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Genomics and Epidemiological Analysis of Melanoma Laterality

机译:黑色素瘤横向的基因组学和流行病学分析

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摘要

Skin cancer is the most commonly diagnosed cancer in the United States and melanoma is considered the deadliest form of skin cancer. Although the environmental causes of melanomas are known, the molecular mechanisms involved are still being researched. Melanomas present more often on the left side of the body, but explanations for this laterality are conflicting and largely focused on epidemiological factors. In this thesis, both epidemiological and genetic factors affecting melanoma laterality are analyzed to explore how tumor laterality and patterning may arise in general. The Surveillance, Epidemiology, and End Results (SEER) and The Cancer Genome Atlas (TCGA) databases were used to analyze clinical cases of melanoma. Data analysis was conducted by calculating a laterality ratio of asymmetric melanomas and comparing how these ratios differ in epidemiological and genetic variables. A machine learning algorithm was also applied to predict which variables or groups of variables may determine laterality. Results showed that, as established, melanomas tend to exhibit left-sided laterality, but epidemiological factors alone are not good indicators of where tumors present. Genomics analysis revealed several genes and targets of interest. Genes involved in cell adhesion were consistently significant, but there was no conclusive evidence that a specific gene or set of genes causes left-sided patterning. Although results did not reveal specific genetic targets as determinants of melanoma laterality, they prove that the methods used can be tools for analyzing tumor laterality in general and can help in predicting what variables are important in molecular mechanisms indicating laterality.
机译:皮肤癌是美国最常被诊断出的癌症,黑色素瘤被认为是皮肤癌中最致命的形式。尽管已知黑色素瘤的环境原因,但涉及的分子机制仍在研究中。黑色素瘤通常出现在身体的左侧,但是这种偏侧性的解释相互矛盾,并且主要集中在流行病学因素上。在本文中,分析了影响黑色素瘤偏侧性的流行病学和遗传因素,以探讨一般情况下如何可能出现肿瘤偏侧性和模式。监测,流行病学和最终结果(SEER)和癌症基因组图谱(TCGA)数据库用于分析黑色素瘤的临床病例。通过计算非对称性黑色素瘤的横向比率并比较这些比率在流行病学和遗传变量方面的差异来进行数据分析。还应用了机器学习算法来预测哪些变量或变量组可以确定横向性。结果表明,如所确定的那样,黑色素瘤倾向于表现出左侧偏侧性,但仅流行病学因素并不能很好地指示肿瘤的位置。基因组学分析揭示了几个感兴趣的基因和靶标。参与细胞粘附的基因始终具有重要意义,但没有确凿证据表明特定基因或一组基因会引起左侧图案。尽管结果并未揭示确定黑色素瘤偏侧性的特定遗传靶标,但它们证明了所使用的方法通常可作为分析肿瘤偏侧性的工具,并可帮助预测哪些变量在指示偏侧性的分子机制中很重要。

著录项

  • 作者

    Fan, Winnie Zhu.;

  • 作者单位

    University of California, Irvine.;

  • 授予单位 University of California, Irvine.;
  • 学科 Bioengineering.
  • 学位 M.S.
  • 年度 2018
  • 页码 50 p.
  • 总页数 50
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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