Detection of gene communities in multi-networks reveals cancer drivers

Abstract

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.

minor modifications

References in corpus (11)

• Fast unfolding of communities in large networks
• Maps of random walks on complex networks reveal community structure
• Near linear time algorithm to detect community structures in large-scale networks
• Multilayer Networks
• The structure and dynamics of multilayer networks
• Resolution limit in community detection
• Comparing community structure identification
• Finding statistically significant communities in networks
• Extracting the multiscale backbone of complex weighted networks
• Consensus clustering in complex networks
• Extracting the hierarchical organization of complex systems

Cited by in corpus (6)

• Centralities of Nodes and Influences of Layers in Large Multiplex Networks
• IEDC: An Integrated Approach for Overlapping and Non-overlapping Community Detection
• A Framework for the Construction of Generative Models for Mesoscale Structure in Multilayer Networks
• Assessing diversity in multiplex networks
• An Evaluation Tool for Backbone Extraction Techniques in Weighted Complex Networks
• Gene communities in co-expression networks across different tissues