基于癌和癌旁miRNA与mRNA并行表达谱数据的人肝癌复合表达调控网络构建、解析和关键模块与分子的识别

Hepatocellular carcinoma (HCC) has greatly threatened human health. Although having provided preliminary hints for its pathogenesis, classic experimental approaches investigating a handful of targets can't review the key regulatory mechanism of HCC in an overall perspective. Gene expression is regulated both in transcription level by transcriptional factors (TF) and in post-transcription level by miRNAs. As the most crucial regulators of gene expression, TF and miRNA act synergistically on determining the cellular landscape and disease development. Therefore, an ideal way to study HCC at the level of molecular system biology is to construct and resolve the combinatorial gene regulatory network composed of TF regulations and miRNA regualtions in HCC. In this project, we first apply Illumina massively parallel signature sequencing to carry out an in-depth analysis of trascriptomes and miRNomes in human HCC and nontumor tissues. Secondly, based on these parallel expression data, we adopt a linear regression model to integrate seed-matching information and expression data and hence construct a comprehensive, precise combinatorial gene regulatory network in HCC. Thirdly, we perform differential coexpression/differential networking analysis to resolve the differences of the two combinatorial regulatory networks, the HCC-specific and the nontumor-specific, and as a result reveal key TF and miRNA as well as the regulation loops related to HCC. Finally, classic experimental biology techniques are used to verify the analysis results and hypotheses, and also identify potential diagnostic and therapeutic targets for HCC. Our results will be in favor of a better understanding of tumorigenesis in HCC and serve as theoretical bases facilitating the identification of clinical targets of HCC.

原发性肝细胞癌(HCC)严重危害人类健康。目前，基于少数目标分子的经典实验研究为认识其发病机理提供了部分依据，但无法从整体高度全面探究其关键调控机制。作为胞内关键的表达调控因子，转录因子(TF)和miRNA的协同调控在很大程度上决定了细胞面貌和疾病发生发展，因此构建和解析由TF调控和miRNA调控组成的复合表达调控网络是在分子系统生物学层面上研究HCC的极佳途径。本课题首先通过二代测序技术获得人HCC及匹配癌旁组织的mRNA及miRNA并行表达谱数据，用整合序列互补信息和表达谱信息的线性回归模型构建完整可靠的人HCC的复合表达调控网络，然后运用差异共表达分析技术对比癌/癌旁两种表型状态的复合表达调控网络，解析HCC相关的关键TF、miRNA及其所组成的调控回路，最后运用靶向性实验生物学技术验证分析结果和假设。本研究将有助于深入理解HCC的发生发展机制，为有效诊疗靶点的识别提供理论支持。

本项目通过二代测序技术获得人HCC及匹配癌旁组织的mRNA及miRNA并行表达谱数据，用整合序列互补信息和表达谱信息的线性回归模型构建了完整可靠的人HCC的复合表达调控网络，利用自主开发的DCGLv2软件包，共筛选出82个差异表达的miRNA和512个差异表达的mRNA，以及1040个差异共表达基因和75个成熟的差异共表达miRNA；经过对比，确定了normal-cancer状态切换特异的基因功能网络和全过程动态功能模块，解析HCC相关的关键TF、miRNA及其所组成的调控回路，最后运用靶向性实验生物学技术验证分析结果和假设。发现肝脏维甲酸诱导基因I（RIG-I）是判断肝癌患者预后和肝癌干扰素治疗的新的分子标志物，RIG-I能够通过抑制SHP1对STAT1活化的抑制作用进而维持了干扰素下游信号的活化，从而增强了干扰素的信号通路和进而促进其诱导肝癌细胞凋亡的效应。本研究将有助于深入理解HCC的发生发展机制，为有效诊疗靶点的识别提供理论支持。

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