Genome-scale metabolic models have been proven to be valuable for defining cancer or to indicate the severity of cancer. However, identifying effective metabolic drug target (DT) of the active small-molecule compound is difficult to unravel and needs to be investigated. In this study, we identify effective DT for breast cancer using proposed network analysis of enzyme-centric network in the metabolic model. Our network-based analysis revealed that high degree nodes (HDNs) of enzymes are key to progression/development of cancer. These HDNs show high interconnections inside the network. It has been found that these HDNs are crucial driver nodes for effectively targeting in breast cancer metabolic network. Furthermore, based on the correlation and principal component analysis, we have shown that certain proteins play a significant role in the network and can be used as an effective DT in cancer therapeutics. In addition, these proteins stimulate the active site of enzymes to activate the target metabolites. Overall, we have shown that a better understanding of the metabolic networks using statistical model could be valuable in DT identification for developing effective therapeutic approaches and personalized medicine.

中文翻译：

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乳腺癌代谢网络中药物靶点的鉴定。

基因组规模的代谢模型已被证明对于定义癌症或表明癌症的严重程度很有价值。然而，确定活性小分子化合物的有效代谢药物靶点 (DT) 很难解开，需要进行研究。在这项研究中，我们使用代谢模型中以酶为中心的网络的拟议网络分析来确定乳腺癌的有效 DT。我们基于网络的分析表明，酶的高度节点 (HDN) 是癌症进展/发展的关键。这些 HDN 显示出网络内部的高度互连。已经发现，这些 HDN 是有效靶向乳腺癌代谢网络的关键驱动节点。此外，基于相关性和主成分分析，我们已经证明某些蛋白质在网络中发挥着重要作用，可以用作癌症治疗中的有效 DT。此外，这些蛋白质刺激酶的活性部位以激活目标代谢物。总的来说，我们已经表明，使用统计模型更好地理解代谢网络对于开发有效的治疗方法和个性化医疗的 DT 识别可能很有价值。