Abstract
BackgroundCurrently the cytological examination of fine needle aspiration (FNA) biopsies is the standard technique for the pre-operative differential diagnosis of thyroid nodules. However, the results may be non-informative in ~20% of cases due to an inadequate sampling and the lack of highly specific, measurable cytological criteria, therefore ancillary biomarkers that could aid in these cases are clearly needed. The aim of our study was to evaluate the mRNA expression levels of 8 candidate marker genes as the diagnostic biomarkers for the discrimination of benign and malignant thyroid nodules and to find a combination of biomarkers with the highest diagnostic value.Materials and methodsmRNA expression levels of eight candidate marker genes - BIRC5, CCND1, CDH1, CITED1, DPP4, LGALS3, MET and TFF3 was measured by real-time RT-PCR in paired nodular and surrounding normal thyroid tissue specimens of 105 consecutive patients undergoing thyroid surgery and compared between different types of thyroid lesions.ResultsSignificant differences in the mRNA expression levels between the normal and malignant thyroid tissues and between benign and malignant nodules were found for BIRC5, CCND1, CITED1, DPP4, LGALS3, MET and TFF3, but not CDH1. On a single gene basis, relative quantity (RQ) of LGALS3 had the highest diagnostic value for the discrimination of malignant and benign thyroid nodules (AUC = 0.832, P < 0.0001 and 90.9% sensitivity and 65.6% specificity at the optimal cut-off on ROC curve). The only two-marker set that outperformed LGALS3 was RQ sum of LGALS3 and BIRC5 (AUC = 0.841, P < 0.0001). An application of multivariate logistic regression analysis resulted in the generation of a multiplex biomarker model based on LGALS3, BIRC5, TFF3, CCND1, MET and CITED1 that had considerably higher specificity than a single marker or two marker gene-based models (AUC = 0.895, P < 0.0001, 70.5% sensitivity and 93.4% specificity).ConclusionsThis study confirmed that mRNA expression levels of 7 out of 8 candidate genes analysed have a diagnostic value for the distinction of benign and malignant thyroid nodules. The multiplex biomarker model based on 6 genes outperformed a single marker or two marker-based models and warrants feasibility studies on FNA biopsies and the validation in a larger cohort of patients.
Highlights
The cytological examination of fine needle aspiration (FNA) biopsies is the standard technique for the pre-operative differential diagnosis of thyroid nodules
An application of multivariate logistic regression analysis resulted in the generation of a multiplex biomarker model based on LGALS3, BIRC5, TFF3, CCND1, MET and CITED1 that had considerably higher specificity than a single marker or two marker gene-based models (AUC = 0.895, P < 0.0001, 70.5% sensitivity and 93.4% specificity)
This study confirmed that mRNA expression levels of 7 out of 8 candidate genes analysed have a diagnostic value for the distinction of benign and malignant thyroid nodules
Summary
The cytological examination of fine needle aspiration (FNA) biopsies is the standard technique for the pre-operative differential diagnosis of thyroid nodules. The results may be noninformative in ~20% of cases due to an inadequate sampling and the lack of highly specific, measurable cytological criteria, ancillary biomarkers that could aid in these cases are clearly needed. Only less than 5% of the palpable nodules are malignant lesions, thyroid cancers are the most common undifferentiated or anaplastic carcinoma that is a highly aggressive and lethal cancer with 5-year survival rate below 1-17% [4]. The differential diagnosis of nodular thyroid disease is based on cytological examination of fine needle aspiration (FNA) biopsies, the results may be noninformative in ~20% of cases due to an inadequate sampling and the lack of highly specific, measurable cytological criteria [4,5]. Molecular biomarkers of malignancy that could reliably discriminate between malignant and benign nodules in the grey zone of thyroid FNA and classify tumours into the histological subtypes are clearly needed
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