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Identification of competing endogenous RNAs of the tumor suppressor gene PTEN: A probabilistic approach

Regulation by microRNAs (miRNAs) and modulation of miRNA activity are critical components of diverse cellular processes. Recent research has shown that miRNA-based regulation of the tumor suppressor gene PTEN can be modulated by the expression of other miRNA targets acting as competing endogenous RN...

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Autores principales: Zarringhalam, Kourosh, Tay, Yvonne, Kulkarni, Prajna, Bester, Assaf C., Pandolfi, Pier Paolo, Kulkarni, Rahul V.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5552881/
https://www.ncbi.nlm.nih.gov/pubmed/28798471
http://dx.doi.org/10.1038/s41598-017-08209-1
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author Zarringhalam, Kourosh
Tay, Yvonne
Kulkarni, Prajna
Bester, Assaf C.
Pandolfi, Pier Paolo
Kulkarni, Rahul V.
author_facet Zarringhalam, Kourosh
Tay, Yvonne
Kulkarni, Prajna
Bester, Assaf C.
Pandolfi, Pier Paolo
Kulkarni, Rahul V.
author_sort Zarringhalam, Kourosh
collection PubMed
description Regulation by microRNAs (miRNAs) and modulation of miRNA activity are critical components of diverse cellular processes. Recent research has shown that miRNA-based regulation of the tumor suppressor gene PTEN can be modulated by the expression of other miRNA targets acting as competing endogenous RNAs (ceRNAs). However, the key sequence-based features enabling a transcript to act as an effective ceRNA are not well understood and a quantitative model associating statistical significance to such features is currently lacking. To identify and assess features characterizing target recognition by PTEN-regulating miRNAs, we analyze multiple datasets from PAR-CLIP experiments in conjunction with RNA-Seq data. We consider a set of miRNAs known to regulate PTEN and identify high-confidence binding sites for these miRNAs on the 3′ UTR of protein coding genes. Based on the number and spatial distribution of these binding sites, we calculate a set of probabilistic features that are used to make predictions for novel ceRNAs of PTEN. Using a series of experiments in human prostate cancer cell lines, we validate the highest ranking prediction (TNRC6B) as a ceRNA of PTEN. The approach developed can be applied to map ceRNA networks of critical cellular regulators and to develop novel insights into crosstalk between different pathways involved in cancer.
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spelling pubmed-55528812017-08-15 Identification of competing endogenous RNAs of the tumor suppressor gene PTEN: A probabilistic approach Zarringhalam, Kourosh Tay, Yvonne Kulkarni, Prajna Bester, Assaf C. Pandolfi, Pier Paolo Kulkarni, Rahul V. Sci Rep Article Regulation by microRNAs (miRNAs) and modulation of miRNA activity are critical components of diverse cellular processes. Recent research has shown that miRNA-based regulation of the tumor suppressor gene PTEN can be modulated by the expression of other miRNA targets acting as competing endogenous RNAs (ceRNAs). However, the key sequence-based features enabling a transcript to act as an effective ceRNA are not well understood and a quantitative model associating statistical significance to such features is currently lacking. To identify and assess features characterizing target recognition by PTEN-regulating miRNAs, we analyze multiple datasets from PAR-CLIP experiments in conjunction with RNA-Seq data. We consider a set of miRNAs known to regulate PTEN and identify high-confidence binding sites for these miRNAs on the 3′ UTR of protein coding genes. Based on the number and spatial distribution of these binding sites, we calculate a set of probabilistic features that are used to make predictions for novel ceRNAs of PTEN. Using a series of experiments in human prostate cancer cell lines, we validate the highest ranking prediction (TNRC6B) as a ceRNA of PTEN. The approach developed can be applied to map ceRNA networks of critical cellular regulators and to develop novel insights into crosstalk between different pathways involved in cancer. Nature Publishing Group UK 2017-08-10 /pmc/articles/PMC5552881/ /pubmed/28798471 http://dx.doi.org/10.1038/s41598-017-08209-1 Text en © The Author(s) 2017 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Zarringhalam, Kourosh
Tay, Yvonne
Kulkarni, Prajna
Bester, Assaf C.
Pandolfi, Pier Paolo
Kulkarni, Rahul V.
Identification of competing endogenous RNAs of the tumor suppressor gene PTEN: A probabilistic approach
title Identification of competing endogenous RNAs of the tumor suppressor gene PTEN: A probabilistic approach
title_full Identification of competing endogenous RNAs of the tumor suppressor gene PTEN: A probabilistic approach
title_fullStr Identification of competing endogenous RNAs of the tumor suppressor gene PTEN: A probabilistic approach
title_full_unstemmed Identification of competing endogenous RNAs of the tumor suppressor gene PTEN: A probabilistic approach
title_short Identification of competing endogenous RNAs of the tumor suppressor gene PTEN: A probabilistic approach
title_sort identification of competing endogenous rnas of the tumor suppressor gene pten: a probabilistic approach
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5552881/
https://www.ncbi.nlm.nih.gov/pubmed/28798471
http://dx.doi.org/10.1038/s41598-017-08209-1
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