Hidden Markov model for defining genomic changes in lung cancer using gene expression data
Hidden Markov model for defining genomic changes in lung cancer using gene expression data
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DOI：
10.1089/omi.2006.10.276
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发表时间：
2006-09-01
影响因子：
3.3
通讯作者：
Kardia, Sharon L. R.
中科院分区：
文献类型：
--
作者：
Huang, Chiang-Ching;Taylor, Jeremy M. G.;Kardia, Sharon L. R.
研究基因表达模式与染色体位置的关系，即“转录组图谱”，已成为一个活跃的研究领域，并揭示了基因表达水平高度相关的意想不到的染色体区域。在癌症研究中，基因表达的这些区域性变化可能是由于染色体水平的改变，如基因扩增或丢失。为了便于利用基因表达数据搜索这些区域，我们开发了一种隐马尔可夫模型（HMM）。最大惩罚似然被用来估计模型中的参数。该方法应用于肺癌微阵列实验，包括86人肺腺癌。通过HMM鉴定的几个区域与该癌症中已知的扩增或缺失的复发区域一致。我们进一步证明了这些异常表达区域与疾病状态的测量，如肿瘤分期，分化和生存的相关性。这些发现表明，在这些区域的基因可能在肺癌的发生过程中发挥了重要作用。我们所提出的方法提供了一个有价值的工具，以准确地查明区域的异常表达，为进一步调查。
The study of gene expression patterns in relationship to chromosomal position, the "transcriptome map," has become an area of active research and has revealed unexpected chromosomal regions within which gene expression levels are highly correlated. In cancer research, these regional changes in gene expression that may result from alterations at the chromosome level such as gene amplification or loss. To facilitate the search for such regions utilizing gene expression data, we have developed a hidden Markov model (HMM). Maximum penalized likelihood is used to estimate the parameters in the model. This method is applied to a lung cancer microarray experiment, including 86 human lung adenocarcinomas. Several regions identified through the HMM are consistent with known recurrent regions of amplification or deletion in this cancer. We further demonstrate the association of these abnormal expression regions with measures of disease status, such as tumor stage, differentiation, and survival. These findings suggest that genes in these regions may play a major role in the process of carcinogenesis of the lung. Our proposed method provides a valuable tool to accurately pinpoint regions of abnormal expression for further investigation.