Department of Pathology, Arthur G. James Comprehensive Cancer Center, The Ohio State University, Columbus, Ohio 43210, USA.
Thyroid. 2012 Jan;22(1):9-16. doi: 10.1089/thy.2011.0081. Epub 2011 Dec 2.
MicroRNA (miR) expression signatures are proposed to be able to differentiate thyroid cancer from benign thyroid lesions. We selected eight miRs (miR-146b, -221, -187, -197, -346, -30d, -138, and -302c) to examine the potential use of miRs to supplement diagnostic cytology in cases designated as "atypia of undetermined significance."
miR expression was measured in thyroid fine needle aspiration (FNA) specimens by quantitative polymerase chain reaction. Gene expression analyses and linear discriminant analysis (LDA) were performed in a training sample set (n=60) to obtain a classification rule to predict FNA cases as benign or malignant. The predictions were cross-validated by comparing with the corresponding histological diagnoses. A validation sample set (n=68) was further tested with the established four-miR LDA classification rule.
A set of four miRs (miR-146b, -221, -187, and -30d) was identified that could differentiate malignant from benign lesions. A four-miR LDA classification rule was obtained and used to predict FNA cases as benign or malignant. For the training sample set, we obtained a diagnostic accuracy of 93.3%, sensitivity of 93.2%, specificity of 93.8%, positive predictive value (PPV) of 0.98, and negative predictive value (NPV) of 0.83. For the validation sample set, we obtained a diagnostic accuracy of 85.3%, sensitivity of 88.9%, specificity of 78.3%, PPV of 0.89, and NPV of 0.78. For the 30 atypia cases in the validation sample set, we obtained a diagnostic accuracy of 73.3%, sensitivity of 63.6%, specificity of 78.9%, PPV of 0.64, and NPV of 0.79. Based on the miR predictions, we classified the atypia cases predicted as "malignant" into "high risk" and those predicted as "benign" into "low risk" categories. While thyroid carcinomas, particularly papillary thyroid carcinomas (PTCs), were relatively enriched in the high-risk category, this particular miR panel is subject to inaccurate results in follicular neoplasias in atypia cases.
We demonstrate that miR amplification from FNA samples is feasible and that the particular four miR profile in this study can identify PTCs. However, further refinement is required for application to FNA cytology of "atypia of undetermined significance" cases due to low accuracy in classifying follicular neoplasias.
微小 RNA(miR)表达谱被认为能够区分甲状腺癌与良性甲状腺病变。我们选择了 8 个 miR(miR-146b、-221、-187、-197、-346、-30d、-138 和-302c)来研究 miR 在被指定为“意义未明的非典型性”的病例中补充诊断细胞学的潜在用途。
通过定量聚合酶链反应测量甲状腺细针抽吸(FNA)标本中的 miR 表达。在训练样本集中进行基因表达分析和线性判别分析(LDA),以获得预测 FNA 病例为良性或恶性的分类规则。通过与相应的组织学诊断进行比较,对预测结果进行交叉验证。使用建立的四 miR LDA 分类规则进一步测试验证样本集(n=68)。
确定了一组四个 miR(miR-146b、-221、-187 和-30d),可区分恶性和良性病变。获得了四 miR LDA 分类规则,并用于预测 FNA 病例的良性或恶性。对于训练样本集,我们获得了 93.3%的诊断准确性、93.2%的敏感性、93.8%的特异性、0.98 的阳性预测值(PPV)和 0.83 的阴性预测值(NPV)。对于验证样本集,我们获得了 85.3%的诊断准确性、88.9%的敏感性、78.3%的特异性、0.89 的 PPV 和 0.78 的 NPV。对于验证样本集中的 30 个非典型病例,我们获得了 73.3%的诊断准确性、63.6%的敏感性、78.9%的特异性、0.64 的 PPV 和 0.79 的 NPV。基于 miR 预测,我们将预测为“恶性”的非典型病例分为“高危”类别,将预测为“良性”的病例分为“低危”类别。虽然甲状腺癌,特别是甲状腺乳头状癌(PTC),在高危类别中相对富集,但该特定 miR 组在非典型病例中的滤泡性肿瘤中存在不准确的结果。
我们证明从 FNA 样本中扩增 miR 是可行的,并且本研究中的特定四 miR 谱可以识别 PTC。然而,由于在分类滤泡性肿瘤方面准确性较低,因此需要进一步改进,以应用于“意义未明的非典型性”病例的 FNA 细胞学。