School of Computer Science and Engineering, South China University of Technology, Guangzhou, 510640, China.
School of Mathematics, South China University of Technology, Guangzhou, 510640, China.
J Transl Med. 2017 Oct 26;15(1):217. doi: 10.1186/s12967-017-1320-7.
The progression of complex diseases, such as diabetes and cancer, is generally a nonlinear process with three stages, i.e., normal state, pre-disease state, and disease state, where the pre-disease state is a critical state or tipping point immediately preceding the disease state. Traditional biomarkers aim to identify a disease state by exploiting the information of differential expressions for the observed molecules, but may fail to detect a pre-disease state because there are generally little significant differences between the normal and pre-disease states. Thus, it is challenging to signal the pre-disease state, which actually implies the disease prediction.
In this work, by exploiting the information of differential associations among the observed molecules between the normal and pre-disease states, we propose a temporal differential network based computational method to accurately signal the pre-disease state or predict the occurrence of severe disease. The theoretical foundation of this work is the quantification of the critical state using dynamical network biomarkers.
Considering that there is one stationary Markov process before reaching the tipping point, a novel index, inconsistency score (I-score), is proposed to quantitatively measure the change of the stationary processes from the normal state so as to detect the onset of pre-disease state. In other words, a drastic increase of I-score implies the high inconsistency with the preceding stable state and thus signals the upcoming critical transition. This approach is applied to the simulated and real datasets of three diseases, which demonstrates the effectiveness of our method for predicting the deterioration into disease states. Both functional analysis and pathway enrichment also validate the computational results from the perspectives of both molecules and networks.
At the molecular network level, this method provides a computational way of unravelling the underlying mechanism of the dynamical progression when a biological system is near the tipping point, and thus detecting the early-warning signal of the imminent critical transition, which may help to achieve timely intervention. Moreover, the rewiring of differential networks effectively extracts discriminatively interpretable features, and systematically demonstrates the dynamical change of a biological system.
复杂疾病（如糖尿病和癌症）的进展通常是一个非线性过程，具有三个阶段，即正常状态、疾病前状态和疾病状态，其中疾病前状态是疾病状态之前的关键状态或临界点。传统的生物标志物旨在利用观察到的分子的差异表达信息来识别疾病状态，但可能无法检测到疾病前状态，因为正常状态和疾病前状态之间通常没有明显的差异。因此，很难发出疾病前状态的信号，而实际上这意味着疾病的预测。
在这项工作中，我们利用正常状态和疾病前状态之间观察到的分子之间的差异关联信息，提出了一种基于时间差异网络的计算方法，以准确地发出疾病前状态的信号或预测严重疾病的发生。这项工作的理论基础是使用动态网络生物标志物来量化临界状态。
考虑到在到达临界点之前有一个稳定的马尔可夫过程，我们提出了一个新的指标，不一致性得分（I-score），来定量测量从正常状态到疾病前状态的稳定过程的变化，以检测疾病前状态的出现。换句话说，I-score 的急剧增加意味着与前面的稳定状态有很大的不一致，因此预示着即将发生的关键转变。该方法应用于三种疾病的模拟和真实数据集，证明了该方法预测疾病状态恶化的有效性。功能分析和途径富集也从分子和网络两个角度验证了计算结果。
在分子网络水平上，该方法提供了一种计算方法，可以揭示生物系统接近临界点时的动态进展的潜在机制，从而检测到即将发生的关键转变的预警信号，这可能有助于实现及时干预。此外，差异网络的重布线有效地提取了可区分的可解释特征，并系统地展示了生物系统的动态变化。
