通过差异相关网络从胃癌中识别模块生物标志物。
Identifying module biomarkers from gastric cancer by differential correlation network.
作者信息
Liu Xiaoping, Chang Xiao
机构信息
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.
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.
出版信息
Onco Targets Ther. 2016 Sep 19;9:5701-5711. doi: 10.2147/OTT.S113281. eCollection 2016.
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.
胃癌是一种严重的疾病，它由许多功能相关基因或信号通路的失调引起，而非单个基因突变所致。系统地鉴定胃癌生物标志物能够为这种致命疾病的潜在机制提供见解，并有助于新药研发。在本文中，我们提出了一种基于网络的全新方法来预测胃癌的模块生物标志物，该方法能够有效区分胃癌样本与正常样本。具体而言，通过假设胃癌主要源于生物分子网络功能失调而非生物体中的单个基因，模块生物标志物中的基因可能与胃癌相关。最终，我们鉴定出了一个包含27个基因的模块生物标志物，通过将该模块生物标志物与已知的胃癌生物标志物进行比较，我们发现我们的模块生物标志物在诊断胃癌样本方面表现出更强的能力。
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