancer is the most common endocrine gland malignancy. Advances in understanding the genetic basis for thyroid cancer revealed the potential involvement of several genes in the formation of thyroid tumors. Mutations in the gene coding for succinate dehydrogenase subtype B (SDHB) have been implicated in papillary thyroid cancer (PTC). Succinate dehydrogenase (SDH) is a heterotetrameric protein composed of four subunits, SDHA, SDHB, SDHC, and SDHD, and participates in bot ...[more]

PMID: 27493882

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Project description:BackgroundThyroid cancer is prone to early lymph node metastasis (LNM), and patients with large volume LNM (LVLNM) tend to have a poorer prognosis. The aim of this study was to predict LVLNM in before surgery based on radiomics and deep learning (DL).MethodsA multicenter retrospective study was performed, including 854 papillary thyroid carcinoma (PTC) patients from three centers. Radiomics features were extracted. Logistic regression (LR), support vector machine (SVM), K-nearest neighbors (KNN), multi-layer perceptron (MLP), random forest (RF), ExtraTrees, extreme gradient boosting (XGBoost), and light gradient boosting machine (LightGBM) algorithms were used to construct radiomics models. AlexNet, DenseNet121, inception_v3, ResNet50, and transformer algorithms were used to construct DL models. The receiver operating characteristic (ROC) curve was employed to select the better-performing model. A combined model was then created by merging radiomics features and DL features. The least absolute shrinkage and selection operator (LASSO) method was utilized to identify metabolites and radiomics features with non-zero coefficients. The performance of the models was evaluated using area under the curve (AUC), accuracy (ACC), sensitivity (SEN), specificity (SPE), positive predictive value (PPV), negative predictive value (NPV), and F1-score.ResultsA total of 1,357 radiomics features were extracted. Among the radiomics models, the ExtraTrees model demonstrated the optimal diagnostic capabilities with an AUC of 0.787 [95% confidence interval (CI): 0.715-0.858], and DenseNet121 DL model demonstrated the optimal diagnostic capabilities with an AUC of 0.766 (95% CI: 0.683-0.848). Furthermore, the combined model, named the Thy-DL-Radiomics model, exhibited an AUC of 0.839 (95% CI: 0.758-0.920) in the internal validation set and 0.789 (95% CI: 0.718-0.859) in the external validation set.ConclusionsA radiomics-DL features integrated model can predict LVLNM in PTC patients and provide guidance for personalized treatment.

Dataset Information

Familiar Papillary Thyroid Carcinoma in a Large Brazilian Family Is Not Associated with Succinate Dehydrogenase Defects.

Background

Method

Results

Conclusion

Publications

Familiar Papillary Thyroid Carcinoma in a Large Brazilian Family Is Not Associated with Succinate Dehydrogenase Defects.

Similar Datasets

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