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Molecular & Cellular Proteomics 8:827-845, 2009.
© 2009 by The American Society for Biochemistry and Molecular Biology, Inc.

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Research

Discovery and Scoring of Protein Interaction Subnetworks Discriminative of Late Stage Human Colon Cancer^*,S

Rod K. Nibbe^,,¶, Sanford Markowitz^||,**, Lois Myeroff^||, Rob Ewing and Mark R. Chance

From the Departments of Pharmacology and ^|| Medicine, Case Center for Proteomics and Bioinformatics, and ^** Howard Hughes Medical Institute, Case Western Reserve University, Cleveland, Ohio 44106

We used a systems biology approach to identify and score protein interaction subnetworks whose activity patterns are discriminative of late stage human colorectal cancer (CRC) versus control in colonic tissue. We conducted two gel-based proteomics experiments to identify significantly changing proteins between normal and late stage tumor tissues obtained from an adequately sized cohort of human patients. A total of 67 proteins identified by these experiments was used to seed a search for protein-protein interaction subnetworks. A scoring scheme based on mutual information, calculated using gene expression data as a proxy for subnetwork activity, was developed to score the targets in the subnetworks. Based on this scoring, the subnetwork was pruned to identify the specific protein combinations that were significantly discriminative of late stage cancer versus control. These combinations could not be discovered using only proteomics data or by merely clustering the gene expression data. We then analyzed the resultant pruned subnetwork for biological relevance to human CRC. A number of the proteins in these smaller subnetworks have been associated with the progression (CSNK2A2, PLK1, and IGFBP3) or metastatic potential (PDGFRB) of CRC. Others have been recently identified as potential markers of CRC (IFITM1), and the role of others is largely unknown in this disease (CCT3, CCT5, CCT7, and GNA12). The functional interactions represented by these signatures provide new experimental hypotheses that merit follow-on validation for biological significance in this disease. Overall the method outlines a quantitative approach for integrating proteomics data, gene expression data, and the wealth of accumulated legacy experimental data to discover significant protein subnetworks specific to disease.

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