基于多组学数据融合的泛癌中非编码RNA crosstalk模式研究

It has been known that the crosstalk (synergistic interactions, competitive interactions) between ncRNAs is complicated and closely associated with oncogenic initiation and progression, so it is a challenge to explore the composite interactions between ncRNAs in the various types of cancer. In the subject, we attempt to propose a series of algorithms to identify the composite synergistic interactions and competitive interactions between ncRNAs by integrating multidimensional high-throughput biological data, such as transcriptome, genome and epigenetics data, and then construct ncRNA crosstalk networks in each of 20 kinds of cancer and pan-cancer respectively. Further we investigate the ncRNA interactive mode in each type of cancer and pan-cancer, and the potential mechanisms and characteristics of the ncRNA interactions. Then, the specific topological features of known cancer-associated ncRNAs are dissected, and a new method is developed to predict candidate cancer-associated ncRNAs from the perspective of ncRNA interactions. The ultimate goals of our subject are to detect the similarities and differences of ncRNA interactions between different cancer types so as to preferably optimize ncRNAs and ncRNA interaction modules with the potential of diagnosis and prognosis, and finally establish a calculation and analysis platform with ncRNA interactions as the core. These results promote the research on ncRNAs as targets and biomarkers in precision medicine, and provide novel insights in understanding of oncogenic initiation and progression.

非编码RNA（ncRNA）间彼此的交互作用（协同交互、竞争交互）是复杂的并且与癌症的发生发展密切相关，研究多种癌症中ncRNA复合交互作用模式是具有挑战意义的课题。本课题从系统生物学角度出发，通过整合转录组、基因组和表观组等高通量数据，开发识别ncRNA协同交互与竞争交互的一系列算法，进而构建20种癌症及泛癌中的ncRNA crosstalk网络，进一步研究单个癌症中及泛癌中ncRNA交互模式、潜在机制和特征属性，总结已知癌相关ncRNA的拓扑性质，并开发从ncRNA交互角度预测候选癌ncRNA的新方法。旨在实现挖掘癌症发生发展过程中ncRNA作用机制的异同从而更好的优化具有诊断和预后潜能的ncRNA及ncRNA交互作用模块，并最终建立以ncRNA交互作用为核心的查询、计算及分析的一体化平台。本课题对推进ncRNA在精准医疗中作为靶点和生物标志物的研究、探讨癌症发生发展的机理具有重大意义。

本项目按原计划完成。在本项目中，我们通过整合基因组、转录组、表观组及功能数据开发了系统识别 ncRNA 共调控的多步骤方法。我们将该方法应用到多种癌症中，识别了泛癌ncRNA共调控关系对（包括miRNA-miRNA及lncRNA-lncRNA 共调控），并构建了ncRNA共调控网络。我们发现共调控ncRNA具有表达高且波动性大，共调控关系对具有倾向共表达并参与癌症Hallmark调控等特征。同时共调控ncRNA的表达及网络特征能够揭示不同的癌症起源。我们还着重关注共调控网络中的关键节点并识别出可以在多种癌症中作为潜在预后标记的ncRNA。在功能分析中我们发现共调控lncRNA显著参与肿瘤免疫微环境调控，因此我们利用免疫 lncRNA 表达聚类识别了具有显著临床特征差异的癌症免疫亚型。进一步，我们利用lncRNA特征集构建了肿瘤免疫细胞浸润比例预测模型。我们将该模型应用到TCGA 33种肿瘤中进行免疫细胞预测，并利用不同免疫细胞浸润的比例对肿瘤样本进行了重新分型。特别地，我们还拓展了本项目的研究工作，进一步系统分析了胶质瘤中lncRNA的动态表达模式，最终识别了预后相关lncRNA模块。同时我们利用转录组数据分析了阿尔兹海默症（AD）患者的lncRNA表达及蛋白编码基因表达，识别了差异表达lncRNA并构建了lncRNA与蛋白编码基因共表达网络，最后预测及优化了AD相关基因及lncRNA。我们还开发了一个正则化的回归模型，通过整合基因组、表观组和调控数据来识别elincRNA。这些方法及分析结果为癌症中ncRNA的研究及其分子机制的探究提供了新的视角。为了进一步推广本项目的研究成果，我们开发了LOA等在线数据分析平台及数据库，为研究ncRNA 的分子功能提供了重要的工具。在本项目的支持下，已经发表SCI论文4篇，主要发表于国外著名生命科学相关杂志《Briefings in Bioinformatics》、《Molecular Therapy: Nucleic Acids》及《Frontiers in Bioengineering and Biotechnology》等。

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