Division of Endocrine Surgery, Department of Surgery, Department of Pathology, Institute for Computational Biomedicine, NY 10021, USA.
Clin Cancer Res. 2012 Apr 1;18(7):2032-8. doi: 10.1158/1078-0432.CCR-11-2487. Epub 2012 Feb 20.
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.
细针穿刺（FNA）的不确定甲状腺病变恶性肿瘤约占 25%。因此，半甲状腺切除术或全甲状腺切除术已被常规推荐用于明确诊断。在这项研究中，我们分析了不确定的 FNA 样本中的 miRNA 表达，并确定其对最终病理诊断的预后影响。
使用 29 个在三级转诊中心的 FNA 中的不确定甲状腺病变来建立一个预测模型，以区分恶性和良性肿瘤，并在 72 个前瞻性收集的体内 FNA 样本的独立组中进行验证。使用逆转录 PCR 测定 miR-222、miR-328、miR-197、miR-21、miR-181a 和 miR-146b 的表达水平。使用支持向量机（SVM）方法建立统计模型。
使用 SVM 模型和四个 miRNA（miR-222、miR-328、miR-197 和 miR-21），最初估计在交叉验证中有 86%的预测准确性。当应用于 72 个独立的体内验证样本时，性能实际上优于预测，恶性与良性不确定病变的敏感性为 100%，特异性为 86%，预测准确率为 90%。当排除 Hurthle 细胞病变时，整体准确性提高到 97%，敏感性为 100%，特异性为 95%。
这项研究表明，miR-222、miR-328、miR-197 和 miR-21 的表达结合在预测模型中，可准确区分 FNA 的恶性与良性不确定甲状腺病变。这些发现表明，FNA miRNA 分析可能是甲状腺结节患者管理算法的有用辅助手段。
