ArticleJournal of the Royal Society, Interface2010
Systems-level cancer gene identification from protein interaction network topology applied to melanogenesis-related functional genomics data.
Article in Journal of the Royal Society, Interface, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.
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Who cites it
36 citing papers in PubMed.
- Review
- Improving Analysis and Annotation of Microarray Data with Protein Interactions.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Identifying genes targeted by disease-associated non-coding SNPs with a protein knowledge graph.PloS one · 2022Article
- Interplay between success and patterns of human collaboration: case study of a Thai Research Institute.Scientific reports · 2021Article
- A predicted protein functional network aids in novel gene mining for characteristic secondary metabolites in tea plantJournal of biosciences · 2020Article
- Large-scale analysis of disease pathways in the human interactome.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2018Article
- Article
- Graph-theoretical model of global human interactome reveals enhanced long-range communicability in cancer networks.PloS one · 2017Article
- PROSNET: INTEGRATING HOMOLOGY WITH MOLECULAR NETWORKS FOR PROTEIN FUNCTION PREDICTION.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2017Article
- Article
- Distinctive Behaviors of Druggable Proteins in Cellular Networks.PLoS computational biology · 2015Article
- The post-genomic era of biological network alignment.EURASIP journal on bioinformatics & systems biology · 2015Article
- Gene expression profiling of ovarian carcinomas and prognostic analysis of outcome.Journal of ovarian research · 2015Article
- Exploiting ontology graph for predicting sparsely annotated gene function.Bioinformatics (Oxford, England) · 2015Article
- Exploring the structure and function of temporal networks with dynamic graphlets.Bioinformatics (Oxford, England) · 2015Article
- Fair evaluation of global network aligners.Algorithms for molecular biology : AMB · 2015Article
- Graphlet signature-based scoring method to estimate protein-ligand binding affinity.Royal Society open science · 2014Article
- Predicting disease associations via biological network analysis.BMC bioinformatics · 2014Article
- Integration of molecular network data reconstructs Gene Ontology.Bioinformatics (Oxford, England) · 2014Article
- Protein-protein interaction networks (PPI) and complex diseases.Gastroenterology and hepatology from bed to bench · 2014Review
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Authors and funding
4 authors.
Funding
Abstract
Many real-world phenomena have been described in terms of large networks. Networks have been invaluable models for the understanding of biological systems. Since proteins carry out most biological processes, we focus on analysing protein-protein interaction (PPI) networks. Proteins interact to perform a function. Thus, PPI networks reflect the interconnected nature of biological processes and analysing their structural properties could provide insights into biological function and disease. We have already demonstrated, by using a sensitive graph theoretic method for comparing topologies of node neighbourhoods called 'graphlet degree signatures', that proteins with similar surroundings in PPI networks tend to perform the same functions. Here, we explore whether the involvement of genes in cancer suggests the similarity of their topological 'signatures' as well. By applying a series of clustering methods to proteins' topological signature similarities, we demonstrate that the obtained clusters are significantly enriched with cancer genes. We apply this methodology to identify novel cancer gene candidates, validating 80 per cent of our predictions in the literature. We also validate predictions biologically by identifying cancer-related negative regulators of melanogenesis identified in our siRNA screen. This is encouraging, since we have done this solely from PPI network topology. We provide clear evidence that PPI network structure around cancer genes is different from the structure around non-cancer genes. Understanding the underlying principles of this phenomenon is an open question, with a potential for increasing our understanding of complex diseases.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.