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Identifying module biomarkers from gastric cancer by differential correlation network

Authors Liu X, Chang X

Received 21 May 2016

Accepted for publication 30 July 2016

Published 19 September 2016 Volume 2016:9 Pages 5701—5711

DOI https://doi.org/10.2147/OTT.S113281

Checked for plagiarism Yes

Review by Single-blind

Peer reviewers approved by Dr Akshita Wason

Peer reviewer comments 4

Editor who approved publication: Dr William Cho

Xiaoping Liu,1–3,* Xiao Chang1,3,*

1College of Statistics and Applied Mathematics, Anhui University of Finance and Economics, Bengbu, Anhui Province, People’s Republic of China; 2Key Laboratory of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, People’s Republic of China; 3Collaborative Research Center for Innovative Mathematical Modeling, Institute of Industrial Science, University of Tokyo, Tokyo, Japan

*These authors contributed equally to this work

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.

Keywords: biomarkers, gastric cancer, stomach cancer, differential network

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