Quantifying critical states of complex diseases using single-sample dynamic network biomarkers.

Luonan Chen,Kazuyuki Aihara,Xiao Chang,Rui Liu,Xiaoping Liu,Xiangtian Yu,Jason A Papin

doi:10.1371/journal.pcbi.1005633

Abstract

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.

Highlights

Biomarkers, which are indicators of physiological states for living things, are commonly used to examine organ functions or disease states in biology or medicine
The pre-disease state is usually considered to be reversible to a normal state if appropriately treated [1, 2], in contrast to disease states such as cancer and diabetes that are generally difficult to return to the normal state
dynamic network biomarker (DNB) theory suggests that a molecular module or DNB will appear at the critical state, and that this can be taken as an early-warning signal during the disease progression from normal to disease onset [1,2,3]

Summary

Introduction

Biomarkers, which are indicators of physiological states for living things, are commonly used to examine organ functions or disease states in biology or medicine. Identifying the predisease state, or the early-warning signals of the disease state, is an important challenge in medicine, and is beneficial for the early diagnosis and treatment of complex diseases and provides dynamical insights into the molecular mechanism of complex diseases at a network level To tackle this problem, the new concept of dynamic network biomarker (DNB) with its three statistical conditions was proposed to detect early-warning signals before disease onset at the molecular network level, and was applied to the analyses on various diseases [1,2,3]. DNB theory suggests that a molecular module or DNB will appear at the critical state (the pre-disease state or tipping point), and that this can be taken as an early-warning signal during the disease progression from normal to disease onset [1,2,3]. We can theoretically prove that when a biological system from a normal state approaches the critical state, a DNB module or a group of molecules (or variables) appear and satisfy the following three statistic conditions [1,2,3]:

Methods

Results

Discussion

Conclusion