Surprisal analysis of Glioblastoma Multiform (GBM) microRNA dynamics unveils tumor specific phenotype.

Gliomablastoma multiform (GBM) is the most fatal form of all brain cancers in humans. Currently there are limited diagnostic tools for GBM detection. Here, we applied surprisal analysis, a theory grounded in thermodynamics, to unveil how biomolecule energetics, specifically a redistribution of free...

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Autores principales:	Sohila Zadran, Francoise Remacle, Raphael Levine
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2014
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/ce68e07f0e98441c89497ae396b1f263
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spelling	oai:doaj.org-article:ce68e07f0e98441c89497ae396b1f2632021-11-25T05:58:50ZSurprisal analysis of Glioblastoma Multiform (GBM) microRNA dynamics unveils tumor specific phenotype.1932-620310.1371/journal.pone.0108171https://doaj.org/article/ce68e07f0e98441c89497ae396b1f2632014-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0108171https://doaj.org/toc/1932-6203Gliomablastoma multiform (GBM) is the most fatal form of all brain cancers in humans. Currently there are limited diagnostic tools for GBM detection. Here, we applied surprisal analysis, a theory grounded in thermodynamics, to unveil how biomolecule energetics, specifically a redistribution of free energy amongst microRNAs (miRNAs), results in a system deviating from a non-cancer state to the GBM cancer -specific phenotypic state. Utilizing global miRNA microarray expression data of normal and GBM patients tumors, surprisal analysis characterizes a miRNA system response capable of distinguishing GBM samples from normal tissue biopsy samples. We indicate that the miRNAs contributing to this system behavior is a disease phenotypic state specific to GBM and is therefore a unique GBM-specific thermodynamic signature. MiRNAs implicated in the regulation of stochastic signaling processes crucial in the hallmarks of human cancer, dominate this GBM-cancer phenotypic state. With this theory, we were able to distinguish with high fidelity GBM patients solely by monitoring the dynamics of miRNAs present in patients' biopsy samples. We anticipate that the GBM-specific thermodynamic signature will provide a critical translational tool in better characterizing cancer types and in the development of future therapeutics for GBM.Sohila ZadranFrancoise RemacleRaphael LevinePublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 9, Iss 9, p e108171 (2014)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Sohila Zadran Francoise Remacle Raphael Levine Surprisal analysis of Glioblastoma Multiform (GBM) microRNA dynamics unveils tumor specific phenotype.
description	Gliomablastoma multiform (GBM) is the most fatal form of all brain cancers in humans. Currently there are limited diagnostic tools for GBM detection. Here, we applied surprisal analysis, a theory grounded in thermodynamics, to unveil how biomolecule energetics, specifically a redistribution of free energy amongst microRNAs (miRNAs), results in a system deviating from a non-cancer state to the GBM cancer -specific phenotypic state. Utilizing global miRNA microarray expression data of normal and GBM patients tumors, surprisal analysis characterizes a miRNA system response capable of distinguishing GBM samples from normal tissue biopsy samples. We indicate that the miRNAs contributing to this system behavior is a disease phenotypic state specific to GBM and is therefore a unique GBM-specific thermodynamic signature. MiRNAs implicated in the regulation of stochastic signaling processes crucial in the hallmarks of human cancer, dominate this GBM-cancer phenotypic state. With this theory, we were able to distinguish with high fidelity GBM patients solely by monitoring the dynamics of miRNAs present in patients' biopsy samples. We anticipate that the GBM-specific thermodynamic signature will provide a critical translational tool in better characterizing cancer types and in the development of future therapeutics for GBM.
format	article
author	Sohila Zadran Francoise Remacle Raphael Levine
author_facet	Sohila Zadran Francoise Remacle Raphael Levine
author_sort	Sohila Zadran
title	Surprisal analysis of Glioblastoma Multiform (GBM) microRNA dynamics unveils tumor specific phenotype.
title_short	Surprisal analysis of Glioblastoma Multiform (GBM) microRNA dynamics unveils tumor specific phenotype.
title_full	Surprisal analysis of Glioblastoma Multiform (GBM) microRNA dynamics unveils tumor specific phenotype.
title_fullStr	Surprisal analysis of Glioblastoma Multiform (GBM) microRNA dynamics unveils tumor specific phenotype.
title_full_unstemmed	Surprisal analysis of Glioblastoma Multiform (GBM) microRNA dynamics unveils tumor specific phenotype.
title_sort	surprisal analysis of glioblastoma multiform (gbm) microrna dynamics unveils tumor specific phenotype.
publisher	Public Library of Science (PLoS)
publishDate	2014
url	https://doaj.org/article/ce68e07f0e98441c89497ae396b1f263
work_keys_str_mv	AT sohilazadran surprisalanalysisofglioblastomamultiformgbmmicrornadynamicsunveilstumorspecificphenotype AT francoiseremacle surprisalanalysisofglioblastomamultiformgbmmicrornadynamicsunveilstumorspecificphenotype AT raphaellevine surprisalanalysisofglioblastomamultiformgbmmicrornadynamicsunveilstumorspecificphenotype
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Surprisal analysis of Glioblastoma Multiform (GBM) microRNA dynamics unveils tumor specific phenotype.

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