Department of Biochemistry and Molecular Biology and Institute of Bioinformatics, University of Georgia, Athens, GA 30602, USA, Department of Statistics, University of Georgia, Athens, GA 30602, USA, Department of Molecular Biology, University of Wyoming, Laramie, WY 82071, USA, Department of Biostatistics, Georgia Southern University, Statesboro, GA 30458, USA, School of Mathematics, Shandong University, Jinan 250100, China, Department of Computer Science and Engineering, University of Nebraska-Lincoln, Lincoln, NE 65888, USA, BioEnergy Science Center, Oak Ridge, TN 37830, USA and College of Computer Science and Technology, Jilin University, Changchun, Jilin, China.
Nucleic Acids Res. 2014 Mar;42(5):2870-8. doi: 10.1093/nar/gkt1320. Epub 2013 Dec 25.
As biotechnology advances rapidly, a tremendous amount of cancer genetic data has become available, providing an unprecedented opportunity for understanding the genetic mechanisms of cancer. To understand the effects of duplications and deletions on cancer progression, two genomes (normal and tumor) were sequenced from each of five stomach cancer patients in different stages (I, II, III and IV). We developed a phylogenetic model for analyzing stomach cancer data. The model assumes that duplication and deletion occur in accordance with a continuous time Markov Chain along the branches of a phylogenetic tree attached with five extended branches leading to the tumor genomes. Moreover, coalescence times of the phylogenetic tree follow a coalescence process. The simulation study suggests that the maximum likelihood approach can accurately estimate parameters in the phylogenetic model. The phylogenetic model was applied to the stomach cancer data. We found that the expected number of changes (duplication and deletion) per gene for the tumor genomes is significantly higher than that for the normal genomes. The goodness-of-fit test suggests that the phylogenetic model with constant duplication and deletion rates can adequately fit the duplication data for the normal genomes. The analysis found nine duplicated genes that are significantly associated with stomach cancer.
随着生物技术的飞速发展，大量的癌症遗传数据已经可用，为理解癌症的遗传机制提供了前所未有的机会。为了了解重复和缺失对癌症进展的影响，我们对来自五个不同阶段（I、II、III 和 IV）的五名胃癌患者的两个基因组（正常和肿瘤）进行了测序。我们开发了一种用于分析胃癌数据的系统发育模型。该模型假设重复和缺失按照沿附有通向肿瘤基因组的五个扩展分支的系统发育树分支的连续时间马尔可夫链发生。此外，系统发育树的合并时间遵循合并过程。模拟研究表明，最大似然法可以准确估计系统发育模型中的参数。该系统发育模型应用于胃癌数据。我们发现，肿瘤基因组的每个基因的预期变化（重复和缺失）数量明显高于正常基因组的预期变化数量。拟合优度检验表明，具有恒定重复和缺失率的系统发育模型可以充分拟合正常基因组的重复数据。分析发现了九个与胃癌显著相关的重复基因。
