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首页> 外文学位 >Classification and Functional Genomics of Mouse Hepatocellular Carcinoma: The Influence of Specific Initiating Oncogene(s).
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Classification and Functional Genomics of Mouse Hepatocellular Carcinoma: The Influence of Specific Initiating Oncogene(s).

机译：小鼠肝细胞癌的分类和功能基因组学：特定起始致癌基因的影响。

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摘要

My research focused on the classification and functional genomics of mouse hepatocellular carcinoma (HCC). I use three approaches to evaluate and address three hypotheses. First, I demonstrated that, with high accuracy, I could classify tumors according to their initiating oncogene(s). I also determined that, of three machine learning algorithms (Support Vector Machine or SVM, Naive Bayes, and Random Forest), SVM performed the best with my data. This study showed that liver tumors display distinct gene expression patterns that can identify their initiating oncogene(s).;Second, I used clustering analysis to examine the extent of similarities within a genotype (oncogene-specific) group and between genotypes. Clustering of the liver tumors lead to functional evaluation, in which I correlated functional categories of changed gene expression with phenotypic data on tumor aggressiveness. I identified several pathways that correlated with either slow or fast growing c-Myc combinations, and some that might explain the aggressiveness of H-ras combined with beta-catenin. In addition, I also identified candidate genes that correlated with the gender effects observed in the phenotype.;Third, gene expression was compared in normal liver, liver in which oncogene(s) were expressed for up to two weeks, and liver tumors. I found that 10 fold more gene expression changes were acquired during the tumorigenesis process then were present after short term expression of the initial molecular alteration(s). Examining the sequence of expression changes allowed me to propose a link between certain expression patterns and specific categories of gene function. These new insights improve our understanding of the molecular mechanisms of HCC.
机译：我的研究集中于小鼠肝细胞癌（HCC）的分类和功能基因组学。我使用三种方法来评估和解决三个假设。首先，我证明了我可以根据肿瘤的起始癌基因对肿瘤进行分类。我还确定，在三种机器学习算法（支持向量机或SVM，朴素贝叶斯和随机森林）中，SVM对我的数据表现最佳。这项研究表明，肝肿瘤显示出可以识别其起始癌基因的独特基因表达模式。肝肿瘤的聚集导致功能评估，其中我将基因表达改变的功能类别与肿瘤侵袭性的表型数据相关联。我确定了几种与缓慢或快速生长的c-Myc组合相关的途径，有些可能解释了H-ras与β-catenin联合的侵略性。此外，我还鉴定了与在表型中观察到的性别效应相关的候选基因。第三，比较了正常肝脏，癌基因表达长达两周的肝脏和肝脏肿瘤中的基因表达。我发现在肿瘤发生过程中获得了10倍以上的基因表达变化，然后在最初的分子改变的短期表达后出现了。通过检查表达变化的顺序，我可以提出某些表达模式与基因功能的特定类别之间的联系。这些新见解增进了我们对HCC分子机制的了解。

著录项

  • 作者

    Pham, Ly Ly.;

  • 作者单位

    The University of Wisconsin - Madison.;

  • 授予单位 The University of Wisconsin - Madison.;
  • 学科 Oncology.
  • 学位 Ph.D.
  • 年度 2014
  • 页码 226 p.
  • 总页数 226
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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