【摘要】：在癌癥研究和醫(yī)學領(lǐng)域,生物標志物能夠在早期對癌癥病人的病情進行診斷,及時提供治療方法,并且還可以對癌癥的病情進行預測,對于癌癥的治療具有極高的指導價值。許多研究已經(jīng)報道基因可作為候選的生物標志物,被應用于疾病或者癌癥的診斷、預后和療效等方面。隨著高通測序技術(shù)的發(fā)展,癌癥生物標志物的研究也開始從單一組學數(shù)據(jù)到多組學數(shù)據(jù)發(fā)展,但是多組學數(shù)據(jù)的整合還停留在簡單整合階段,不能發(fā)現(xiàn)多組學數(shù)據(jù)的內(nèi)在聯(lián)系。我們整合基因表達數(shù)據(jù)和DNA甲基化數(shù)據(jù)進行癌癥生物標志物的研究與分析。本論文的研究內(nèi)容如下:1、傳統(tǒng)的特征選擇方法在高維小樣本數(shù)據(jù)中往往考慮特征選擇結(jié)果的高分類性能,而忽略了特征選擇結(jié)果的穩(wěn)定性。對此,本文提出在對基因表達數(shù)據(jù)進行特征選擇的時候,保留研究者公認的與癌癥相關(guān)的重要基因,得到一個穩(wěn)定性高的基因特征組合的方法。2、由于450K甲基化芯片僅覆蓋全部甲基化位點的2%,采用簡單融合的方式可能導致結(jié)果有偏。本文首次提出使用擴展后的450K甲基化芯片數(shù)據(jù)與基因表達數(shù)據(jù)進行融合的方法,從多個層面分析癌癥生物標志物,并且盡可能的利用現(xiàn)有的DNA甲基化數(shù)據(jù),融合多組學數(shù)據(jù)的時候保留更多的信息,得到穩(wěn)定可靠的具有推廣價值的潛在癌癥生物標志物。本文的方法比傳統(tǒng)的方法分類精確度和可靠性更高。本文分析了多種癌癥特定的潛在癌癥生物標志物和多種癌癥共有的潛在癌癥生物標志物,為醫(yī)學研究和臨床治療提供指導和幫助。3、構(gòu)建基于模糊規(guī)則的分類器模型來驗證本文選擇的潛在癌癥生物標志物對于正常和癌癥樣本的分類效果,通過交叉驗證對比本文的方法和傳統(tǒng)的基因表達數(shù)據(jù)和DNA甲基化數(shù)據(jù)的簡單的融合方法,發(fā)現(xiàn)本文的方法要優(yōu)于傳統(tǒng)方法,并且本文的方法對獨立樣本的預測結(jié)果也優(yōu)于傳統(tǒng)的方法,最后基于找到的潛在癌癥生物標志物得到了魯棒性更高、易于理解的分類規(guī)則。
[Abstract]:In the field of cancer research and medicine, biomarkers can diagnose the condition of cancer patients in the early stage, provide timely treatment methods, and can also predict the condition of cancer, which has a very high guiding value for the treatment of cancer. Many studies have reported that genes can be used as candidate biomarkers for the diagnosis, prognosis and efficacy of diseases or cancer. With the development of high-pass sequencing technology, the research of cancer biomarkers has also begun to develop from single group data to multi-group data, but the integration of multi-group data is still in the stage of simple integration, and the internal relationship of multi-group data can not be found. We integrate gene expression data and DNA methylation data to study and analyze cancer biomarkers. The research contents of this paper are as follows: 1. The traditional feature selection methods often consider the high classification performance of feature selection results in high-dimensional small sample data, but ignore the stability of feature selection results. In order to select the characteristics of gene expression data, this paper proposes to preserve the important genes related to cancer recognized by researchers, and to obtain a stable method of gene feature combination. 2, because 450K methylated chip covers only 2% of all methylated sites, simple fusion may lead to biased results. In this paper, a method of fusion between extended 450K methylated chip data and gene expression data is proposed for the first time, and cancer biomarkers are analyzed from many levels, and as much as possible, more information is retained when fusion of multigroup data, and stable and reliable potential cancer biomarkers with popularizing value are obtained. The classification accuracy and reliability of this method are higher than those of the traditional method. In this paper, a variety of cancer specific potential cancer biomarkers and potential cancer biomarkers common to a variety of cancers are analyzed to provide guidance and help for medical research and clinical treatment. 3. A classification model based on fuzzy rules is constructed to verify the classification effect of the potential cancer biomarkers selected in this paper for normal and cancer samples. By cross-verifying and comparing the traditional method of gene expression and the simple fusion method of DNA methylation data, it is found that the method in this paper is superior to the traditional method, and the prediction results of independent samples are also better than the traditional method. Finally, based on the potential cancer biomarkers found, a classification rule with higher robustness and easy to understand is obtained.
【學位授予單位】：電子科技大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：R730.4

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