近日，国际学术期刊National Science Review在线发表了中国科学院生物化学与细胞生物学研究所陈洛南研究组题为“Detection for disease tipping points by landscape dynamic network biomarkers”的最新研究成果。该成果首次建立单样本“landscape”动态网络标志物(DNB: dynamic network biomarker)理论和方法，实现基于单个样本数据可检测生物动态过程/疾病过程的临界状态及其关键分子。该理论结合单样本网络构建方法和动态网络标志物理论，提出针对单样本“landscape”的动态网络标志物检测方法，并利用该方法对癌症数据进行了分析，不仅成功地检测到疾病临界状态而且得到了在三种癌症的DNB和“预后生物标志物”。
在复杂疾病的研究中，疾病的前期预警或恶化的早期预警信号是复杂疾病预防和治疗重要的诊断指标，如果能够成功的量化疾病临界状态或捕获疾病的早期预警信号，对复杂疾病的预防和治疗有着深远意义。
基于这个问题，研究人员利用动态网络标志物的概念并结合单样本网络构建方法，提出了在单样本水平上，对每个基因/分子进行进行系统性甄别的“landscape”方法。该方法可以对单次采样的表达谱数据进行分析，有效地找到样本特异性的动态网络标志物，并检测其生物动态过程的临界状态或疾病的早期预警信号。通过对癌症数据的分析，研究人员利用该方法成功找到了LUAD、THCA和KIRC三种癌症的DNB和“预后生物标志物”。该成果也可应用于进化等复杂非线性生物过程的研究。
该研究得到了科技部、中国科学院和国家自然科学基金的经费支持。
(A) Schematic diagram for disease progression of a complex disease in a subject. There are three states during disease progression comprising a normal state, a critical state (or pre-disease state or tipping point), and the final disease state. Generally, the phenotypic and molecular expressions of the disease state are significantly different from those of the normal state, but there is no significant differences observed between the critical and the normal states. Thus, detection of the critical state is difficult. However, there are strong collective fluctuations in processes at the critical state, which differs from the other states. (B) The l-DNB flowchart for identifying DNBs from a single sample. The individual data of sample d is used to construct the SSN for sample d. For every gene x, its local module is comprised of gene x and its first-order neighbors. The local module score for gene x can indicate the local DNB score for the gene. After ranking the scores for all genes, the top-k genes can be regarded as the potential DNB for the sample d. The sEDn, sPCCin and sPCCout values for each individual sample are defined in the Methods.
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