Exploring Drug Treatment Patterns Based on the Action of Drug and Multilayer Network Model
Exploring Drug Treatment Patterns Based on the Action of Drug and Multilayer Network Model
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基于药物作用和多层网络模型探索药物治疗模式
DOI：
10.3390/ijms21145014
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发表时间：
2020-07-01
影响因子：
5.6
通讯作者：
Gao，Lin
中科院分区：
文献类型：
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作者：
Yu，Liang;Shi，Yayong;Gao，Lin
一些药物可用于治疗多种疾病，这表明了药物治疗的潜在模式。确定药物治疗模式可以提高我们对药物作用机制的理解，使药物再利用成为可能。药物可以与用于治疗疾病的多层组织特异性蛋白质-蛋白质相互作用（TSPPI）网络相关联。蛋白质通常与其他蛋白质相互作用以实现引起疾病的功能。因此，研究药物治疗模式类似于研究多层TSPPI网络中的常见模块结构。因此，我们提出了一个基于网络的模型来研究药物的治疗模式。该方法被命名为SDTP（研究药物治疗模式），并基于药物效应和多层网络模型。为了证明SDTP方法的应用，我们重点分析了白血病、乳腺癌和前列腺癌中的阿司他汀A（TSA）。我们构建了一个TSPPI多层网络，并从网络中获得候选药物靶点模块。基因本体分析提供了深入了解药物靶模块和共表达网络的意义。最后，两个模块得到的TSA的潜在治疗模式。通过分析所选药物-靶点模块的意义、组成和功能，验证了SDTP方法识别药物治疗模式的可行性和合理性。总之，我们的新方法使用多层网络模型来克服单层网络的缺点，并将网络与药物活性信息相结合。根据发现的药物治疗模式，我们可以预测该药物可以治疗的潜在疾病。也就是说，如果一个疾病相关的蛋白质模块具有相似的结构，那么该药物很可能是治疗该疾病的潜在药物。
Some drugs can be used to treat multiple diseases, suggesting potential patterns in drug treatment. Determination of drug treatment patterns can improve our understanding of the mechanisms of drug action, enabling drug repurposing. A drug can be associated with a multilayer tissue-specific protein–protein interaction (TSPPI) network for the diseases it is used to treat. Proteins usually interact with other proteins to achieve functions that cause diseases. Hence, studying drug treatment patterns is similar to studying common module structures in multilayer TSPPI networks. Therefore, we propose a network-based model to study the treatment patterns of drugs. The method was designated SDTP (studying drug treatment pattern) and was based on drug effects and a multilayer network model. To demonstrate the application of the SDTP method, we focused on analysis of trichostatin A (TSA) in leukemia, breast cancer, and prostate cancer. We constructed a TSPPI multilayer network and obtained candidate drug-target modules from the network. Gene ontology analysis provided insights into the significance of the drug-target modules and co-expression networks. Finally, two modules were obtained as potential treatment patterns for TSA. Through analysis of the significance, composition, and functions of the selected drug-target modules, we validated the feasibility and rationality of our proposed SDTP method for identifying drug treatment patterns. In summary, our novel approach used a multilayer network model to overcome the shortcomings of single-layer networks and combined the network with information on drug activity. Based on the discovered drug treatment patterns, we can predict the potential diseases that the drug can treat. That is, if a disease-related protein module has a similar structure, then the drug is likely to be a potential drug for the treatment of the disease.