Detecting disease genes of non-small lung cancer based on consistently differential interactions
Detecting disease genes of non-small lung cancer based on consistently differential interactions
复制标题
基于一致差异相互作用检测非小细胞肺癌的疾病基因
DOI：
10.1007/s10555-015-9561-5
复制
发表时间：
2015
影响因子：
9.2
通讯作者：
Chen Luonan
中科院分区：
文献类型：
--
作者：
Shi Qianqian;Liu Xiaoping;Zeng Tao;Wang William;Chen Luonan
致病基因的系统鉴定可以揭示复杂疾病的机制，并为开发有效的生物标志物或设计合适的治疗方法提供关键信息。本文描述了一种新的方法来检测潜在的肺癌疾病基因，基于一致性差异相互作用（CDI）计划从异质性疾病数据集。特别是，通过识别CDIs发现了疾病状态中可靠的无序调节，根据疾病基因在网络上的拓扑结构，进一步检测疾病基因。作为基于CDI的方法的应用，使用非小细胞肺癌的两种亚型的RNA-seq数据来鉴定从正常到癌症发作的CDI。分析结果与先验知识和实验结果吻合较好，从而表明了基于CDI的方法的预测能力。与其他方法的比较也表明了基于CDI的方法在检测肺癌和转移的疾病特异性基因的准确性和有效性方面的优越性。与传统的分子生物标志物相比，所鉴定的CDIs作为新的网络生物标志物或边缘生物标志物可以应用于预测两种肺癌亚型的患者生存，并且CDIs之间的相互作用可以进一步用作网络药物设计的新的边缘靶标。此外，一个潜在的分子机制被开发来解释的关键作用，确定CDIs在肺癌和转移从网络的角度来看。
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.