Quantifying critical states of complex diseases using single-sample dynamic network biomarkers.
Quantifying critical states of complex diseases using single-sample dynamic network biomarkers.
复制标题
使用单样本动态网络生物标志物量化复杂疾病的关键状态
DOI：
10.1371/journal.pcbi.1005633
复制
发表时间：
2017-07
影响因子：
4.3
通讯作者：
Aihara K
中科院分区：
文献类型：
--
作者：
Liu X;Chang X;Liu R;Yu X;Chen L;Aihara K
动态网络生物标志物（DNB）可以识别疾病的临界状态或临界点，从而预测而不是诊断疾病。然而，由于在临界状态下评估DNB需要每个个体的多个样本的数据，而这些数据通常是不可用的，因此DNB理论难以应用于临床实践，从而限制了DNB的适用性。在这项研究中，我们开发了一种新的方法，即，单样本DNB（sDNB），仅用单个样本检测个体患者的疾病预警信号或临界状态，从而开辟了以个性化方式预测疾病的新途径。与传统生物标志物中用于“诊断疾病”的差异表达的信息相反，sDNB基于差异关联的信息，从而具有“预测疾病”或“诊断近期疾病”的能力。将该方法应用于流感病毒感染和癌症转移的数据集，可以基于单个样本准确识别临界状态或正确预测即时疾病。我们成功地确定了流感病毒感染的疾病症状出现之前的临界状态或临界点，以及癌症患者远处转移的发生，从而证明了我们的方法在单样本水平上量化临界状态的有效性和效率。
Dynamic network biomarkers (DNB) can identify the critical state or tipping point of a disease, thereby predicting rather than diagnosing the disease. However, it is difficult to apply the DNB theory to clinical practice because evaluating DNB at the critical state required the data of multiple samples on each individual, which are generally not available, and thus limit the applicability of DNB. In this study, we developed a novel method, i.e., single-sample DNB (sDNB), to detect early-warning signals or critical states of diseases in individual patients with only a single sample for each patient, thus opening a new way to predict diseases in a personalized way. In contrast to the information of differential expressions used in traditional biomarkers to “diagnose disease”, sDNB is based on the information of differential associations, thereby having the ability to “predict disease” or “diagnose near-future disease”. Applying this method to datasets for influenza virus infection and cancer metastasis led to accurate identification of the critical states or correct prediction of the immediate diseases based on individual samples. We successfully identified the critical states or tipping points just before the appearance of disease symptoms for influenza virus infection and the onset of distant metastasis for individual patients with cancer, thereby demonstrating the effectiveness and efficiency of our method for quantifying critical states at the single-sample level.
登录
查看更多内容
影响因子：
3.6
作者：
Jerhammar, Fredrik;Ceder, Rebecca;Roberg, Karin
通讯作者：
Roberg, Karin
影响因子：
4
作者：
Jang, Jae-Hwi;Baerts, Lesley;Jungraithmayr, Wolfgang
通讯作者：
Jungraithmayr, Wolfgang
影响因子：
3.7
作者：
Inoue, Yasuhiro;Yokobori, Takehiko;Kusunoki, Masato
通讯作者：
Kusunoki, Masato
DOI：
10.1158/1078-0432.ccr-15-2071
发表时间：
2016-03-01
期刊：
Clinical cancer research : an official journal of the American Association for Cancer Research
影响因子：
--
作者：
Field MG;Decatur CL;Kurtenbach S;Gezgin G;van der Velden PA;Jager MJ;Kozak KN;Harbour JW
通讯作者：
Harbour JW
影响因子：
9
作者：
通讯作者：
--