非编码RNA在人类癌症中的转录调控研究

发布时间：2018-01-04 19:21

本文关键词：非编码RNA在人类癌症中的转录调控研究　出处：《华东师范大学》2017年博士论文　论文类型：学位论文

【摘要】：癌症是威胁人类生命健康最严重的疾病之一,其发病率与死亡率一直处于各种疾病之首。目前已发现的癌症类型有100多种。有研究表明,非编码RNA,如小RNA(miRNA),长链非编码RNA(lncRNA)在癌症的发生发展过程中发挥着重要的调控作用。因此,对癌症中非编码RNA的转录调控研究是了解、治疗癌症的重要手段。近些年来,非编码RNA在癌症中的研究方法还非常有限。生物信息学算法和高通量测序技术的发展为癌症中非编码RNA的转录调控研究提供了新的理论和方法,同时,若干与癌症转录调控相关的科学问题也需要更全面的审视和探讨。本论文所关注的重点是miRNA和lncRNA在癌症中转录调控功能的研究。本论文的第一部分是miRNA与转录因子联合调控作用在多种癌症和正常组织细胞系中的研究。首先,我们研究了 19种人类细胞系中基因是否受miRNA靶向调控同基因转录因子结合位点数目之间的关系。研究结果不仅进一步支持了miRNA偏向于结合转录调控复杂的基因,同时还发现癌症和正常组织细胞系具有类似的调控机制。因此,miRNA和转录因子的这种联合调控作用具有普遍的意义。通过对转录因子结合位点的位置特征进行分析研究,我们发现基因是否受miRNA调控主要受基因转录起始区域的转录因子结合位点信息影响。在此基础上,我们构建了数学模型,成功利用基因转录因子结合位点信息对基因是否受miRNA调控进行准确预测。通过对单个miRNA的靶基因进行预测研究,我们发现,miRNA和转录因子的这种联合调控作用可以为优化miRNA靶基因识别算法提供新的思路。本论文的第二部分是lncRNA-mRNA类型的竞争性内源RNA(ceRNA)调控网络在癌症和癌旁组织中的比较研究。首先我们对13种癌症类型的癌症和癌旁组织分别构建了 ceRNA调控网络。通过对同种癌症类型中癌症和癌旁组织进行网络比较分析,我们发现癌旁组织拥有更多的ceRNA调控作用,而癌变过程中仅有少部分ceRNA调控作用得到了保留。通过对癌变过程中增加或消失的ceRNA调控作用进行功能分析,我们发现,增加的ceRNA调控作用往往会带来细胞周期的波动,而消失的ceRNA调控作用则会对Wnt信号通路和细胞凋亡信号通路产生影响。本论文的第三部分是构建人类癌症转移数据库(HCMDB)。首先,我们基于已发表的文献,对miRNA、lncRNA以及蛋白编码基因在癌症转移过程中发挥的功能进行了人工整理,共筛选出2183个与癌症转移相关的基因,包括1901个蛋白编码基因,24个lncRNA基因和203个miRNA。然后,我们从GEO和TCGA数据库中搜集了 152套与癌症转移相关的表达数据,通过更合理的样品分组设计,对癌症转移相关的基因进行了鉴定和调控网络分析。该研究基于文献信息和高通量数据挖掘,构建了第一个癌症转移相关的基因表达和功能数据库,为接下来探索癌症转移过程中非编码RNA和蛋白编码基因的功能提供了数据支持。总之,本论文基于大量公共的转录组和表观遗传学数据,系统地研究了若干癌症中非编码RNA参与的转录调控机制,构建了与癌症转移相关的基因表达和功能数据库。这对于未来癌症中非编码RNA的研究可能具有重要的意义,而且为今后的癌症转录调控研究提供有意义的思路和数据挖掘平台。
[Abstract]:Cancer is one of the most serious disease threat to human life and health, its incidence and mortality have been in all kinds of diseases. It has been found that the type of cancer. There are 100 kinds of studies have shown that non RNA encoding, such as RNA (miRNA), a long chain of non encoding RNA (lncRNA) plays an important role in the regulation of in the process of tumorigenesis. Therefore, study on the transcriptional regulation of non encoding RNA in cancer is an important means of understanding, for the treatment of cancer. In recent years, the research method of non encoding of RNA in cancer is still very limited. The development of bioinformatics algorithms and high-throughput sequencing technology provides a new theory and method. To study the transcriptional regulation of non encoding RNA in cancer at the same time, some associated with cancer transcriptional regulation of scientific problems requires a more comprehensive review and discussion. The focus of this paper is concerned with the miRNA and lncRNA in cancer control function transfer record Study. The first part of this paper is to study the miRNA and transcription factor combined with regulatory role in cell lines of many types of cancer and normal tissues. Firstly, we studied 19 genes in human cell lines by miRNA targeting the same gene transcription factor binding sites. The relationship between the number of research results not only further support miRNA combined with the bias in the complex transcriptional regulation genes, it was also found that the cancer and normal tissue cells have a similar mechanism of regulation. Therefore, it has universal significance and the role of miRNA and regulation of transcription factors. The transcription factor binding site location characteristics analysis, we found that the gene is mainly regulated by transcription by miRNA gene transcription initiation region binding site information. On this basis, we established the mathematical model, the successful use of gene transcription factor binding Site information on whether the gene regulated by miRNA. Through the accurate prediction of single miRNA of target gene prediction research, we found that the combined effect of miRNA and the regulation of transcription factors can provide a new idea for the optimization of miRNA target gene identification algorithm. The second part of this thesis is the competition of endogenous RNA type lncRNA-mRNA (ceRNA) Comparative Study on the regulation of network organization in cancer and para cancer. First we on the 13 types of cancer and cancer adjacent tissues were constructed by ceRNA regulation network. Through a network of the same type of cancer in cancer and paracancerous tissues of comparative analysis, we found that the adjacent tissues have ceRNA more regulatory role, and carcinogenesis just a few of ceRNA regulation have been retained. Through the functional analysis, the regulatory role of ceRNA increased or disappeared during the carcinogenesis process we found that increased CE RNA regulation often leads to cell cycle fluctuations, ceRNA regulation will disappear and effect on Wnt signaling pathway and apoptosis signal pathway. The third part of this thesis is to build a database of human cancer metastasis (HCMDB). First, we have published literature, based on miRNA, lncRNA and protein encoding play in the process of cancer metastasis gene function in the manual sorting, we screened 2183 genes associated with cancer metastasis, including 1901 protein encoding genes, 24 lncRNA genes and 203 miRNA. then, we collected 152 sets of cancer metastasis and expression of related data from GEO and TCGA database, through the sample group the design is more reasonable, the cancer metastasis related genes were identified and regulatory networks. The study of literature information and high-throughput data mining based on the construction of the first cancer metastasis The gene expression and function of the database, provides data support for the next exploration of the non protein encoding gene encoding RNA and cancer metastasis in the process of function. In short, this paper based on a large number of public transcriptome and epigenetic data, systematically studied the mechanism of transcriptional regulation of non coding RNA in several cancers, construct gene expression the function and database related with cancer metastasis. For the future research on non RNA encoding in cancer may have important significance, but also provide meaningful ideas and data mining platform for the study of transcriptional regulation of cancer in the future.

【学位授予单位】：华东师范大学
【学位级别】：博士
【学位授予年份】：2017
【分类号】：R730.2

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