Characterization of protein-interaction networks in tumors

Alexander Platzer¹ , Paul Perco¹ , Arno Lukas² and Bernd Mayer¹^,2

¹Institute for Theoretical Chemistry, University of Vienna, Waehringer Strasse 17, A-1090 Vienna, Austria

²emergentec biodevelopment GmbH, Rathausstrasse 5/3, A-1010 Vienna, Austria

author email corresponding author email

BMC Bioinformatics 2007, 8:224doi:10.1186/1471-2105-8-224


Published:	27 June 2007

Abstract

Background

Analyzing differential-gene-expression data in the context of protein-interaction networks (PINs) yields information on the functional cellular status. PINs can be formally represented as graphs, and approximating PINs as undirected graphs allows the network properties to be characterized using well-established graph measures.

This paper outlines features of PINs derived from 29 studies on differential gene expression in cancer. For each study the number of differentially regulated genes was determined and used as a basis for PIN construction utilizing the Online Predicted Human Interaction Database.

Results

Graph measures calculated for the largest subgraph of a PIN for a given differential-gene-expression data set comprised properties reflecting the size, distribution, biological relevance, density, modularity, and cycles. The values of a distinct set of graph measures, namely Closeness Centrality, Graph Diameter, Index of Aggregation, Assortative Mixing Coefficient, Connectivity, Sum of the Wiener Number, modified Vertex Distance Number, and Eigenvalues differed clearly between PINs derived on the basis of differential gene expression data sets characterizing malignant tissue and PINs derived on the basis of randomly selected protein lists.

Conclusion

Cancer PINs representing differentially regulated genes are larger than those of randomly selected protein lists, indicating functional dependencies among protein lists that can be identified on the basis of transcriptomics experiments. However, the prevalence of hub proteins was not increased in the presence of cancer. Interpretation of such graphs in the context of robustness may yield novel therapies based on synthetic lethality that are more effective than focusing on single-action drugs for cancer treatment.