IEEE Trans Biomed Eng. 2019 Sep;66(9):2684-2692. doi: 10.1109/TBME.2019.2894980. Epub 2019 Jan 23.
Breast cancer is the second leading cause of cancer death among US women; hence, identifying potential drug targets is an ever increasing need. In this paper, we integrate existing biological information with graphical models to deduce the significant nodes in the breast cancer signaling pathway.
We make use of biological information from the literature to develop a Bayesian network. Using the relevant gene expression data we estimate the parameters of this network. Then, using a message passing algorithm, we infer the network. The inferred network is used to quantitatively rank different interventions for achieving a desired phenotypic outcome. The particular phenotype considered here is the induction of apoptosis.
Theoretical analysis pinpoints to the role of Cryptotanshinone, a compound found in traditional Chinese herbs, as a potent modulator for bringing about cell death in the treatment of cancer.
Using a mathematical framework, we showed that the combination therapy of mTOR and STAT3 genes yields the best apoptosis in breast cancer.
The computational results we arrived at are consistent with the experimental results that we obtained using Cryptotanshinone on MCF-7 breast cancer cell lines and also by the past results of others from the literature, thereby demonstrating the effectiveness of our model.
乳腺癌是美国女性癌症死亡的第二大主要原因；因此，确定潜在的药物靶点是日益增长的需求。在本文中，我们将现有的生物学信息与图形模型相结合，推导出乳腺癌信号通路中的重要节点。
我们利用文献中的生物学信息来开发贝叶斯网络。我们使用相关的基因表达数据来估计该网络的参数。然后，使用消息传递算法推断网络。推断出的网络用于定量评估实现所需表型结果的不同干预措施的效果。这里考虑的特定表型是诱导细胞凋亡。
理论分析指出 Cryptotanshinone（一种存在于传统中药中的化合物）作为一种有效的调节剂，可以在癌症治疗中诱导细胞死亡。
我们使用数学框架表明，mTOR 和 STAT3 基因的联合治疗在乳腺癌中产生最佳的细胞凋亡。
我们得出的计算结果与我们在 MCF-7 乳腺癌细胞系中使用 Cryptotanshinone 获得的实验结果以及文献中其他人的过去结果一致，从而证明了我们模型的有效性。
