Complex Systems and Bioinformatics Lab, Hanoi University of Industry, Hanoi, Viet Nam.
School of IT Convergence, University of Ulsan, Ulsan, Republic of Korea.
PLoS One. 2018 Jun 18;13(6):e0199109. doi: 10.1371/journal.pone.0199109. eCollection 2018.
Specific molecular signaling networks underlie different cancer types and quantitative analyses on those cancer networks can provide useful information about cancer treatments. Their structural metrics can reveal survivability of cancer patients and be used to identify biomarker genes for early cancer detection. In this study, we devised a novel structural metric called hierarchical closeness (HC) entropy and found that it was negatively correlated with 5-year survival rates. We also made an interesting observation that a network of higher HC entropy was likely to be more robust against mutations. This finding suggested that cancers of high HC entropy tend to be incurable because their signaling networks are robust to perturbations caused by treatment. We also proposed a novel core identification method based on the reachability factor in the HC measure. The cores were permitted to decompose such that the negative relationship between HC entropy and cancer survival rate was consistently conserved in every core level. Interestingly, we observed that many promising biomarker genes for early cancer detection reside in the innermost core of a signaling network. Taken together, the proposed analyses of the hierarchical structure of cancer signaling networks may be useful in developing future novel cancer treatments.
特定的分子信号网络是不同癌症类型的基础，对这些癌症网络进行定量分析可以提供有关癌症治疗的有用信息。它们的结构指标可以揭示癌症患者的生存能力，并可用于识别用于早期癌症检测的生物标志物基因。在这项研究中，我们设计了一种新的结构指标，称为层次接近度（HC）熵，发现它与 5 年生存率呈负相关。我们还观察到一个有趣的现象，即具有更高 HC 熵的网络更有可能抵抗突变。这一发现表明，具有高 HC 熵的癌症往往无法治愈，因为它们的信号网络对治疗引起的干扰具有很强的鲁棒性。我们还提出了一种基于 HC 测度中可达性因子的新核心识别方法。允许核心分解，使得 HC 熵与癌症存活率之间的负相关关系在每个核心级别中都得到一致的保持。有趣的是，我们观察到许多用于早期癌症检测的有前途的生物标志物基因存在于信号网络的最内层核心中。总之，对癌症信号网络层次结构的分析可能有助于开发未来新的癌症治疗方法。
