Abstract
Background: The DREAM Challenge evaluated methods to identify molecular pathways facilitating the detection of multiple genes affecting critical interactions and processes. Dysregulation of pathways by well-known driver genes is often found in the development and progression of cancer. We used the gene interaction networks provided and the scoring rounds to test disease module identification methods to nominate candidate driver genes in these modules. Method: Our algorithm calculated the proportion of the whole network accessible in two steps from each node in a combined network, which was defined as a 2-reach gene value. Genes with high 2-reach values were used to form the center of star cover clusters. These clusters were assessed for significant modules. Within these modules we identified novel candidate driver genes, by considering the parent-child relationship of well-known driver genes. Disturbance to such driver genes or their upstream parents, can lead to disruption of highly regulated signals affecting the normal functions of cells. We explored these parents as a potential source for candidate driver genes. Results: An initial list of 57 candidate driver genes was identified from 13 significant modules. Analysis of the parent-child relationships of well-known driver genes in these modules prioritized PRKDC, YWHAB, GSK3B, and PPP1CB. Conclusion: Our method incorporated the simple m-reach topology metric in disease module identification and its relationship with known driver genes to identify candidate genes. The four genes shortlisted have been highlighted in recent publications in the literature, which supports the need for further wet lab experimental investigation.
Highlights
Cancer is a disease of uncontrolled cell proliferation
We considered driver gene parent protein products that were functionally related in physical protein-protein interaction (PPI) networks and co-expression networks as candidate driver genes
The CMgenes were based on the network module identification and genome-wide association studies (GWAS) scoring, while the parent genes (Pgenes) were based on the parent-child relationships with well-known driver genes in the signaling network
Summary
Cancer is a disease of uncontrolled cell proliferation. Genetic mutations alter operations inside normal cells in ways that promote tumorigenesis. As listed in the Cancer Genome Census (CGC), within this signaling network provided points of reference. Disturbance to these driver genes or their upstream parents by mutations in either parent or child, can lead to disruption of highly regulated signals affecting the normal functions of cells. We explored these parents as potential candidate driver genes. Each of our modules were scored for enrichment against 104 GWAS, using their PASCAL tool8 We classified these novel disease pathways with CGC known driver genes as cancer modules. The signaling parent genes, which formed part of these novel cancer pathways, were nominated as candidate driver genes
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