title: A panel of four miRNAs accurately differentiates malignant from benign indeterminate thyroid lesions on fine needle aspiration.
abstract: OBJECTIVE
  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.
  
  
  METHODS
  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.
  
  
  RESULTS
  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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Division of Endocrine Surgery, Department of Surgery, Department of Pathology, Institute for Computational Biomedicine, NY 10021, USA.
authors.forename: Xavier M
authors.initials: XM
authors.lastname: Keutgen
authors.forename: Filippo
authors.initials: F
authors.lastname: Filicori
authors.forename: Michael J
authors.initials: MJ
authors.lastname: Crowley
authors.forename: Yongchun
authors.initials: Y
authors.lastname: Wang
authors.forename: Theresa
authors.initials: T
authors.lastname: Scognamiglio
authors.forename: Rana
authors.initials: R
authors.lastname: Hoda
authors.forename: Daniel
authors.initials: D
authors.lastname: Buitrago
authors.forename: David
authors.initials: D
authors.lastname: Cooper
authors.forename: Martha A
authors.initials: MA
authors.lastname: Zeiger
authors.forename: Rasa
authors.initials: R
authors.lastname: Zarnegar
authors.forename: Olivier
authors.initials: O
authors.lastname: Elemento
authors.forename: Thomas J
authors.initials: TJ
authors.lastname: Fahey
mesh_terms: D000328:Adult; D000368:Aged; D014408:Biomarkers, Tumor / Q000235:genetics; D044963:Biopsy, Fine-Needle; D003937:Diagnosis, Differential; D005260:Female; D006801:Humans; D016015:Logistic Models; D008297:Male; D035683:MicroRNAs / Q000235:genetics*; D008875:Middle Aged; D011446:Prospective Studies; D020133:Reverse Transcriptase Polymerase Chain Reaction; D012680:Sensitivity and Specificity; D060388:Support Vector Machine; D013961:Thyroid Gland / Q000378:metabolism* / Q000473:pathology / Q000601:surgery; D013964:Thyroid Neoplasms / Q000175:diagnosis / Q000235:genetics / Q000473:pathology; D016606:Thyroid Nodule / Q000175:diagnosis / Q000235:genetics* / Q000473:pathology; D013965:Thyroidectomy
chemical_list: D014408:Biomarkers, Tumor; D035683:MicroRNAs; C546085:MIRN197 microRNA, human; C524940:MIRN21 microRNA, human; C525710:MIR222, human; C548170:MIRN328 microRNA, human