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Journal of Biomedicine and Biotechnology
Volume 2010, Article ID 878709, 11 pages
Methodology Report

Identification of Multiple Hypoxia Signatures in Neuroblastoma Cell Lines by - Regularization and Data Reduction

1Laboratory of Molecular Biology, Gaslini Institute, 16147 Genoa, Italy
2Department of Computer and Information Science, University of Genoa, 16146 Genoa, Italy
3Center for Biological & Computational Learning, MIT, Cambridge, MA 02139, USA
4Human Pathology Section, Gaslini Institute, 16147 Genoa, Italy

Received 10 February 2010; Accepted 28 April 2010

Academic Editor: Xin-yuan Guan

Copyright © 2010 Paolo Fardin et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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 - 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. - 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.