【摘要】：由于技術(shù)的發(fā)展以及成本的降低,基因組測(cè)序在孟德爾遺傳疾病,復(fù)雜疾病,以及癌癥基因檢測(cè)中得到了運(yùn)用,并產(chǎn)生了海量的測(cè)序數(shù)據(jù)。這些數(shù)據(jù)對(duì)研究疾病的致病機(jī)制、疾病的臨床診斷、以及對(duì)疾病的個(gè)性化治療都有重要意義。超過4000種人類遺傳疾病的分子致病機(jī)制尚不清楚。研究表明,遺傳疾病的發(fā)生機(jī)制與可變剪接密切相關(guān),剪接位點(diǎn)是可變剪接機(jī)制的重要調(diào)控元素之一,在剪接位點(diǎn)層面上研究疾病的致病機(jī)制對(duì)遺傳疾病的致病機(jī)制研究具有至關(guān)重要的作用。為了解決這個(gè)問題,本文采用序列模式挖掘模型研究遺傳疾病的剪接位點(diǎn)致病突變。癌癥是人類健康的最大威脅,識(shí)別潛在的原癌基因和抑癌基因不僅能提高我們對(duì)腫瘤發(fā)生和癌癥演進(jìn)的理解,而且有助于癌癥個(gè)性化治療的發(fā)展。過去幾年的基因組測(cè)序研究產(chǎn)生了大量的癌癥體細(xì)胞突變數(shù)據(jù),但是如何解釋這些序列信息仍然是一個(gè)巨大的挑戰(zhàn),在過去的研究中,人們根據(jù)攜帶突變的基因的功能對(duì)突變是否具有驅(qū)動(dòng)性來識(shí)別驅(qū)動(dòng)基因,還出現(xiàn)了很多方法對(duì)這種識(shí)別方法進(jìn)行補(bǔ)充。雖然已經(jīng)有一些計(jì)算工具可以預(yù)測(cè)突變的功能影響,但是它們的作用是有限的。因遺傳疾病致病突變與癌癥體細(xì)胞突變的共同突變建立影響蛋白質(zhì)功能的分子機(jī)制,我們假設(shè)這些共享相同突變的基因是癌癥驅(qū)動(dòng)基因,在本文的研究中,我們利用遺傳疾病致病突變與癌癥體細(xì)胞突變的重疊突變來識(shí)別潛在的新型癌癥驅(qū)動(dòng)突變。本文的主要工作如下:(1)應(yīng)用序列模式挖掘模型研究遺傳疾病的剪接位點(diǎn)區(qū)域致病突變。本文應(yīng)用的序列模式挖掘模型是頻繁模式挖掘算法融合PSSM算法的模型,通過該模型的實(shí)驗(yàn)結(jié)果表明,該模型在區(qū)分遺傳疾病致病突變與普通變異時(shí)具有良好的分類效果,遺傳疾病剪接位點(diǎn)區(qū)域的致病變異使剪接位點(diǎn)信號(hào)變?nèi)?從而使正常的剪接被破壞,導(dǎo)致疾病的發(fā)生。(2)利用遺傳疾病致病突變識(shí)別癌癥原癌基因和抑癌基因。在這項(xiàng)研究中,我們利用孟德爾疾病致病突變與癌癥體細(xì)胞突變的重疊變異識(shí)別潛在的原癌基因和抑癌基因。因?yàn)檫z傳疾病致病突變與癌癥體細(xì)胞突變共享突變已有影響蛋白質(zhì)功能的明確分子機(jī)制,因此我們假設(shè)這些突變更可能是癌癥驅(qū)動(dòng)突變。我們的研究表明,癌癥體細(xì)胞突變與遺傳疾病致病突變的重疊突變?cè)诎┌Y中的突變頻率較高,并在已知的癌癥基因中富集。我們根據(jù)不同的重疊突變數(shù)目來識(shí)別潛在的腫瘤抑制基因,結(jié)果表明:離子通道、膠原蛋白、馬方綜合征相關(guān)基因可能是抑癌基因的新分類。然后在每種特異癌癥類型中,我們根據(jù)高復(fù)發(fā)率,以及根據(jù)與癌基因基因突變互斥的重疊突變識(shí)別潛在原癌基因�？傊�,我們的研究表明可以使用遺傳疾病致病突變和癌癥體細(xì)胞突變的重疊突變從大量癌癥基因組測(cè)序數(shù)據(jù)中發(fā)現(xiàn)新的癌癥基因。
[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.
【學(xué)位授予單位】：安徽大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：R596;TP311.13

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