A Panel of Four miRNAs Accurately Differentiates Malignant from Benign Indeterminate Thyroid Lesions on Fine Needle Aspiration

Abstract

Indeterminate thyroid lesions on fine needle aspiration (FNA) harbor malignancy in about 25% of cases. Hemi- or total thyroidectomy has, therefore, been routinely advocated for definitive diagnosis. In this study, we analyzed miRNA expression in indeterminate FNA samples and determined its prognostic effects on final pathologic diagnosis. A predictive model was derived using 29 ex vivo indeterminate thyroid lesions on FNA to differentiate malignant from benign tumors at a tertiary referral center and validated on an independent set of 72 prospectively collected in vivo FNA samples. Expression levels of miR-222, miR-328, miR-197, miR-21, miR-181a, and miR-146b were determined using reverse transcriptase PCR. A statistical model was developed using the support vector machine (SVM) approach. A SVM model with four miRNAs (miR-222, miR-328, miR-197, and miR-21) was initially estimated to have 86% predictive accuracy using cross-validation. When applied to the 72 independent in vivo validation samples, performance was actually better than predicted with a sensitivity of 100% and specificity of 86%, for a predictive accuracy of 90% in differentiating malignant from benign indeterminate lesions. When Hurthle cell lesions were excluded, overall accuracy improved to 97% with 100% sensitivity and 95% specificity. This study shows that that the expression of miR-222, miR-328, miR-197, and miR-21 combined in a predictive model is accurate at differentiating malignant from benign indeterminate thyroid lesions on FNA. These findings suggest that FNA miRNA analysis could be a useful adjunct in the management algorithm of patients with thyroid nodules.