Hyduke Daniel R, Laiakis Evagelia C, Li Heng-Hong, Fornace Albert J
Department of Biochemistry and Molecular and Cellular Biology, and Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, Washington, DC 20057, USA.
Int J Bioinform Res Appl. 2013;9(4):365-85. doi: 10.1504/IJBRA.2013.054701.
Ionising radiation is a pleiotropic stress agent that may induce a variety of adverse effects. Molecular biomarker approaches possess promise to assess radiation exposure, however, the pleiotropic nature of ionising radiation induced transcriptional responses and the historically poor inter-laboratory performance of omics-derived biomarkers serve as barriers to identification of unequivocal biomarker sets. Here, we present a whole-genome survey of the murine transcriptomic response to physiologically relevant radiation doses, 2 Gy and 8 Gy. We used this dataset with the Random Forest algorithm to correctly classify independently generated data and to identify putative metabolite biomarkers for radiation exposure.
电离辐射是一种多效性应激因子，可能会引发多种不良反应。分子生物标志物方法有望用于评估辐射暴露情况，然而，电离辐射诱导的转录反应具有多效性，且组学衍生生物标志物在不同实验室间的表现历来不佳，这些都阻碍了明确生物标志物集的识别。在此，我们展示了对小鼠转录组对生理相关辐射剂量（2 Gy和8 Gy）反应的全基因组调查。我们使用该数据集和随机森林算法对独立生成的数据进行正确分类，并识别辐射暴露的潜在代谢物生物标志物。
