Detecting disease genes of non-small lung cancer based on consistently differential interactions

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• 作者：Qianqian Shi ; Xiaoping Liu ; Tao Zeng ; William Wang…
• 关键词：Heterogeneous dataset ; Disease gene ; Lung cancer metastasis ; Differential interaction ; Edge biomarker ; Network biomarker
• 刊名：Cancer and Metastasis Reviews
• 出版年：2015
• 出版时间：June 2015
• 年：2015
• 卷：34
• 期：2
• 页码：195-208
• 全文大小：1,736 KB
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• 作者单位：Qianqian Shi (1)
  Xiaoping Liu (1)
  Tao Zeng (1)
  William Wang (2)
  Luonan Chen (1) (3)
  
  1. Key Laboratory of Systems Biology, Innovation Center for Cell Signaling Network, Institute of Biochemistry and Cell Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, 200031, China
  2. Department of Biomedical Science, University College of London, London, UK
  3. School of Life Science and Technology, ShanghaiTech University, Shanghai, 201210, China
  
• 刊物类别：Medicine
• 刊物主题：Medicine & Public Health
  Oncology
  Cancer Research
  
• 出版者：Springer Netherlands
• ISSN：1573-7233

文摘

Systematic identification of causal disease genes can shed light on the mechanisms underlying complex diseases and provide crucial information to develop efficient biomarkers or design suitable therapies. The present paper describes a novel approach to detect potential disease genes for lung cancer, based on consistently differential interaction (CDI) scheme from heterogeneous disease datasets. In particular, reliable disordered regulations in disease states were discovered by identifying the CDIs, from which the disease genes were further detected based on their topological structures on the network. As an application of the CDI-based method, the RNA-seq data of two subtypes of non-small lung cancer were used to identify CDIs from normal to cancer onset. The results of analysis well agree with the prior knowledge as well as the experiments, thereby implying the predictive power of the CDI-based method. The comparison with other approaches also indicated the superiority of the CDI-based method in terms of accuracy and effectiveness on detecting disease-specific genes for lung cancer and metastasis. In contrast to conventional molecular biomarkers, the identified CDIs as novel network biomarkers or edge biomarkers can be applied to predict patient survival for both subtypes of lung cancers, and the interactions among CDIs can be further used as new edgetic targets for network drug design. In addition, a potential molecular mechanism was developed to explain the key roles of the identified CDIs in lung cancer and metastasis from a network perspective.