恶性肿瘤关键突变介导的circRNA-lncRNA-miRNA多层次调控网络扰动模型构建及分析研究

Cancer is fundamentally a genetic disease that alters cellular pathways to promote tumor growth. However, recurring molecular mechanisms for noncoding RNA (ncRNAs) are now being realized and emerging technologies are expanding investigators abilities to functionally annotate cancer-associated ncRNAs. Moreover, genome-wide cancer mutation analyses are revealing an extensive landscape of functional mutations within the noncoding genome, with profound effects on the expression of ncRNAs, such as circular RNAs (circRNAs), long noncoding RNAs (lncRNAs) and microRNA (miRNAs). Understanding how these mutations perturb ncRNA interactions with other molecules is critical to therapeutic intervention in the fight against cancer. In this project, we aim to investigate the mutation perturbed circRNA-lncRNA-miRNA functional networks across cancer types for identifying the candidate driver mutations. Specifically, we will focus on the following aspects: (I) Developing a pipeline to construct the mutation and transcriptome profiles of circRNAs, lncRNAs and miRNAs for various types of cancer based on high-throughput sequencing data; (II) Construction of mutation perturbed circRNA-lncRNA-miRNA functional networks in cancer based on integration of multiple omics datasets; (III) Propose several machine-learning methods to identify the candidate driver mutations as well circRNAs/lncRNAs/miRNAs based on features summarized from perturbed networks, including nodes and edgetic ones; (IV) Prediction of the potential functions of the mutations and the ncRNAs based on perturbed networks and further validate the predictions based on low-throughput molecular experiments. Applied the proposed methods to various types of cancer, we will reveal cancer-specific as well as the recurrent mutations, circRNA/lncRNA/miRNAs. Based on the difference among the perturbed networks, it is better to dissect the molecular heterogeneity underlying phenotypes. Finally, all the datasets, computational methods and the candidate biomarkers will be integrated into a user-friendly website for investigating the functions of mutation and ncRNAs in cancer. This project will develop the computational pipelines and machine learning methods to identify the critical mutations and circRNAs/lncRNAs/miRNAs biomarkers that would provide a unique opportunity to design better therapeutic interventions in cancer.

恶性肿瘤的发生发展与ncRNA密切相关，研究突变扰动的circRNA-lncRNA-miRNA多层次调控模式是具有挑战意义的课题。本课题从网络系统生物学角度，通过整合突变组、转录组和调控组等完善circRNA/lncRNA/miRNA注释；结合靶向调控、竞争调控和协同调控特点，构建癌症中突变介导的circRNA/lncRNA/miRNA多层次调控网络扰动模型；提出综合网络节点和边的拓扑测度，建立风险标记预测模型，优化癌症关键突变及circRNA/lncRNA/miRNA；结合网络功能富集和生物实验方法解析其生物功能；建立突变扰动的circRNA-lncRNA-miRNA多层次调控网络构建及分析的生物信息学平台。旨在实现挖掘癌症发生发展过程中关键突变和ncRNA多层次调控模块，应用于多种癌症，剖析癌症发生的共性和异质性，推进人类复杂疾病多ncRNA研究、对探讨癌症发生发展的机理具有重大意义。

恶性肿瘤的发生发展与ncRNA密切相关，研究癌症背景特异的ncRNA多层次调控网络及突变的扰动模式极具挑战意义。本课题从网络系统生物学角度，提出了突变扰动产生ncRNA获得性调控的新视角；开发了调控网络扰动模型的计算方法；准确预测除公认的编码区结构域外其他功能区域上的癌症驱动突变；位点扰动ncRNA结构和调控的在线分析平台PRES；系统识别癌症中调控免疫通路的关键lncRNA，并揭示lncRNA对免疫通路的协同调控作用；系统分析了lncRNA空间表达模式，并探讨m6A修饰对组织特异表达的贡献及m6A修饰子在癌症中的重要作用；开发了系统识别ncRNA参与的ceRNA互作的软件包CeRNASeek；证实了癌症lncRNA标记物MiR22HG，揭示其可作为支架参与蛋白互作，参与癌症发生和免疫治疗；另外，我们拓展地开发了增强子RNA互作及增强子lncRNA识别的计算模型；进一步识别了癌症免疫的多种调控因子，包括miRNA等；揭示lncRNA可翻译成多肽及成为潜在免疫新抗原，并综述了非编码区产生免疫抗原的机制。本项目发表相关SCI论文18篇，ESI高被引和封面论文各1篇；获黑龙江省高校科学技术一等奖；在国内学术会议做专题报告7次，参与举办国内学术会议2次，培养/协助培养研究生12名，其中博士毕业生4名。负责人入选国家级青年人才、全球前2%顶尖科学家榜单，成为省生物医学工程学科后备带头人并获资助，新中标国自然面上、省杰出青年和校少帅揭榜项目各1项，参编研究生教材和英文专著各1部、正参与编写国家本科规划教材和作为主编编写配套习题集。以第一负责人获中省教学成果二等奖，以第二完成人获中省教学成果一等奖并已申报国家教学成果奖。