遗传疾病突变的数据挖掘分析

发布时间：2018-09-08 20:59

【摘要】：由于技术的发展以及成本的降低,基因组测序在孟德尔遗传疾病,复杂疾病,以及癌症基因检测中得到了运用,并产生了海量的测序数据。这些数据对研究疾病的致病机制、疾病的临床诊断、以及对疾病的个性化治疗都有重要意义。超过4000种人类遗传疾病的分子致病机制尚不清楚。研究表明,遗传疾病的发生机制与可变剪接密切相关,剪接位点是可变剪接机制的重要调控元素之一,在剪接位点层面上研究疾病的致病机制对遗传疾病的致病机制研究具有至关重要的作用。为了解决这个问题,本文采用序列模式挖掘模型研究遗传疾病的剪接位点致病突变。癌症是人类健康的最大威胁,识别潜在的原癌基因和抑癌基因不仅能提高我们对肿瘤发生和癌症演进的理解,而且有助于癌症个性化治疗的发展。过去几年的基因组测序研究产生了大量的癌症体细胞突变数据,但是如何解释这些序列信息仍然是一个巨大的挑战,在过去的研究中,人们根据携带突变的基因的功能对突变是否具有驱动性来识别驱动基因,还出现了很多方法对这种识别方法进行补充。虽然已经有一些计算工具可以预测突变的功能影响,但是它们的作用是有限的。因遗传疾病致病突变与癌症体细胞突变的共同突变建立影响蛋白质功能的分子机制,我们假设这些共享相同突变的基因是癌症驱动基因,在本文的研究中,我们利用遗传疾病致病突变与癌症体细胞突变的重叠突变来识别潜在的新型癌症驱动突变。本文的主要工作如下:(1)应用序列模式挖掘模型研究遗传疾病的剪接位点区域致病突变。本文应用的序列模式挖掘模型是频繁模式挖掘算法融合PSSM算法的模型,通过该模型的实验结果表明,该模型在区分遗传疾病致病突变与普通变异时具有良好的分类效果,遗传疾病剪接位点区域的致病变异使剪接位点信号变弱,从而使正常的剪接被破坏,导致疾病的发生。(2)利用遗传疾病致病突变识别癌症原癌基因和抑癌基因。在这项研究中,我们利用孟德尔疾病致病突变与癌症体细胞突变的重叠变异识别潜在的原癌基因和抑癌基因。因为遗传疾病致病突变与癌症体细胞突变共享突变已有影响蛋白质功能的明确分子机制,因此我们假设这些突变更可能是癌症驱动突变。我们的研究表明,癌症体细胞突变与遗传疾病致病突变的重叠突变在癌症中的突变频率较高,并在已知的癌症基因中富集。我们根据不同的重叠突变数目来识别潜在的肿瘤抑制基因,结果表明:离子通道、胶原蛋白、马方综合征相关基因可能是抑癌基因的新分类。然后在每种特异癌症类型中,我们根据高复发率,以及根据与癌基因基因突变互斥的重叠突变识别潜在原癌基因。总之,我们的研究表明可以使用遗传疾病致病突变和癌症体细胞突变的重叠突变从大量癌症基因组测序数据中发现新的癌症基因。
[Abstract]:Because of the development of technology and the reduction of cost, genome sequencing has been applied in Mendelian genetic diseases, complex diseases, and cancer gene detection, and produced a large amount of sequencing data. These data are important for studying the pathogenesis, clinical diagnosis and individualized treatment of disease. The molecular pathogenesis of more than 4000 human genetic diseases is unclear. Studies have shown that the mechanism of genetic diseases is closely related to variable splicing, splicing site is one of the important regulatory elements of variable splicing mechanism. It is very important to study the pathogenesis of genetic diseases at splicing site level. In order to solve this problem, sequence pattern mining model is used to study the mutation of splicing sites in genetic diseases. Cancer is the greatest threat to human health. The identification of potential proto-oncogenes and tumor suppressor genes can not only improve our understanding of tumorigenesis and cancer progression, but also contribute to the development of personalized cancer therapy. Genome sequencing studies over the past few years have produced a lot of data on somatic mutations in cancer, but how to interpret this sequence information remains a huge challenge in previous studies. Many methods have been developed to identify the driving genes according to the function of the genes that carry the mutations. Although some computing tools are available to predict the functional impact of mutations, their role is limited. The common mutation of genetic disease and cancer somatic cell establishes the molecular mechanism that affects the function of protein. We assume that these genes shared the same mutation are cancer driving genes. We use overlapping mutations of genetic diseases and cancer somatic mutations to identify potential new types of cancer driven mutations. The main work of this paper is as follows: (1) the sequence pattern mining model is used to study the mutation of splicing site region in genetic diseases. The sequential pattern mining model used in this paper is a fusion model of frequent pattern mining algorithm and PSSM algorithm. The experimental results show that the model has a good classification effect in distinguishing genetic disease mutation from common mutation. The signal of splicing site is weakened by the variation of splicing site region of genetic diseases, which leads to the destruction of normal splicing and the occurrence of disease. (2) Identification of cancer proto-oncogene and tumor suppressor gene by genetic disease mutation. In this study, we identified potential oncogenes and tumor suppressor genes using overlapping mutations of Mendelian disease and somatic mutations. Since genetic disease mutations and somatic mutations share mutations have clear molecular mechanisms that affect the function of proteins, we assume that these mutations are more likely to be cancer-driven mutations. Our studies have shown that superposition mutations of cancer somatic mutations and genetic disease pathogenic mutations are more frequently mutated in cancer and are enriched in known cancer genes. We identify potential tumor suppressor genes according to the number of overlapped mutations. The results show that ion channels, collagen and Marfan syndrome related genes may be a new classification of tumor suppressor genes. Then in each specific cancer type we identify potential proto-oncogenes based on high recurrence rates and overlapping mutations that are mutually exclusive to oncogene mutations. In conclusion, our research suggests that new cancer genes can be found from a large number of cancer genome sequencing data using overlapping mutations of genetic disease and cancer somatic mutations.
【学位授予单位】：安徽大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：R596;TP311.13

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