肝脏炎癌恶性转化关键节点的动态网络标志物模型及临床应用研究

我国肝癌的发病常伴有“肝炎－肝硬化－肝癌”的慢性非可控性迁延不愈过程,患者出现临床症状时多已处于肝癌中晚期，错过了治疗的最佳时间。因此提高肝癌的早期诊断水平、寻找肝癌早期干预方案已经成为一个迫在眉睫的关键科学问题。本项目将由动力系统理论和生物实验相结合的系统生物学研究模式，以病理学明确的肝癌早期病变组织（高度不典型性增生结节）和慢性肝炎高危队列为主要切入点，建立肝炎向肝癌转化过程中临床样本的多层次组学大数据（如基因组、转录组等）整合模型，从系统的视角，研究非可控肝癌恶性转化过程中的调控网络，揭示恶性转化过程中活性调控网络的动态变化及疾病的分子机制，确定表征疾病进程的组织学和血清学动态网络标记物(DNB)，识别肝炎恶性转化过程的关键节点和关键网络，建立癌症前兆的“前疾病状态”预测和诊断方法，并由临床样本或生物实验验证基于DNB的理论预测结果。

It is a nonlinear and dynamical process from hepatitis to liver cancer involving a malignant transformation at a network level. This project aims to develop a mathematical model to analyze such a malignant transformation from hepatitis to liver cancer based on both theoretical analysis and clinical data, and further establish a method for early prediction of liver cancer. Specifically, (a) derive gene regulatory networks of hepatitis to liver cancer by integrating multi-level omics data; (b) identify dynamical network biomarkers to characterize the critical transition from inflammation to cancer, based on the observed data and on various conditions; (3) develop a prediction model to identify the early signal of the critical transition of the inflammation to liver cancer, and further validate the theoretical results with clinic data or animal models.