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MiRNA Expression May Account for Chronic but Not for Acute Regulation of mRNA Expression in Human Thyroid Tumor Models

Author

Listed:
  • Sébastien L Floor
  • Aline Hebrant
  • Jaime M Pita
  • Manuel Saiselet
  • Christophe Trésallet
  • Frederick Libert
  • Guy Andry
  • Jacques E Dumont
  • Wilma C van Staveren
  • Carine Maenhaut

Abstract

Background: For thyroid tumorigenesis, two main human in vitro models are available: primary cultures of human thyrocytes treated with TSH or EGF/serum as models for autonomous adenomas (AA) or papillary thyroid carcinomas (PTC) respectively, and human thyroid tumor derived cell lines. Previous works of our group have assessed properties of those models, with a special emphasis on mRNA regulations. It is often assumed that miRNA may be one of the primary events inducing these mRNA regulations. Methods: The purpose of this study was to investigate the representativity of those models to study microRNA regulations and their relation with mRNA expression. To achieve this aim, the miRNA expressions profiles of primary cultures treated with TSH or EGF/serum and of 6 thyroid cancer cell lines were compared to the expression profiles of 35 tumor tissues obtained by microarrays. Results: Our data on primary cultures have shown that the TSH or EGF/serum treatment did not greatly modify the microRNA expression profiles, which is contrary to what is observed for mRNA expression profiles, although they still evolved differently according to the treatment. The analysis of miRNA and mRNA expressions profiles in the cell lines has shown that they have evolved into a common, dedifferentiated phenotype, closer to ATC than to the tumors they are derived from. Conclusions: Long-terms TSH or EGF/serum treatments do not mimic AA or PTC respectively in terms of miRNA expression as they do for mRNA, suggesting that the regulations of mRNA expression induced by these physiological agents occur independently of miRNA. The general patterns of miRNA expression in the cell lines suggest that they represent a useful model for undifferentiated thyroid cancer. Mirna probably do not mediate the rapid changes in gene expression in rapid cell biology regulation.

Suggested Citation

  • Sébastien L Floor & Aline Hebrant & Jaime M Pita & Manuel Saiselet & Christophe Trésallet & Frederick Libert & Guy Andry & Jacques E Dumont & Wilma C van Staveren & Carine Maenhaut, 2014. "MiRNA Expression May Account for Chronic but Not for Acute Regulation of mRNA Expression in Human Thyroid Tumor Models," PLOS ONE, Public Library of Science, vol. 9(11), pages 1-9, November.
    • Handle: RePEc:plo:pone00:0111581
    DOI: 10.1371/journal.pone.0111581
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    1. Amaia Lujambio & Scott W. Lowe, 2012. "The microcosmos of cancer," Nature, Nature, vol. 482(7385), pages 347-355, February.
    2. Smyth Gordon K, 2004. "Linear Models and Empirical Bayes Methods for Assessing Differential Expression in Microarray Experiments," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 3(1), pages 1-28, February.
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