Evaluation of candidate diagnostic MicroRNAs in thyroid fine-needle aspiration biopsy samples

Background: Thyroid cancer diagnosis in the United States has increased by 2.3-folds in the last three decades. Up to 30% of thyroid fine-needle aspiration biopsy (FNAB) results are inconclusive. Several differentially expressed microRNAs (miRNAs) have been identified as candidate diagnostic markers for thyroid nodules. We hypothesized that these differentially expressed miRNAs may improve the accuracy of FNAB in difficult to diagnose thyroid nodules. Methods: Expression levels of four miRNAs (miR-7, -126, -374a, and let-7g) were analyzed using quantitative real-time reverse transcription-polymerase chain reaction in 95 FNAB samples as the training set. A predictor model was formulated based on the most differentially expressed miRNA (miR-7) ΔCt value and the model was applied on a separate cohort of 59 FNAB samples as the validation set. Results: miR-7 was the best predictor to distinguish benign from malignant thyroid FNAB samples. The other three miRNAs were co-expressed and did not significantly contribute to the predictor model. miR-7 had a sensitivity of 100%, specificity of 29%, positive predictive value (PPV) of 36%, negative predictive value (NPV) of 100%, and overall accuracy of 76% when applied to the validation set. In subgroup analysis of preoperative nondiagnostic, indeterminate, or suspicious FNAB samples, the predictor model had an overall accuracy of 37% with sensitivity of 100%, specificity of 20%, PPV of 25%, and NPV of 100%. Conclusions: miR-7 may be a helpful adjunct marker to thyroid FNAB in tumor types which are inconclusive. Given the high NPV of miR-7, a patient with a benign result based on the predictor model may be followed as opposed to performing an immediate diagnostic thyroidectomy. Future prospective clinical trials evaluating its accuracy in a larger cohort are warranted to determine its clinical utility.