Detection for disease tipping points by landscape dynamic network biomarkers

Abstract

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.

Personalized Early-Warning Signals during Progression of Human Coronary Atherosclerosis by Landscape Dynamic Network Biomarker

“…The l-DNB method we used for detecting the individual early-warning signals in complex disease was reported previously [ 14 ] with slight modification in this study.…”

“…In addition, it is not trivial to computationally determine DNB members and DNB module size. To solve those problems, the landscape DNB (l-DNB) method was developed to identify the tipping point of diseases from a single sample [ 14 ]. Based on the three criteria of traditional DNB theory and single-sample network, the l-DNB method can evaluate the local DNB score for molecule by molecule in a sample, and then compile all of the local DNB scores into a landscape of this sample.…”

Critical transitions and tipping points in EMT

“…DNB is actually a group of molecules with strong fluctuations and also high correlation, and can signal the tipping point even though there are no significant differences between E-state and critical state in terms of gene/protein expressions [2,3,[27][28][29][41][42][43][44]. Since DNB theory is based mainly on the second order statistics (such as deviation and correlation) rather than the traditional firstorder statistics (such as average values), we can even detect the "dark genes" in terms of gene expression, which have no differential expression changes during the EMT but play important roles from the perspectives of network [2,3,[41][42][43][44]. DNB theory has been applied to various areas by many researchers, including successfully identifying the tipping points of cell fate decision [45], studying immune checkpoint blockade [46], and obtaining the early-warning signals of cancer metastasis [2,3,[41][42][43][44] and detecting two tipping points of type-2 diabetes [47].…”

“…Since DNB theory is based mainly on the second order statistics (such as deviation and correlation) rather than the traditional firstorder statistics (such as average values), we can even detect the "dark genes" in terms of gene expression, which have no differential expression changes during the EMT but play important roles from the perspectives of network [2,3,[41][42][43][44]. DNB theory has been applied to various areas by many researchers, including successfully identifying the tipping points of cell fate decision [45], studying immune checkpoint blockade [46], and obtaining the early-warning signals of cancer metastasis [2,3,[41][42][43][44] and detecting two tipping points of type-2 diabetes [47]. EMT is a typical phase transition process from E-state to M-state, and its bi-stability or multistability feature implies that there are tipping points just before the critical transitions, which play key roles for the phase transition.…”

Gene expression analysis reveals the tipping points during infant brain development for human and chimpanzee

“…where PCC d is the average Pearson's correlation coefficient (PCC) between the genes in the dominant group of the same time period in absolute value; PCC o is the average PCC between the dominant group and others of the same time period in absolute value; SD d is the average standard deviations (SD) of the genes in the dominant group. These three criteria together construct the composite index (CI) [14,15,[24][25][26][27][28][46][47][48][49][50][51]. The CI is expected to reach the peak or increase sharply during the measured periods when the system approaches the tipping point, thus indicating the imminent transition.…”