OBJECTIVE: A complex disease is generally caused by the mutation of multiple genes or by the dysfunction of multiple biological processes. Systematic identification of causal disease genes and module biomarkers can provide insights into the mechanisms underlying complex diseases, and help develop efficient therapies or effective drugs. MATERIALS AND METHODS: In this paper, we present a novel approach to predict disease genes and identify dysfunctional networks or modules, based on the analysis of differential interactions between disease and control samples, in contrast to the analysis of differential gene or protein expressions widely adopted in existing methods. RESULTS AND DISCUSSION: As an example, we applied our method to the study of three-stage microarray data for gastric cancer. We identified network modules or module biomarkers that include a set of genes related to gastric cancer, implying the predictive power of our method. The results on holdout validation data sets show that our identified module can serve as an effective module biomarker for accurately detecting or diagnosing gastric cancer, thereby validating the efficiency of our method. CONCLUSION: We proposed a new approach to detect module biomarkers for diseases, and the results on gastric cancer demonstrated that the differential interactions are useful to detect dysfunctional modules in the molecular interaction network, which in turn can be used as robust module biomarkers.
OBJECTIVE: A complex disease is generally caused by the mutation of multiple genes or by the dysfunction of multiple biological processes. Systematic identification of causal disease genes and module biomarkers can provide insights into the mechanisms underlying complex diseases, and help develop efficient therapies or effective drugs. MATERIALS AND METHODS: In this paper, we present a novel approach to predict disease genes and identify dysfunctional networks or modules, based on the analysis of differential interactions between disease and control samples, in contrast to the analysis of differential gene or protein expressions widely adopted in existing methods. RESULTS AND DISCUSSION: As an example, we applied our method to the study of three-stage microarray data for gastric cancer. We identified network modules or module biomarkers that include a set of genes related to gastric cancer, implying the predictive power of our method. The results on holdout validation data sets show that our identified module can serve as an effective module biomarker for accurately detecting or diagnosing gastric cancer, thereby validating the efficiency of our method. CONCLUSION: We proposed a new approach to detect module biomarkers for diseases, and the results on gastric cancer demonstrated that the differential interactions are useful to detect dysfunctional modules in the molecular interaction network, which in turn can be used as robust module biomarkers.
Authors: Yibin Kang; Wei He; Shaun Tulley; Gaorav P Gupta; Inna Serganova; Chang-Rung Chen; Katia Manova-Todorova; Ronald Blasberg; William L Gerald; Joan Massagué Journal: Proc Natl Acad Sci U S A Date: 2005-09-19 Impact factor: 11.205
Authors: G J Riggins; S Thiagalingam; E Rozenblum; C L Weinstein; S E Kern; S R Hamilton; J K Willson; S D Markowitz; K W Kinzler; B Vogelstein Journal: Nat Genet Date: 1996-07 Impact factor: 38.330
Authors: P Andrew Futreal; Lachlan Coin; Mhairi Marshall; Thomas Down; Timothy Hubbard; Richard Wooster; Nazneen Rahman; Michael R Stratton Journal: Nat Rev Cancer Date: 2004-03 Impact factor: 60.716
Authors: N Y Sung; K S Choi; E C Park; K Park; S Y Lee; A K Lee; I J Choi; K W Jung; Y J Won; H R Shin Journal: Br J Cancer Date: 2007-07-17 Impact factor: 7.640