  We propose a new multi-network-based strategy to integrate different layers
of genomic information and use them in a coordinate way to identify driving
cancer genes. The multi-networks that we consider combine transcription factor
co-targeting, microRNA co-targeting, protein-protein interaction and gene
co-expression networks. The rationale behind this choice is that gene
co-expression and protein-protein interactions require a tight coregulation of
the partners and that such a fine tuned regulation can be obtained only
combining both the transcriptional and post-transcriptional layers of
regulation. To extract the relevant biological information from the
multi-network we studied its partition into communities. To this end we applied
a consensus clustering algorithm based on state of art community detection
methods. Even if our procedure is valid in principle for any pathology in this
work we concentrate on gastric, lung, pancreas and colorectal cancer and
identified from the enrichment analysis of the multi-network communities a set
of candidate driver cancer genes. Some of them were already known oncogenes
while a few are new. The combination of the different layers of information
allowed us to extract from the multi-network indications on the regulatory
pattern and functional role of both the already known and the new candidate
driver genes.
