Finding biological process modifications in cancer tissues by mining gene expression correlations

We apply here this "functional correlations comparison" approach to identify the existing correlations in hepatocarcinoma (161 microarray experiments) and to reveal functional differences between normal liver and cancer tissues. The number of well-correlated pairs in each GO term highlights several differences in genetic interactions between cancer and normal tissues. We performed a bootstrap analysis in order to compute false detection rates (FDR) and confidence limits.Experimental results show the main advantage of the applied method: it both picks up general and specific GO terms (in particular it shows a fine resolution in the specific GO terms). The results obtained by this novel method are highly coherent with the ones proposed by other cancer biology studies. But additionally they highlight the most specific and interesting GO terms helping the biologist to focus his/her studies on the most relevant biological processes.From DNA microarray experiments, we can obtain genome-wide data about gene expression [1-3]. Each gene may be involved in one or more biological process/es. The biological process is described in the Gene Ontology datatabase (GO) provided by the GO consortium [4]. Merging microarray results, gene information and GO data within an experimental dataset allows efficient mining of functional knowledge and, for example, it can be useful in identifying differences between normal and cancer tissues.Mutations are gained during carcinogenesis and tumour progression. Chromosomal rearrangements too lead to dysregulation of a number of cellular processes. We therefore hypothesized that it should be possible to identify deranged molecular pathways by mining expression profiles. The rationale is based on the assumption that although expression data do not give direct insight into mutations and rearrangements, they can reveal the molecular imprints consequential to oncogenic changes in cellular DNA. In fact, because tumours are the results of stratified gene