Global topological features of cancer proteins in the human interactome

By Pall F. Jonsson, Paul A. Bates. 2006

519 citations recorded by the source corpus.

Motivation The study of interactomes, or networks of protein-protein interactions, is increasingly providing valuable information on biological systems. Here we report a study of cancer proteins in an extensive human protein-protein interaction network constructed by computational methods. Results We show that human proteins translated from known cancer genes exhibit a network topology that is different from that of proteins not documented as being mutated in cancer. In particular, cancer proteins show an increase in the number of proteins they interact with. They also appear to participate in central hubs rather than peripheral ones, mirroring their greater centrality and participation in networks that form the backbone of the proteome. Moreover, we show that cancer proteins contain a high ratio of highly promiscuous structural domains, i.e., domains with a high propensity for mediating protein interactions. These observations indicate an underlying evolutionary distinction between the two groups of proteins, reflecting the central roles of proteins, whose mutations lead to cancer.

Subfields: Computational Theory and Mathematics, Molecular Biology

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Citations in the corpus

EgoNet: identification of human disease ego-network modules (2014, neutral): “Similar conclusions have also been reported in the literature [61,62].”
Network-based methods for human disease gene prediction. (2011, background): “A similar conclusion was drawn by Jonsson and Bates [30] that cancer-related proteins have about twice the interaction partners when compared with proteins unrelated to cancer.”
Algorithms for Detecting Significantly Mutated Pathways in Cancer (2010, unknown): “There are reports that cancer-associated genes have more interaction partners than non-cancer genes (Lin et al., 2007; Jonsson and Bates, 2006), and indeed highly mutated cancer genes like TP53 have high degree in most interaction networks (e.g., the degree of TP53 in HPRD is 236).”
Network Medicine: A Network-based Approach to Human Disease (2011, background): “In a separate study, 346 proteins that are implicated in cancer were found to have, on average, twice as many interaction partners as did non-cancer protein”
Structure and dynamics of molecular networks: A novel paradigm of drug discovery: A comprehensive review (2012, background): “They form a rich-club, acting as bridges between modules of different functions, and behave as bottlenecks providing exclusive connections between network segments or are otherwise central nodes (Jonsson & Bates, 2006; Chuang et al., 2007; Sun & Zhao, 2010; Rosado et al., 2011; Xia et al., 2011).”
Protein networks in disease. (2008, background): “In humans, some groups report an elevated degree for disease genes (Wachi et al. 2005; Jonsson and Bates 2006 ), while others do not (Goh et al. 2007).”
NOA: a novel Network Ontology Analysis method (2011, background): “It is well known that tumor-related genes are important and tend to have many functions (36).”
The Roles of Post-translational Modifications in the Context of Protein Interaction Networks (2015, background): “The increased likelihood of a functional association of proteins with high interaction degree and their involvement in human disease has been reported before [52, 53].”