title: Identifying module biomarkers from gastric cancer by differential correlation network.
abstract: Gastric cancer (stomach cancer) is a severe disease caused by dysregulation of many functionally correlated genes or pathways instead of the mutation of individual genes. Systematic identification of gastric cancer biomarkers can provide insights into the mechanisms underlying this deadly disease and help in the development of new drugs. In this paper, we present a novel network-based approach to predict module biomarkers of gastric cancer that can effectively distinguish the disease from normal samples. Specifically, by assuming that gastric cancer has mainly resulted from dysfunction of biomolecular networks rather than individual genes in an organism, the genes in the module biomarkers are potentially related to gastric cancer. Finally, we identified a module biomarker with 27 genes, and by comparing the module biomarker with known gastric cancer biomarkers, we found that our module biomarker exhibited a greater ability to diagnose the samples with gastric cancer.
authors.affiliation: College of Statistics and Applied Mathematics, Anhui University of Finance and Economics, Bengbu, Anhui Province, People's Republic of China; Key Laboratory of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, People's Republic of China; Collaborative Research Center for Innovative Mathematical Modeling, Institute of Industrial Science, University of Tokyo, Tokyo, Japan.
authors.forename: Xiaoping
authors.initials: X
authors.lastname: Liu
authors.affiliation: College of Statistics and Applied Mathematics, Anhui University of Finance and Economics, Bengbu, Anhui Province, People's Republic of China; Collaborative Research Center for Innovative Mathematical Modeling, Institute of Industrial Science, University of Tokyo, Tokyo, Japan.
authors.forename: Xiao
authors.initials: X
authors.lastname: Chang
keywords: biomarkers; differential network; gastric cancer; stomach cancer