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Computational analysis of the mesenchymal signature landscape in gliomas
BACKGROUND: Epithelial to mesenchymal transition, and mimicking processes, contribute to cancer invasion and metastasis, and are known to be responsible for resistance to various therapeutic agents in many cancers. While a number of studies have proposed molecular signatures that characterize the sp...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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BioMed Central
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5345226/ https://www.ncbi.nlm.nih.gov/pubmed/28279210 http://dx.doi.org/10.1186/s12920-017-0252-7 |
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author | Celiku, Orieta Tandle, Anita Chung, Joon-Yong Hewitt, Stephen M. Camphausen, Kevin Shankavaram, Uma |
author_facet | Celiku, Orieta Tandle, Anita Chung, Joon-Yong Hewitt, Stephen M. Camphausen, Kevin Shankavaram, Uma |
author_sort | Celiku, Orieta |
collection | PubMed |
description | BACKGROUND: Epithelial to mesenchymal transition, and mimicking processes, contribute to cancer invasion and metastasis, and are known to be responsible for resistance to various therapeutic agents in many cancers. While a number of studies have proposed molecular signatures that characterize the spectrum of such transition, more work is needed to understand how the mesenchymal signature (MS) is regulated in non-epithelial cancers like gliomas, to identify markers with the most prognostic significance, and potential for therapeutic targeting. RESULTS: Computational analysis of 275 glioma samples from “The Cancer Genome Atlas” was used to identify the regulatory changes between low grade gliomas with little expression of MS, and high grade glioblastomas with high expression of MS. TF (transcription factor)-gene regulatory networks were constructed for each of the cohorts, and 5 major pathways and 118 transcription factors were identified as involved in the differential regulation of the networks. The most significant pathway - Extracellular matrix organization - was further analyzed for prognostic relevance. A 20-gene signature was identified as having prognostic significance (HR (hazard ratio) 3.2, 95% CI (confidence interval) = 1.53–8.33), after controlling for known prognostic factors (age, and glioma grade). The signature’s significance was validated in an independent data set. The putative stem cell marker CD44 was biologically validated in glioma cell lines and brain tissue samples. CONCLUSIONS: Our results suggest that the differences between low grade gliomas and high grade glioblastoma are associated with differential expression of the signature genes, raising the possibility that targeting these genes might prolong survival in glioma patients. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12920-017-0252-7) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-5345226 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-53452262017-03-14 Computational analysis of the mesenchymal signature landscape in gliomas Celiku, Orieta Tandle, Anita Chung, Joon-Yong Hewitt, Stephen M. Camphausen, Kevin Shankavaram, Uma BMC Med Genomics Research Article BACKGROUND: Epithelial to mesenchymal transition, and mimicking processes, contribute to cancer invasion and metastasis, and are known to be responsible for resistance to various therapeutic agents in many cancers. While a number of studies have proposed molecular signatures that characterize the spectrum of such transition, more work is needed to understand how the mesenchymal signature (MS) is regulated in non-epithelial cancers like gliomas, to identify markers with the most prognostic significance, and potential for therapeutic targeting. RESULTS: Computational analysis of 275 glioma samples from “The Cancer Genome Atlas” was used to identify the regulatory changes between low grade gliomas with little expression of MS, and high grade glioblastomas with high expression of MS. TF (transcription factor)-gene regulatory networks were constructed for each of the cohorts, and 5 major pathways and 118 transcription factors were identified as involved in the differential regulation of the networks. The most significant pathway - Extracellular matrix organization - was further analyzed for prognostic relevance. A 20-gene signature was identified as having prognostic significance (HR (hazard ratio) 3.2, 95% CI (confidence interval) = 1.53–8.33), after controlling for known prognostic factors (age, and glioma grade). The signature’s significance was validated in an independent data set. The putative stem cell marker CD44 was biologically validated in glioma cell lines and brain tissue samples. CONCLUSIONS: Our results suggest that the differences between low grade gliomas and high grade glioblastoma are associated with differential expression of the signature genes, raising the possibility that targeting these genes might prolong survival in glioma patients. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12920-017-0252-7) contains supplementary material, which is available to authorized users. BioMed Central 2017-03-09 /pmc/articles/PMC5345226/ /pubmed/28279210 http://dx.doi.org/10.1186/s12920-017-0252-7 Text en © The Author(s). 2017 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided 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 Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Article Celiku, Orieta Tandle, Anita Chung, Joon-Yong Hewitt, Stephen M. Camphausen, Kevin Shankavaram, Uma Computational analysis of the mesenchymal signature landscape in gliomas |
title | Computational analysis of the mesenchymal signature landscape in gliomas |
title_full | Computational analysis of the mesenchymal signature landscape in gliomas |
title_fullStr | Computational analysis of the mesenchymal signature landscape in gliomas |
title_full_unstemmed | Computational analysis of the mesenchymal signature landscape in gliomas |
title_short | Computational analysis of the mesenchymal signature landscape in gliomas |
title_sort | computational analysis of the mesenchymal signature landscape in gliomas |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5345226/ https://www.ncbi.nlm.nih.gov/pubmed/28279210 http://dx.doi.org/10.1186/s12920-017-0252-7 |
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