Identification of Multiple Hypoxia Signatures in Neuroblastoma Cell Lines by l(1)-l(2) Regularization and Data Reduction

Hypoxia is a condition of low oxygen tension occurring in the tumor and negatively correlated with the progression of the disease. We studied the gene expression profiles of nine neuroblastoma cell lines grown under hypoxic conditions to define gene signatures that characterize hypoxic neuroblastoma...

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Main Authors: Rosasco, Lorenzo Andrea, Fardin, Paolo, Cornero, Andrea, Barla, Annalisa, Mosci, Sofia, Acquaviva, Massimo, Gambini, Claudio, Verri, Alessandro, Varesio, Luigi
Other Authors: Massachusetts Institute of Technology. Center for Biological & Computational Learning
Format: Article
Language:en_US
Published: Hindawi 2011
Online Access:http://hdl.handle.net/1721.1/61340
https://orcid.org/0000-0001-6376-4786
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author Rosasco, Lorenzo Andrea
Fardin, Paolo
Cornero, Andrea
Barla, Annalisa
Mosci, Sofia
Acquaviva, Massimo
Gambini, Claudio
Verri, Alessandro
Varesio, Luigi
author2 Massachusetts Institute of Technology. Center for Biological & Computational Learning
author_facet Massachusetts Institute of Technology. Center for Biological & Computational Learning
Rosasco, Lorenzo Andrea
Fardin, Paolo
Cornero, Andrea
Barla, Annalisa
Mosci, Sofia
Acquaviva, Massimo
Gambini, Claudio
Verri, Alessandro
Varesio, Luigi
author_sort Rosasco, Lorenzo Andrea
collection MIT
description Hypoxia is a condition of low oxygen tension occurring in the tumor and negatively correlated with the progression of the disease. We studied the gene expression profiles of nine neuroblastoma cell lines grown under hypoxic conditions to define gene signatures that characterize hypoxic neuroblastoma. The l[subscript 1]-l[subscript 2] regularization applied to the entire transcriptome identified a single signature of 11 probesets discriminating the hypoxic state. We demonstrate that new hypoxia signatures, with similar discriminatory power, can be generated by a prior knowledge-based filtering in which a much smaller number of probesets, characterizing hypoxia-related biochemical pathways, are analyzed. l[subscript 1]-l[subscript 2] regularization identified novel and robust hypoxia signatures within apoptosis, glycolysis, and oxidative phosphorylation Gene Ontology classes. We conclude that the filtering approach overcomes the noisy nature of the microarray data and allows generating robust signatures suitable for biomarker discovery and patients risk assessment in a fraction of computer time.
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spelling mit-1721.1/613402022-09-29T20:41:54Z Identification of Multiple Hypoxia Signatures in Neuroblastoma Cell Lines by l(1)-l(2) Regularization and Data Reduction Identification of Multiple Hypoxia Signatures in Neuroblastoma Cell Lines by l [subscript 1]-l subscript 2] Regularization and Data Reduction Rosasco, Lorenzo Andrea Fardin, Paolo Cornero, Andrea Barla, Annalisa Mosci, Sofia Acquaviva, Massimo Gambini, Claudio Verri, Alessandro Varesio, Luigi Massachusetts Institute of Technology. Center for Biological & Computational Learning McGovern Institute for Brain Research at MIT Rosasco, Lorenzo Andrea Rosasco, Lorenzo Andrea Hypoxia is a condition of low oxygen tension occurring in the tumor and negatively correlated with the progression of the disease. We studied the gene expression profiles of nine neuroblastoma cell lines grown under hypoxic conditions to define gene signatures that characterize hypoxic neuroblastoma. The l[subscript 1]-l[subscript 2] regularization applied to the entire transcriptome identified a single signature of 11 probesets discriminating the hypoxic state. We demonstrate that new hypoxia signatures, with similar discriminatory power, can be generated by a prior knowledge-based filtering in which a much smaller number of probesets, characterizing hypoxia-related biochemical pathways, are analyzed. l[subscript 1]-l[subscript 2] regularization identified novel and robust hypoxia signatures within apoptosis, glycolysis, and oxidative phosphorylation Gene Ontology classes. We conclude that the filtering approach overcomes the noisy nature of the microarray data and allows generating robust signatures suitable for biomarker discovery and patients risk assessment in a fraction of computer time. Fondazione Italiana per la Lotta al Neuroblastoma Italian Association for Cancer Research (AIRC) Italian Ministry of Health EU Integrated Project Health-e-Child (IST-2004-027749) Compagnia di San Paolo (Foundation) (Project 4998- ID/CV 2007.0887) 2011-02-24T22:52:20Z 2011-02-24T22:52:20Z 2010-04 2010-02 Article http://purl.org/eprint/type/JournalArticle 1110-7243 http://hdl.handle.net/1721.1/61340 Fardin, Paolo et al. “Identification of Multiple Hypoxia Signatures in Neuroblastoma Cell Lines by l1-l2 Regularization and Data Reduction.” Journal of Biomedicine and Biotechnology 2010 (2010): 1-12. https://orcid.org/0000-0001-6376-4786 en_US http://dx.doi.org/10.1155/2010/878709 Journal of Biomedicine and Biotechnology Creative Commons Attribution http://creativecommons.org/licenses/by/2.0/ application/pdf Hindawi Hindawi
spellingShingle Rosasco, Lorenzo Andrea
Fardin, Paolo
Cornero, Andrea
Barla, Annalisa
Mosci, Sofia
Acquaviva, Massimo
Gambini, Claudio
Verri, Alessandro
Varesio, Luigi
Identification of Multiple Hypoxia Signatures in Neuroblastoma Cell Lines by l(1)-l(2) Regularization and Data Reduction
title Identification of Multiple Hypoxia Signatures in Neuroblastoma Cell Lines by l(1)-l(2) Regularization and Data Reduction
title_full Identification of Multiple Hypoxia Signatures in Neuroblastoma Cell Lines by l(1)-l(2) Regularization and Data Reduction
title_fullStr Identification of Multiple Hypoxia Signatures in Neuroblastoma Cell Lines by l(1)-l(2) Regularization and Data Reduction
title_full_unstemmed Identification of Multiple Hypoxia Signatures in Neuroblastoma Cell Lines by l(1)-l(2) Regularization and Data Reduction
title_short Identification of Multiple Hypoxia Signatures in Neuroblastoma Cell Lines by l(1)-l(2) Regularization and Data Reduction
title_sort identification of multiple hypoxia signatures in neuroblastoma cell lines by l 1 l 2 regularization and data reduction
url http://hdl.handle.net/1721.1/61340
https://orcid.org/0000-0001-6376-4786
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