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The combination of four molecular markers improves thyroid cancer cytologic diagnosis and patient management
BACKGROUND: Papillary thyroid cancer is the most common endocrine malignancy. The most sensitive and specific diagnostic tool for thyroid nodule diagnosis is fine-needle aspiration (FNA) biopsy with cytological evaluation. Nevertheless, FNA biopsy is not always decisive leading to “indeterminate” or...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4652365/ https://www.ncbi.nlm.nih.gov/pubmed/26581891 http://dx.doi.org/10.1186/s12885-015-1917-2 |
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author | Panebianco, Federica Mazzanti, Chiara Tomei, Sara Aretini, Paolo Franceschi, Sara Lessi, Francesca Di Coscio, Giancarlo Bevilacqua, Generoso Marchetti, Ivo |
author_facet | Panebianco, Federica Mazzanti, Chiara Tomei, Sara Aretini, Paolo Franceschi, Sara Lessi, Francesca Di Coscio, Giancarlo Bevilacqua, Generoso Marchetti, Ivo |
author_sort | Panebianco, Federica |
collection | PubMed |
description | BACKGROUND: Papillary thyroid cancer is the most common endocrine malignancy. The most sensitive and specific diagnostic tool for thyroid nodule diagnosis is fine-needle aspiration (FNA) biopsy with cytological evaluation. Nevertheless, FNA biopsy is not always decisive leading to “indeterminate” or “suspicious” diagnoses in 10 %–30 % of cases. BRAF V600E detection is currently used as molecular test to improve the diagnosis of thyroid nodules, yet it lacks sensitivity. The aim of the present study was to identify novel molecular markers/computational models to improve the discrimination between benign and malignant thyroid lesions. METHODS: We collected 118 pre-operative thyroid FNA samples. All 118 FNA samples were characterized for the presence of the BRAF V600E mutation (exon15) by pyrosequencing and further assessed for mRNA expression of four genes (KIT, TC1, miR-222, miR-146b) by quantitative polymerase chain reaction. Computational models (Bayesian Neural Network Classifier, discriminant analysis) were built, and their ability to discriminate benign and malignant tumors were tested. Receiver operating characteristic (ROC) analysis was performed and principal component analysis was used for visualization purposes. RESULTS: In total, 36/70 malignant samples carried the V600E mutation, while all 48 benign samples were wild type for BRAF exon15. The Bayesian neural network (BNN) and discriminant analysis, including the mRNA expression of the four genes (KIT, TC1, miR-222, miR-146b) showed a very strong predictive value (94.12 % and 92.16 %, respectively) in discriminating malignant from benign patients. The discriminant analysis showed a correct classification of 100 % of the samples in the malignant group, and 95 % by BNN. KIT and miR-146b showed the highest diagnostic accuracy of the ROC curve, with area under the curve values of 0.973 for KIT and 0.931 for miR-146b. CONCLUSIONS: The four genes model proposed in this study proved to be highly discriminative of the malignant status compared with BRAF assessment alone. Its implementation in clinical practice can help in identifying malignant/benign nodules that would otherwise remain suspicious. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12885-015-1917-2) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-4652365 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-46523652015-11-20 The combination of four molecular markers improves thyroid cancer cytologic diagnosis and patient management Panebianco, Federica Mazzanti, Chiara Tomei, Sara Aretini, Paolo Franceschi, Sara Lessi, Francesca Di Coscio, Giancarlo Bevilacqua, Generoso Marchetti, Ivo BMC Cancer Research Article BACKGROUND: Papillary thyroid cancer is the most common endocrine malignancy. The most sensitive and specific diagnostic tool for thyroid nodule diagnosis is fine-needle aspiration (FNA) biopsy with cytological evaluation. Nevertheless, FNA biopsy is not always decisive leading to “indeterminate” or “suspicious” diagnoses in 10 %–30 % of cases. BRAF V600E detection is currently used as molecular test to improve the diagnosis of thyroid nodules, yet it lacks sensitivity. The aim of the present study was to identify novel molecular markers/computational models to improve the discrimination between benign and malignant thyroid lesions. METHODS: We collected 118 pre-operative thyroid FNA samples. All 118 FNA samples were characterized for the presence of the BRAF V600E mutation (exon15) by pyrosequencing and further assessed for mRNA expression of four genes (KIT, TC1, miR-222, miR-146b) by quantitative polymerase chain reaction. Computational models (Bayesian Neural Network Classifier, discriminant analysis) were built, and their ability to discriminate benign and malignant tumors were tested. Receiver operating characteristic (ROC) analysis was performed and principal component analysis was used for visualization purposes. RESULTS: In total, 36/70 malignant samples carried the V600E mutation, while all 48 benign samples were wild type for BRAF exon15. The Bayesian neural network (BNN) and discriminant analysis, including the mRNA expression of the four genes (KIT, TC1, miR-222, miR-146b) showed a very strong predictive value (94.12 % and 92.16 %, respectively) in discriminating malignant from benign patients. The discriminant analysis showed a correct classification of 100 % of the samples in the malignant group, and 95 % by BNN. KIT and miR-146b showed the highest diagnostic accuracy of the ROC curve, with area under the curve values of 0.973 for KIT and 0.931 for miR-146b. CONCLUSIONS: The four genes model proposed in this study proved to be highly discriminative of the malignant status compared with BRAF assessment alone. Its implementation in clinical practice can help in identifying malignant/benign nodules that would otherwise remain suspicious. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12885-015-1917-2) contains supplementary material, which is available to authorized users. BioMed Central 2015-11-19 /pmc/articles/PMC4652365/ /pubmed/26581891 http://dx.doi.org/10.1186/s12885-015-1917-2 Text en © Panebianco et al. 2015 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Article Panebianco, Federica Mazzanti, Chiara Tomei, Sara Aretini, Paolo Franceschi, Sara Lessi, Francesca Di Coscio, Giancarlo Bevilacqua, Generoso Marchetti, Ivo The combination of four molecular markers improves thyroid cancer cytologic diagnosis and patient management |
title | The combination of four molecular markers improves thyroid cancer cytologic diagnosis and patient management |
title_full | The combination of four molecular markers improves thyroid cancer cytologic diagnosis and patient management |
title_fullStr | The combination of four molecular markers improves thyroid cancer cytologic diagnosis and patient management |
title_full_unstemmed | The combination of four molecular markers improves thyroid cancer cytologic diagnosis and patient management |
title_short | The combination of four molecular markers improves thyroid cancer cytologic diagnosis and patient management |
title_sort | combination of four molecular markers improves thyroid cancer cytologic diagnosis and patient management |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4652365/ https://www.ncbi.nlm.nih.gov/pubmed/26581891 http://dx.doi.org/10.1186/s12885-015-1917-2 |
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