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MiRNAs with the potential to distinguish follicular thyroid carcinomas from benign follicular thyroid tumors: Results of a meta-analysis

  • T. Stokowy
  • , B. Wojtaś
  • , K. Fujarewicz
  • , B. Jarzab
  • , M. Eszlinger
  • , R. Paschke
  • Silesian University of Technology
  • Maria Sklodowska-Curie Institute of Oncology
  • Leipzig University

Research output: Contribution to journalArticlepeer-review

38 Citations (Scopus)

Abstract

The detection of somatic mutations in indeterminate or follicular proliferation fine-needle aspiration cytologies (FNACs) is able to clarify only a subgroup of those FNACs. Therefore, further markers to differentiate this problematic FNAC category by the identification of mutation negative thyroid cancers and benign nodules are urgently needed. Our objective was to evaluate previously published miRNA markers and discover novel ones from all publicly available miRNA expression profiling data sets. By literature review and data repository search we gathered 3 data sets describing human miRNA expression profiles of follicular thyroid cancer (FTC) and follicular adenoma (FA) samples. Literature review summarized 27 previously published miRNAs, which were validated in the 3 available data sets. By means of uniform statistical analysis 6 further miRNAs were identified and tested in an independent, previously published microarray data set. Meta-analysis confirmed 7 out of 27 previously published, and 4 out of 6 de novo identified miRNAs. The low confirmation rate of previously published miRNA markers was induced by low numbers of samples in the analyzed studies and high false discovery rates that were higher than 0.2. Finally, miR-637, miR-181c-3p, miR-206, and miR-7-5p were discovered as de novo potential FTC markers and validated in at least one independent, previously published data set. Two out of these new identified miRNAs (miR-7-5p and miR-206) were validated by qPCR in an independent sample set of 32 FTC and 46 FA samples. Especially miR-7-5p was able to differentiate benign and malignant thyroid tumors in several datasets.

Original languageEnglish
Pages (from-to)171-180
Number of pages10
JournalHormone and Metabolic Research
Volume46
Issue number3
DOIs
Publication statusPublished - Mar 2014

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

Keywords

  • follicular thyroid tumors
  • integrated statistical analysis
  • miRNA
  • microarray

ASJC Scopus subject areas

  • Endocrinology, Diabetes and Metabolism
  • Biochemistry
  • Endocrinology
  • Clinical Biochemistry
  • Biochemistry (medical)

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