Characterization of protein-interaction networks in tumors

• Published in: BMC bioinformatics Vol. 8; no. 1; p. 224
• Main Authors: Platzer, Alexander, Perco, Paul, Lukas, Arno, Mayer, Bernd
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 27-06-2007; BioMed Central; BMC
• Subjects: Algorithms; Biomarkers, Tumor - metabolism; Computer Simulation; Databases, Protein; Gene expression; Gene Expression Profiling - methods; Humans; Identification and classification; Models, Biological; Neoplasm Proteins - metabolism; Neoplasms - metabolism; Protein Interaction Mapping - methods; Protein-protein interactions; Signal Transduction; Tumor suppressor genes; Two-hybrid system; Austria
• ISSN: 1471-2105; 1471-2105
• DOI: 10.1186/1471-2105-8-224

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.
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.
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.