长链非编码RNA在肿瘤代谢中的作用研究

The transcriptional regulation of cell metabolism is pivotal to all cancer developmental processes. Each of ~1,000 protein-coding metabolic enzymes in human genome must be expressed at the right subcellular location with proper abundance as well as under the right developmental or physiological circumstances. Consequently, inappropriate gene expression related to metabolism is implicated in a myriad of human cancers. For rapid cell growth, most cancer cells predominantly produce energy by a high rate of glycolysis followed by lactic acid fermentation in the cytosol, rather than by a comparatively low rate of glycolysis followed by oxidation of pyruvate in mitochondria as in most normal cells. This observation is called the Warburg effect about the "power station" in cancer cells for rapidly proliferating.The abnormal cell metabolism often involves multi-step changes in the key regulator in the genome. Since tumor suppressors (TSGs) and oncogenes (OCGs) influence cancer cell growth in opposite ways, we hypothesis that TSGs and OCGs may act as post-translational modulators of transcription factors (TFs) to competitively control cancer metabolism. Although transcriptional regulation of protein–coding TSGs/OCGs has been studied intensively, it has left unanswered questions on the combinatorial metabolic regulation of non-coding TSGs/OCGs. By married with a large TCGA cancer genomics big data, we aims to construct a blue-prints of metabolic regulatory networks in pan-cancer level. ..Aim 1: Explore pan-cancer co-expression data to identify regulatory triplet. This task is based on our previous computational framework to identify regulatory modulators of TFs with expression profiles based on conditional mutual information. It requires four inputs, including gene expression, TFs, a list of potential TSGs/OCGs, and a list of potential TF targets. The most comprehensive human TSGs/OCGs list will be downloaded from our TSGene database. To add lncRNA TSGs/OCGs in this project, our published pan-cancer co-expression network for human lncRNA and cancer genes, LnCaNet, will be used. ..Aim 2: Overlap with protein-protein interaction data to identify competitive regulatory network on rate-limiting enzymes (RLEs). The RLEs such as IDH1, because of their relatively low velocity, are critical for metabolic flux control. By filtering the targeting genes from Task 1 with our curated RLEs, we will further characterize the regulatory triplets with TSGs/OCGs, TFs, and RLEs. The competitive pattern will be defined if the TSGs and OCGs targeted on the same TFs to competitively regulate a RLE. ..Aim 3: Identify the common regulatory pattern across multiple cancer types. On the basis of aim1/2, we will scrutinize the common competitive patterns across multiple cancers with particular focusing on the unexplored lncRNA TSGs/OCGs. The candidate competitive triplets with lncRNAs will also be prioritized in specific anatomic location, histological type, and developmental stage and model its global network mechanisms of cancer progression. Expected outcomes: Our analyses will define the common competitive networks between lncRNA TSGs and OCGs that drive cancer metabolism across multiple cancers. The interference of critical lncRNAs with competitive roles in cancer metabolism may provide a non-toxin treatment to starve cancer cells.

长链非编码RNA(lncRNA)在癌症代谢中的作用尚缺乏系统的研究。申请人前期研究发现lncRNA与大量的抑癌基因(TSG)，促癌基因(OCG)在多个癌症中有共表达的模式。预分析TCGA数据显示多个TSG与OCG涉及癌症代谢。TSG/OCG能竞争性结合限速酶而影响癌症的代谢过程，故推测：lncRNA可与OCG和TSG共同调控限速酶而影响多种癌症的代谢。本项目拟通过分析TCGA数据，根据lncRNA与TSG/OCG共表达关系构建介导癌症代谢的“lncRNA→TSG/OCG→转录因子→靶标限速酶”三联调控单元，并依据蛋白蛋白相互作用将其演变成调控网络；检测肿瘤组织中相关 lncRNA及蛋白的表达水平,验证该调控网络；构建lncRNA gain/loss of function细胞系，分析其对能量代谢的影响；系统地探索及阐明关键lncRNA在癌症代谢中作用及其调控机，为肿瘤靶向治疗提供科学依据。

本课题主要研究基于长链非编码RNA（lncRNA）的癌细胞代谢的转录调控网络。大量研究表明细胞新陈代谢中不适当的基因表达涉及众多的人类癌症。这些异常的细胞代谢往往涉及到多种细胞途径及代谢酶。例如由于肿瘤抑制因子（TSG）和致癌因子（OCG）以相反的方式影响癌细胞的生长，我们验证了具有TSG和OCG性质的lncRNA作为转录后层面上发挥调控作用。具体来说，我们在多种癌症类型上发现OCG和TSG可能作为转录因子（TF）的翻译后调节剂，以 竞争性地控制癌症的代谢。我们构建了一个泛癌层面的代谢调控网络蓝图。为了探讨TGS和OCG相关调控网络，我们首先构建了最为全面的TSG和OCG数据库，并提取了其中的lncRNA。为了在本项目中构建lncRNA和编码 TSG/OCG的关系，我们使用已发表的人类lncRNA和癌症基因的表达谱数据构建了泛癌症共表达网络LnCaNet。利用这些lncRNA和编码基因间的竞争性的调控TF的关系以及TF-代谢酶相互作用数据，我们确定了竞争性的限速酶（RLEs）的调节网络。同时我们在9个结直肠癌样本中产生了环状lncRNA，线性lncRNA、mRNA以及基因拷贝数（CNV）的多维组学数据。通过多维组学的数据挖掘，我们构建了一个针对患者的CNV-lncRNA-mRNA以及lncRNA-小RNA/TF-mRNA的调控三联体。针对两个三联体调控信息发现的排名靠前的调控三联体均进行了实验验证。例如，我们发现circMET基因受到小RNA和转录因子调控，具有促进结直肠癌发展的作用。同样的，我们发现LSM14B相关的由于CNV引起的lncRNA表达变化也可以促进结直肠癌细胞增殖和转移。所有这些结果对癌症代谢中具有竞争作用的关键lncRNAs机制的探索具有前瞻性。为了在涉及相关lncRNA干扰的治疗方案提供一种可替代的分子诊断以及靶标基因。