Identification of Multiple Hypoxia Signatures in Neuroblastoma Cell Lines by l(1)-l(2) Regularization and Data Reduction
Name
Fardin-2010-Identification of Multiple Hypoxia .pdf
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Author(s)
Rosasco, Lorenzo Andrea
Fardin, Paolo
Cornero, Andrea
Barla, Annalisa
Mosci, Sofia
Acquaviva, Massimo
Gambini, Claudio
Verri, Alessandro
Varesio, Luigi
Alternative Title
Identification of Multiple Hypoxia Signatures in Neuroblastoma Cell Lines by l [subscript 1]-l subscript 2] Regularization and Data Reduction
Date Issued
April 2010
Journal
Journal of Biomedicine and Biotechnology
Publisher
Hindawi
Citation
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.
Version
Final published version
Abstract
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.
MIT Department
Massachusetts Institute of Technology. Center for Biological & Computational Learning
McGovern Institute for Brain Research at MIT
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DOI of Published Version
https://doi.org/10.1155/2010/878709
