Global topological features of cancer proteins in the human interactome

Abstract

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.

Contact: paul.bates@cancer.org.uk

Supplementary information: The interactome data are available though the PIP (Potential Interactions of Proteins) web server at http://bmm.cancerresearchuk.org/servers/pip. Further additional material is available at http://bmm.cancerresearchuk.org/servers/pip/bioinformatics/

Fig. 1

Curve of receiver operating characteristics (ROC) at different cut-off points of scores. The area under the curve is 0.89.

Fig. 2

The number of interactions, in which cancer proteins participate (left), compared with the number of interactions of non-cancer proteins (right). Cancer proteins are, on average, involved in 23.4 interactions, whereas non-cancer proteins are involved in 11.4. The centre of the box is the median and the box spans from first to third quartiles (the inter-quartile range). The whiskers extend to the furthest point within 1.5 times the inter-quartile range. Beyond the whiskers, all outliers are shown, in open circles up to a distance of three times the inter-quartile range and closed beyond that.

Fig. 3

A description of the protein communities identified by k-clique cluster analysis (k = 6). Each community is distinctly coloured, with cancer proteins shown as triangles. The main functional classes of each cluster (in bold) and individual pathways, as defined in the KEGG database, are listed alongside each community. Note that proteins can be members of more than one community, but the figure shows only one community assignment for each protein. A detailed version of this figure, including gene names for each protein, is available as supplementary material (see Supplementary Figure 2).

Fig. 4

Average size distribution of protein communities that contain cancer proteins compared to those containing non-cancer proteins. Distributions are classified according to clustering k-value, with cancer communities on the left and non-cancer on the right. The difference between cancer and non-cancer groups is statistically significant, according to Wilcoxon rank sum tests, for k-values 3, 4, 5 (p < 0.005), and 6 (p < 0.05).