Integrating Gene Regulatory Networks to identify cancer-specific genes

Abstract

Consensus approaches have been widely used to identify Gene Regulatory Networks (GRNs) that are common to multiple studies. However, in this research we develop an application that semi-automatically identifies key mechanisms that are specific to a particular set of conditions. We analyse four different types of cancer to identify gene pathways unique to each of them. To support the results reliability we calculate the prediction accuracy of each gene for the specified conditions and compare to predictions on other conditions. The most predictive are validated using the GeneCards encyclopaedia1 coupled with a statistical test for validating clusters. Finally, we implement an interface that allows the user to identify unique subnetworks of any selected combination of studies using AND & NOT logic operators. Results show that unique genes and sub-networks can be reliably identified and that they reflect key mechanisms that are fundamental to the cancer types under study.

Figure 1:

Nodes with grey background indicate a prediction accuracy for the nodes greater than 0.6. Isolated nodes do not have connections due to the structure differences between glasso U-Ns and Bayesian U-Ns. Nodes are labelled with numbers (directly corresponding to the gene ID) for visualization purposes.

Figure 2:

Internal vs External prediction accuracy for each study averaged among all genes involved in the related unique-network.

Figure 3:

Logic Application interface.