Detection for disease tipping points by landscape dynamic network biomarkers

作     者：Xiaoping Liu Xiao Chang Siyang Leng Hui Tang Kazuyuki Aihara Luonan Chen Xiaoping Liu;Xiao Chang;Siyang Leng;Hui Tang;Kazuyuki Aihara;Luonan Chen

作者机构：Key Laboratory of Systems Biology Center for Excellence in Molecular Cell Science Institute of Biochemistry and Cell Biology Shanghai Institutes for Biological Sciences Chinese Academy of Sciences School of Mathematics and Statistics Shandong University at Weihai Institute of Industrial Science the University of Tokyo Institute of Statistics and Applied Mathematics Anhui University of Finance & Economics School of Life Science and Technology Shanghai Tech University Center for Excellence in Animal Evolution and Genetics Research Center for Brain Science and Brain-Inspired Intelligence 

出 版 物：《National Science Review》 (国家科学评论(英文版))

年 卷 期：2019年第6卷第4期

页      面：775-785页

核心收录：

学科分类：1002[医学-临床医学] 07[理学] 070104[理学-应用数学] 100214[医学-肿瘤学] 0701[理学-数学] 10[医学] 

基　　金：supported by the National Key R&D Program of China(2017YFA0505500) the Strategic Priority Research Program of the Chinese Academy of Sciences(XDB13040700) the National Natural Science Foundation of China(61403363,91529303,31771476,81471047) the Key Project of Natural Science of Anhui Provincial Education Department(KJ2016A002) supported by JSPS KAKENHI(15H05707) JST CREST(JPMJCR14D2),Japan 

主　　题：single-sample network dynamic network biomarkers tipping points of complex disease 

摘      要：A new model-free method has been developed and termed the landscape dynamic network biomarker(l-DNB) methodology. The method is based on bifurcation theory, which can identify tipping points prior to serious disease deterioration using only single-sample omics data. Here, we show that l-DNB provides early-warning signals of disease deterioration on a single-sample basis and also detects critical genes or network biomarkers(i.e. DNB members) that promote the transition from normal to disease states. As a case study, l-DNB was used to predict severe influenza symptoms prior to the actual symptomatic appearance in influenza virus infections. The l-DNB approach was then also applied to three tumor disease datasets from the TCGA and was used to detect critical stages prior to tumor deterioration using an individual DNB for each patient. The individual DNBs were further used as individual biomarkers in the analysis of physiological data, which led to the identification of two biomarker types that were surprisingly effective in predicting the prognosis of tumors. The biomarkers can be considered as common biomarkers for cancer, wherein one indicates a poor prognosis and the other indicates a good prognosis.