Identification of multiple hypoxia signatures in neuroblastoma cell lines by l1-l2 regularization and data reduction

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(1)-l(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(1)-l(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.

Figure 1

Three-dimensional representation of the principal components analysis of the Glycolysis GO process. Principal components representation of the multivariate analysis performed on the 9 cell lines by l₁-l₂ algorithm. This figure illustrates a 3-dimensional visualization of the dataset restricted to the selected probesets projected on their 3 principal components. Red squares (H) represent the cell lines in hypoxic status and the blue circles (N) the corresponding cell lines in normoxic status. The numbers indicate the cell lines.

Figure 2

Symmetrical heatmap of the correlation analysis for oxidative phosphorylation-signature probesets. The correlation values among the 32 probesets selected by l₁-l₂ algorithm (ε = 100) for oxidative phosphorylation process are reported. The probesets, named accordingly to Affymetrix HG-U133 Plus 2.0 GeneChip platform, have been subdivided into 8 clusters according to the similarity among their correlation profiles by hierarchical clustering. Cluster ID is indicated at the top of the figure and correlation scale is reported on the right side.