We propose a new multiplex-based strategy to integrate different layers of genomic information and use them in a coordinate way to identify driving cancer genes. The multiplex 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 coexpression 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 multiplex we studied its partition into communities. To this end we applied a consensus clustering algorithm to the single-layer partitions obtained using state of art community detection methods , combined with a filtering preprocessing of the individual networks . Even if our procedure is valid in principle for any pathology in this work we concentrate in particular on the gastric, lung, pancreas and colorectal cancer and identified from the enrichment analysis of the multiplex communities a set of candidate driver cancer genes. Some of them were already known oncogenes while a few are new and represent the major result of our analysis . The combination of the different layers of information allowed us to extract from the multiplex indications on the regulatory pattern and the functional role of both the already known oncogenes and the new candidate driver genes.
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.
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