Institute of Systems Biology, Shanghai University, Shanghai, China.
J Am Med Inform Assoc. 2012 Mar-Apr;19(2):241-8. doi: 10.1136/amiajnl-2011-000658. Epub 2011 Dec 20.
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.
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.
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.
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.
复杂疾病通常是由多个基因的突变或多个生物过程的功能障碍引起的。系统地识别致病基因和模块生物标志物可以深入了解复杂疾病的机制，并有助于开发有效的治疗方法或有效的药物。
在本文中，我们提出了一种新的方法，基于对疾病和对照样本之间差异相互作用的分析，而不是广泛应用于现有方法的差异基因或蛋白质表达分析，来预测疾病基因和识别功能失调的网络或模块。
例如，我们将我们的方法应用于胃癌三阶段微阵列数据的研究。我们确定了包含一组与胃癌相关的基因的网络模块或模块生物标志物，这表明了我们方法的预测能力。在保留验证数据集上的结果表明，我们识别的模块可以作为一种有效的模块生物标志物，用于准确检测或诊断胃癌，从而验证了我们方法的效率。
我们提出了一种用于检测疾病模块生物标志物的新方法，胃癌的结果表明，差异相互作用有助于检测分子相互作用网络中的功能失调模块，而这些模块反过来又可以作为稳健的模块生物标志物。
