An Integrative -omics Approach to Identify Functional Sub-Networks in Human Colorectal Cancer

Article (PDF Available)inPLoS Computational Biology 6(1):e1000639 · January 2010with16 Reads
DOI: 10.1371/journal.pcbi.1000639 · Source: PubMed
Abstract
Emerging evidence indicates that gene products implicated in human cancers often cluster together in "hot spots" in protein-protein interaction (PPI) networks. Additionally, small sub-networks within PPI networks that demonstrate synergistic differential expression with respect to tumorigenic phenotypes were recently shown to be more accurate classifiers of disease progression when compared to single targets identified by traditional approaches. However, many of these studies rely exclusively on mRNA expression data, a useful but limited measure of cellular activity. Proteomic profiling experiments provide information at the post-translational level, yet they generally screen only a limited fraction of the proteome. Here, we demonstrate that integration of these complementary data sources with a "proteomics-first" approach can enhance the discovery of candidate sub-networks in cancer that are well-suited for mechanistic validation in disease. We propose that small changes in the mRNA expression of multiple genes in the neighborhood of a protein-hub can be synergistically associated with significant changes in the activity of that protein and its network neighbors. Further, we hypothesize that proteomic targets with significant fold change between phenotype and control may be used to "seed" a search for small PPI sub-networks that are functionally associated with these targets. To test this hypothesis, we select proteomic targets having significant expression changes in human colorectal cancer (CRC) from two independent 2-D gel-based screens. Then, we use random walk based models of network crosstalk and develop novel reference models to identify sub-networks that are statistically significant in terms of their functional association with these proteomic targets. Subsequently, using an information-theoretic measure, we evaluate synergistic changes in the activity of identified sub-networks based on genome-wide screens of mRNA expression in CRC. Cross-classification experiments to predict disease class show excellent performance using only a few sub-networks, underwriting the strength of the proposed approach in discovering relevant and reproducible sub-networks.

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    • "The complete human protein interaction network from Pathway Commons [21] was used as the backbone network topological structure, and only interactions in the backbone network are kept to construct the raw GGI network. Such methodological approach has been proven fruitful in a variety of tumor genetic research fields [10,[22][23][24][25][26][27]. LPRP detects both linear and probabilistic relations among genes. "
    [Show abstract] [Hide abstract] ABSTRACT: The importance of the construction of gene-gene interaction (GGI) network to better understand breast cancer has previously been highlighted. In this study, we propose a novel GGI network construction method called linear and probabilistic relations prediction (LPRP) and used it for gaining system level insight into breast cancer mechanisms. We construct separate genome-wide GGI networks for tumor and normal breast samples, respectively, by applying LPRP on their gene expression datasets profiled by The Cancer Genome Atlas. According to our analysis, a large loss of gene interactions in the tumor GGI network was observed (7436; 88.7 % reduction), which also contained fewer functional genes (4757; 32 % reduction) than the normal network. Tumor GGI network was characterized by a bigger network diameter and a longer characteristic path length but a smaller clustering coefficient and much sparse network connections. In addition, many known cancer pathways, especially immune response pathways, are enriched by genes in the tumor GGI network. Furthermore, potential cancer genes are filtered in this study, which may act as drugs targeting genes. These findings will allow for a better understanding of breast cancer mechanisms.
    Article · Sep 2016
    • "Though previous studies combine mutational or differential expression data with protein interaction networks, few use network information to integrate mutational and expression data. In particular, Nibbe et al. [11] propose a method that integrates protein expression data with mRNA expression data, with the purpose of extending the scale of of proteomic data that has limited coverage of the proteome. In Nibbe et al.'s study proteomic and transcriptomic data from different patients is used to integrate mRNA-level gene expression and protein expression data. "
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    Full-text · Article · Dec 2015
    • "Weinberg et al. summarized the first and next generation of cancer hallmarks to expand the current understanding of the basic mechanisms of cancer [2,3]. Recently, due to the scale up in high throughput data, availability of integrated OMICS data, and various advanced statistical analysis methods, many novel systems biology approaches have been employed to reveal the deeper underlying systematic mechanisms of various cancers456. Traditional computer-aided drug design (CADD) focuses on a single target for therapy, such as Src, FAK, and EGFR in the case of cancer [7,8]. "
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