4.6 Article

MicroRNA deep-sequencing reveals master regulators of follicular and papillary thyroid tumors

Journal

MODERN PATHOLOGY
Volume 28, Issue 6, Pages 748-757

Publisher

NATURE PUBLISHING GROUP
DOI: 10.1038/modpathol.2015.44

Keywords

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Funding

  1. Fondo de Investigaciones Sanitarias [PI11/01359, PI14/00240, PI11/01354, RD12/0036/0030, RD12/0036/0013]
  2. Fundacion Mutua Madrilena [AP2775/2008]
  3. Comunidad de Madrid [S2011/BMD-2328 TIRONET]
  4. Spanish Ministry of Economy and Competitiveness [SAF2011/23638, SAF2013/44709R]
  5. European Foundation for the Study of Diabetes [BI 2014_6_clinical] Funding Source: researchfish

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MicroRNA deregulation could be a crucial event in thyroid carcinogenesis. However, current knowledge is based on studies that have used inherently biased methods. Thus, we aimed to define in an unbiased way a list of deregulated microRNAs in well-differentiated thyroid cancer in order to identify diagnostic and prognostic markers. We performed a microRNA deep-sequencing study using the largest well-differentiated thyroid tumor collection reported to date, comprising 127 molecularly characterized tumors with follicular or papillary patterns of growth and available clinical follow-up data, and 17 normal tissue samples. Furthermore, we integrated microRNA and gene expression data for the same tumors to propose targets for the novel molecules identified. Two main microRNA expression profiles were identified: one common for follicular-pattern tumors, and a second for papillary tumors. Follicular tumors showed a notable overexpression of several members of miR-515 family, and downregulation of the novel microRNA miR-1247. Among papillary tumors, top upregulated microRNAs were miR-146b and the miR-221 similar to 222 cluster, while miR-1179 was downregulated. BRAF-positive samples displayed extreme downregulation of miR-7 and -204. The identification of the predicted targets for the novel molecules gave insights into the proliferative potential of the transformed follicular cell. Finally, by integrating clinical follow-up information with microRNA expression, we propose a prediction model for disease relapse based on expression of two miRNAs (miR-192 and let-7a) and several other clinicopathological features. This comprehensive study complements the existing knowledge about deregulated microRNAs in the development of well-differentiated thyroid cancer and identifies novel markers associated with recurrence-free survival.

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