Zhang Yuping, Tibshirani Robert J, Davis Ronald W
Stanford University, USA.
Stat Appl Genet Mol Biol. 2010;9(1):Article41. doi: 10.2202/1544-6115.1617. Epub 2010 Nov 22.
Characterizing dynamic gene expression pattern and predicting patient outcome is now significant and will be of more interest in the future with large scale clinical investigation of microarrays. However, there is currently no method that has been developed for prediction of patient outcome using longitudinal gene expression, where gene expression of patients is being monitored across time. Here, we propose a novel prediction approach for patient survival time that makes use of time course structure of gene expression. This method is applied to a burn study. The genes involved in the final predictors are enriched in the inflammatory response and immune system related pathways. Moreover, our method is consistently better than prediction methods using individual time point gene expression or simply pooling gene expression from each time point.
表征动态基因表达模式并预测患者预后目前具有重要意义，并且随着微阵列的大规模临床研究，未来将更受关注。然而，目前还没有开发出利用纵向基因表达来预测患者预后的方法，即对患者的基因表达随时间进行监测。在此，我们提出一种利用基因表达的时间进程结构来预测患者生存时间的新方法。该方法应用于一项烧伤研究。最终预测指标中涉及的基因在炎症反应和免疫系统相关通路中富集。此外，我们的方法始终优于使用单个时间点基因表达或简单汇集每个时间点基因表达的预测方法。
