DataSheet_5_Identifying Critical States of Complex Diseases by Single-Sample Jensen-Shannon Divergence.xlsx

MotivationThe evolution of complex diseases can be modeled as a time-dependent nonlinear dynamic system, and its progression can be divided into three states, i.e., the normal state, the pre-disease state and the disease state. The sudden deterioration of the disease can be regarded as the state transition of the dynamic system at the critical state or pre-disease state. How to detect the critical state of an individual before the disease state based on single-sample data has attracted many researchers’ attention.
MethodsIn this study, we proposed a novel approach, i.e., single-sample-based Jensen-Shannon Divergence (sJSD) method to detect the early-warning signals of complex diseases before critical transitions based on individual single-sample data. The method aims to construct score index based on sJSD, namely, inconsistency index (ICI).
ResultsThis method is applied to five real datasets, including prostate cancer, bladder urothelial carcinoma, influenza virus infection, cervical squamous cell carcinoma and endocervical adenocarcinoma and pancreatic adenocarcinoma. The critical states of 5 datasets with their corresponding sJSD signal biomarkers are successfully identified to diagnose and predict each individual sample, and some “dark genes” that without differential expressions but are sensitive to ICI score were revealed. This method is a data-driven and model-free method, which can be applied to not only disease prediction on individuals but also targeted drug design of each disease. At the same time, the identification of sJSD signal biomarkers is also of great significance for studying the molecular mechanism of disease progression from a dynamic perspective.

研究背景：复杂疾病的演化可被建模为时变非线性动态系统，其疾病进展可划分为正常状态、疾病前状态与疾病状态三类。疾病的突发恶化可视为该动态系统在临界状态或疾病前状态下发生的状态跃迁。如何基于单样本数据，在个体进入疾病状态前检测其临界状态，已吸引了众多研究者的关注。
研究方法：本研究提出了一种新颖的方法，即基于单样本的詹森-香农散度（single-sample-based Jensen-Shannon Divergence, sJSD）法，用于基于个体单样本数据检测复杂疾病临界转变前的早期预警信号。该方法旨在构建基于sJSD的评分指标，即不一致性指数（inconsistency index, ICI）。
研究结果：本方法被应用于五组真实数据集，包括前列腺癌、膀胱尿路上皮癌、流感病毒感染、宫颈鳞状细胞癌与宫颈腺癌，以及胰腺腺癌。研究成功识别出五组数据集的临界状态及其对应的sJSD信号生物标志物，可用于每位个体样本的诊断与预测，并揭示了一批无差异表达但对ICI评分敏感的暗基因（dark genes）。本方法属于数据驱动且无模型依赖的方法，不仅可应用于个体的疾病预测，还可用于各疾病的靶向药物设计。同时，sJSD信号生物标志物的识别对于从动态视角研究疾病进展的分子机制亦具有重要意义。