基于mRNA/miRNA表达谱的复合基因调控网络构建分析以识别胃的炎-癌转化关键分子模块

Gastric cancer is the second leading cause of cancer-related deaths worldwide, and approximately 40% of all cases occur in China. It is one of the key public health issues in cancer prevention and control because the onset ages have the tendency of getting increasingly younger in recent years. Epidemiological studies provide associations between chronic gastric inflammation and gastric cancer; however, classical experimental approaches investigating a handful of targets cannot elucidate its key regulatory mechanism 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, TFs and miRNAs act synergistically on determining the cellular landscape and disease development. In this project, we first apply next-generation sequencing technology to obtain the mRNA trascriptomes and miRNomes in human intestinal metaplasia (IM) tissues and gastric cancer tissues. Secondly, based on these parallel expression data, we perform differential coexpression analysis (DCEA) to identify the diffentially coexpressed genes and gene pairs, which are used as seeds to construct IM-specific and cancer-specific combinatorial gene regulatory networks through a linear regression model that integrates seed-matching information and expression data. Thirdly, through differential network analysis, we may reveal the differential regulators, regulation loops and modules. Finally, classical experimental techniques are used to verify the analysis results, and identify key factors and the signaling pathway, i.e., key molecular modules relevant to the progression from gastric inflammation to cancer. Our results will be in favor of a better understanding of tumorigenesis in gastric cancer, especially progression from inflammation to cancer, and serve as theoretical bases facilitating the identification ofdiagnostic and therapeutic targets of gastric cancer.

胃癌的死亡率在各类肿瘤中排名第二。我国胃癌高发，且近年来呈发病年轻化趋势。流行病学证据支持慢性胃炎发展为胃癌，但目前的发病机理研究仅限于针对少数目标分子的经典分子生物学研究，无法从全局的角度探究其关键调控机制。由于转录因子(TF)和miRNA的协同调控很大程度上决定了细胞的发育分化和疾病的发生发展，因此本课题首先通过二代测序技术获得人胃上皮肠化生组织和胃癌组织的mRNA及miRNA并行表达谱数据，采用差异共表达分析技术获得差异共表达基因/基因对，随后以差异共表达基因/基因对为种子，用整合序列互补信息和表达谱数据的线性回归模型分别构建炎症特异和胃癌特异的复合基因调控网络，通过网络的差异分析，识别差异调控因子、调控回路和调控模块，最后运用靶向性实验生物学技术进行验证和筛选，揭示炎-癌转化相关的关键分子模块，为深入理解慢性胃炎向胃癌转化的基因转录调控机制、识别有效诊疗靶点提供理论与实验支持。

胃癌的死亡率在各类肿瘤中排名第二。我国胃癌高发，且近年来呈发病年轻化趋势。近年来，感染、慢性炎症等因素与肿瘤之间的关系在多个肿瘤类型中被关注，然而目前胃癌发生发展的机制研究多局限于针对少数目标分子的经典分子生物学研究，难以从全局的角度探究其关键调控机制。本课题首先研发了复合基因调控网络构建及差异分析技术，分别在网络整体拓扑层次和单个调控关系层次上建立了对应于不同条件的基因调控网络之间的差异分析方法；随后，采用正反向工程结合的策略构建了阶段特异的包含TF和miRNA多种调控因子的胃癌复合基因调控网络，通过整合差异调控、差异表达及调控子调控效应贡献度三方面的信息，有效地识别到胃癌发生发展、炎癌转化相关的差异调控因子、差异调控关系及其关键调控模块；在此基础上提出了围绕TCF7L1、TCF4、MEIS1、ARGLU1、CREB1、TCEAL2、MBNL1、BPTF、GATA6、CEBPA等一系列主效调控因子的分子机制假说；进而，运用靶向生物学技术，开展了分子、细胞和动物水平的实验验证和筛选，并以临床标本检验其临床病理学意义，得到ARGLU1、TCEAL2、MBNL1等有希望的诊疗靶标。.本项目整合计算生物学与实验生物学的分子网络研究，以期深入理解胃癌发生发展的基因转录调控机制，为胃癌的防治提供有效的诊疗靶点。项目所生成的异常调控分析方法、机制解释性标志物识别方法、基于PDX模型的药效学分析工具等，以及干湿结合的技术框架具有良好的可拓展性，可广泛应用于其它肿瘤发生发展或表型变化过程的研究。