In post-genomic cancer research laboratories, microarray-based techniques are commonly employed to find valuable insights into differences in an individual's tumor as compared with constitutional DNA, mRNA (or miRNA) expression and protein activity. The application of these high-throughput technologies is providing huge amounts of data that should be computationally processed and analyzed in order to extract valuable biological information.
The Bioinformatics Unit is currently working with various CNIO wet-lab groups and external collaborators on projects related to the analysis of large-scale data sets (i.e. gene expression and miRNAs microarrays, ChIP on chip, etc.). As a matter of course, we carry out the processing and normalization of several gene expression microarray platforms (Agilent, Affymetrix, Codelink, etc.). Additionally, statistical differential expression testing, geneset-oriented studies and functional analysis of gene lists of interest are offered to both CNIO users and external collaborators. This working attitude has provided a number of successful collaborations joining computational and experimental scenarios.
To illustrate this, Ferreira et al. (Oncogene, 2008) have employed a high-throughput approach showing a genomically unstable group of Ewing’s tumours that is correlated with a signiﬁcant poor prognosis. Interestingly, gene-set based approach revealed some significantly enriched biological pathways related to DNA repair and cell cycle control (ATRBRCA, Rb pathway, etc.) in this group of patients.
In the same way, García-Aragoncillo et al. (Oncogene, 2008) have revealed a new function for DAX1 gene as a cell-cycle progression regulator in Ewing’s sarcomas. In this work, the functional analysis of gene expression data identiﬁed that Rb and Cell Cycle pathways are similarly regulated by EWS/FLI1 (see Figure 1), a chimeric protein related to Ewing’s sarcoma oncogenesis, and DAX1.