, Volume 54, Issue 2, pp 440–447 | Cite as

Two-miRNA classifiers differentiate mutation-negative follicular thyroid carcinomas and follicular thyroid adenomas in fine needle aspirations with high specificity

  • Tomasz Stokowy
  • Bartosz Wojtas
  • Barbara Jarzab
  • Knut Krohn
  • David Fredman
  • Henning Dralle
  • Thomas Musholt
  • Steffen Hauptmann
  • Dariusz Lange
  • László Hegedüs
  • Ralf Paschke
  • Markus Eszlinger
Original Article


Diagnosis of thyroid by fine needle aspiration is challenging for the “indeterminate” category and can be supported by molecular testing. We set out to identify miRNA markers that could be used in a diagnostic setting to improve the discrimination of mutation-negative indeterminate fine needle aspirations. miRNA high-throughput sequencing was performed for freshly frozen tissue samples of 19 RAS and PAX8/PPARG mutation-negative follicular thyroid carcinomas, and 23 RAS and PAX8/PPARG mutation-negative follicular adenomas. Differentially expressed miRNAs were validated by quantitative polymerase chain reaction in a set of 44 fine needle aspiration samples representing 24 follicular thyroid carcinomas and 20 follicular adenomas. Twenty-six miRNAs characterized by a significant differential expression between follicular thyroid carcinomas and follicular adenomas were identified. Nevertheless, since no single miRNA had satisfactory predictive power, classifiers comprising two differentially expressed miRNAs were designed with the aim to improve the classification. Six two-miRNA classifiers were established and quantitative polymerase chain reaction validated in fine needle aspiration samples. Four out of six classifiers were characterized by a high specificity (≥94 %). The best two-miRNA classifier (miR-484/miR-148b-3p) identified thyroid malignancy with a sensitivity of 89 % and a specificity of 87 %. The high-throughput sequencing allowed the identification of subtle differences in the miRNA expression profiles of follicular thyroid carcinomas and follicular adenomas. While none of the differentially expressed miRNAs could be used as a stand-alone malignancy marker, the validation results for two-miRNA classifiers in an independent set of fine needle aspirations are very promising. The ultimate evaluation of these classifiers for their capability of discriminating mutation-negative indeterminate fine needle aspirations will require the evaluation of a sufficiently large number of fine needle aspirations with histological confirmation.


Follicular thyroid cancer Follicular thyroid adenoma High-throughput sequencing Classifier miRNA Fine needle aspiration cytology 



The authors thank Eileen Bösenberg and Anja Moll for their excellent technical assistance. Dario Veneziano is thanked for suggestions related to bioinformatics analyses. This research was supported by the Foundation for Polish Science MPD program “Molecular Genomics, Transcriptomics and Bioinformatics in Cancer” carried by the School of Molecular Medicine at the Medical University of Warsaw (Tomasz Stokowy and Bartosz Wojtaś). Tomasz Stokowy is supported by the Bergen Medical Research Foundation (BMFS 807964). Laszlo Hegedüs is supported by an unrestricted grant from the Novo Nordisk Foundation. Ralf Paschke is supported by a DFG (PA423/14-1,2), a Deutsche Krebshilfe (109670), and a Wilhelm Sander Foundation (2013.010.1) grant. Markus Eszlinger is supported by a DFG (ES162/4-1), a Deutsche Krebshilfe (109994), and a Wilhelm Sander Foundation (2013.010.1) grant.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Supplementary material

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Supplementary Information
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Supplementary Information


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Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Tomasz Stokowy
    • 1
    • 2
  • Bartosz Wojtas
    • 2
    • 3
  • Barbara Jarzab
    • 2
  • Knut Krohn
    • 4
  • David Fredman
    • 5
  • Henning Dralle
    • 6
  • Thomas Musholt
    • 7
  • Steffen Hauptmann
    • 8
  • Dariusz Lange
    • 9
  • László Hegedüs
    • 10
  • Ralf Paschke
    • 11
  • Markus Eszlinger
    • 12
    • 13
  1. 1.Department of Clinical ScienceUniversity of BergenBergenNorway
  2. 2.Department of Nuclear Medicine and Endocrine OncologyM. Sklodowska-Curie Memorial Cancer Center and Institute of Oncology, and Institute of Automatic Control, Silesian University of TechnologyGliwicePoland
  3. 3.Laboratory of Molecular NeurobiologyNencki Institute of Experimental BiologyWarsawPoland
  4. 4.IZKF LeipzigUniversity of LeipzigLeipzigGermany
  5. 5.Computational Biology Unit, Department of InformaticsUniversity of BergenBergenNorway
  6. 6.Department of General, Visceral and Vascular SurgeryUniversity of Halle-WittenbergHalle (Saale)Germany
  7. 7.Department of General, Visceral, and Transplantation SurgeryUniversity Medical Center of the Johannes Gutenberg UniversityMainzGermany
  8. 8.Department of PathologyMartin Luther University Halle-WittenbergHalle (Saale)Germany
  9. 9.Tumor Pathology DepartmentM. Sklodowska-Curie Memorial Cancer Center and Institute of OncologyGliwicePoland
  10. 10.Department of Endocrinology and MetabolismOdense University HospitalOdenseDenmark
  11. 11.Division of Endocrinology and Metabolism, Departments of Medicine and Oncology and Arnie Charbonneau Cancer Institute, Cummings School of MedicineUniversity of CalgaryCalgaryCanada
  12. 12.Department of Oncology and Arnie Charbonneau Cancer Institute, Cumming School of MedicineUniversity of CalgaryCalgaryCanada
  13. 13.Divisions of Endocrinology and NephrologyUniversity of LeipzigLeipzigGermany

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