Discovery and Scoring of Protein Interaction Subnetworks Discriminative of Late Stage Human Colon Cancer

Abstract

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 proteinprotein 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,

An Evolutionary Biology Approach to Understanding Neurological Disorders

“…Nonetheless, this approach has already been proved to be effective for the identification of complex disease genes, including those involved in colon cancer (Nibbe et al, 2009). Therefore, analyzing the functional networks of human genes is providing a key framework for prioritizing candidate disease genes.…”

Protein-protein Interactions: Network Analysis and Applications in Drug Discovery

“…Integrating PPI networks with expression data, for example, led to the identification of cancer susceptibility genes and oncogenes in breast carcinomas [122], B-cell acute myeloid lymphomas [123,124]; the identification of markers for metastasis in breast [125,126], colorectal [127,128] and gastric [129] cancer; the prediction of disease outcome [130] and response to chemotherapy [131,132] and the determination of therapy-resistance genes [133,134]. By combining protein-protein and protein-DNA interaction studies Kim et al identified a Myc-centered regulatory network in embryonic stem cells, and showed that the Myc module is active in various cancers and predicts cancer outcome [135].…”

Subnetwork State Functions Define Dysregulated Subnetworks in Cancer

“…Subnetworks identified by this approach are also used as features for classification of breast cancer metastasis, providing significant improvement over single-gene markers [20]. Nibbe et al [21,22] show that this notion of coordinate dysregulation is also effective in integrating protein and mRNA expression data to identify important subnetworks in colon cancer (CRC). Anastassiou [23] introduces the concept of synergy to delineate the complementarity of multiple genes in the manifestation of phenotype.…”