Detection for disease tipping points by landscape dynamic network biomarkers  

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

机构地区：[1]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,Shanghai 200031,China [2]School of Mathematics and Statistics,Shandong University at Weihai,Weihai 264209,China [3]Institute of Industrial Science,the University of Tokyo,Tokyo 153–8505,Japan [4]Institute of Statistics and Applied Mathematics,Anhui University of Finance&Economics,Bengbu 233030,China [5]School of Life Science and Technology,ShanghaiTech University,Shanghai 201210,China [6]Center for Excellence in Animal Evolution and Genetics,Kunming 650223,China [7]Research Center for Brain Science and Brain-Inspired Intelligence,Shanghai 201210,China

出　　处：《National Science Review》2019年第4期775-785,共11页国家科学评论（英文版）

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

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.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.

关 键 词：single-sample NETWORK dynamic NETWORK biomarkers TIPPING POINTS of complex DISEASE 

分 类 号：N[自然科学总论]