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

Abstract 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 endo...

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Autores principales:	Kourosh Zarringhalam, Yvonne Tay, Prajna Kulkarni, Assaf C. Bester, Pier Paolo Pandolfi, Rahul V. Kulkarni
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2017
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/9eebffdf6a484073afb802d526c80efc
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spelling	oai:doaj.org-article:9eebffdf6a484073afb802d526c80efc2021-12-02T11:50:57ZIdentification of competing endogenous RNAs of the tumor suppressor gene PTEN: A probabilistic approach10.1038/s41598-017-08209-12045-2322https://doaj.org/article/9eebffdf6a484073afb802d526c80efc2017-08-01T00:00:00Zhttps://doi.org/10.1038/s41598-017-08209-1https://doaj.org/toc/2045-2322Abstract 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.Kourosh ZarringhalamYvonne TayPrajna KulkarniAssaf C. BesterPier Paolo PandolfiRahul V. KulkarniNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 7, Iss 1, Pp 1-12 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Kourosh Zarringhalam Yvonne Tay Prajna Kulkarni Assaf C. Bester Pier Paolo Pandolfi Rahul V. Kulkarni Identification of competing endogenous RNAs of the tumor suppressor gene PTEN: A probabilistic approach
description	Abstract 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.
format	article
author	Kourosh Zarringhalam Yvonne Tay Prajna Kulkarni Assaf C. Bester Pier Paolo Pandolfi Rahul V. Kulkarni
author_facet	Kourosh Zarringhalam Yvonne Tay Prajna Kulkarni Assaf C. Bester Pier Paolo Pandolfi Rahul V. Kulkarni
author_sort	Kourosh Zarringhalam
title	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_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_sort	identification of competing endogenous rnas of the tumor suppressor gene pten: a probabilistic approach
publisher	Nature Portfolio
publishDate	2017
url	https://doaj.org/article/9eebffdf6a484073afb802d526c80efc
work_keys_str_mv	AT kouroshzarringhalam identificationofcompetingendogenousrnasofthetumorsuppressorgeneptenaprobabilisticapproach AT yvonnetay identificationofcompetingendogenousrnasofthetumorsuppressorgeneptenaprobabilisticapproach AT prajnakulkarni identificationofcompetingendogenousrnasofthetumorsuppressorgeneptenaprobabilisticapproach AT assafcbester identificationofcompetingendogenousrnasofthetumorsuppressorgeneptenaprobabilisticapproach AT pierpaolopandolfi identificationofcompetingendogenousrnasofthetumorsuppressorgeneptenaprobabilisticapproach AT rahulvkulkarni identificationofcompetingendogenousrnasofthetumorsuppressorgeneptenaprobabilisticapproach
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Identification of competing endogenous RNAs of the tumor suppressor gene PTEN: A probabilistic approach

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