Topological and functional discovery in a gene coexpression meta-network of gastric cancer

Department of Surgery, Stanford University, Palo Alto, California, United States
Cancer Research (Impact Factor: 9.28). 01/2006; 66(1):232-41. DOI: 10.1158/0008-5472.CAN-05-2232
Source: PubMed

ABSTRACT Gastric cancer is a leading cause of global cancer mortality, but comparatively little is known about the cellular pathways regulating different aspects of the gastric cancer phenotype. To achieve a better understanding of gastric cancer at the levels of systems topology, functional modules, and constituent genes, we assembled and systematically analyzed a consensus gene coexpression meta-network of gastric cancer incorporating >300 tissue samples from four independent patient populations (the "gastrome"). We find that the gastrome exhibits a hierarchical scale-free architecture, with an internal structure comprising multiple deeply embedded modules associated with diverse cellular functions. Individual modules display distinct subtopologies, with some (cellular proliferation) being integrated within the primary network, and others (ribosomal biosynthesis) being relatively isolated. One module associated with intestinal differentiation exhibited a remarkably high degree of autonomy, raising the possibility that its specific topological features may contribute towards the frequent occurrence of intestinal metaplasia in gastric cancer. At the single-gene level, we discovered a novel conserved interaction between the PLA2G2A prognostic marker and the EphB2 receptor, and used tissue microarrays to validate the PLA2G2A/EphB2 association. Finally, because EphB2 is a known target of the Wnt signaling pathway, we tested and provide evidence that the Wnt pathway may also similarly regulate PLA2G2A. Many of these findings were not discernible by studying the single patient populations in isolation. Thus, besides enhancing our knowledge of gastric cancer, our results show the broad utility of applying meta-analytic approaches to genome-wide data for the purposes of biological discovery.

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Available from: Alex Boussioutas, Jun 11, 2014
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