Clinical Research Division, Fred Hutchinson Cancer Research Center, 1100 Fairview Avenue N., Seattle, WA 98109, USA.
Radiat Res. 2011 Feb;175(2):172-84. doi: 10.1667/rr1977.1. Epub 2010 Nov 17.
In the event of a radiation accident or attack, it will be imperative to quickly assess the amount of radiation exposure to accurately triage victims for appropriate care. RNA-based radiation dosimetry assays offer the potential to rapidly screen thousands of individuals in an efficient and cost-effective manner. However, prior to the development of these assays, it will be critical to identify those genes that will be most useful to delineate different radiation doses. Using global expression profiling, we examined expression changes in nonimmortalized T cells across a wide range of doses (0.15-12 Gy). Because many radiation responses are highly dependent on time, expression changes were examined at three different times (3, 8, and 24 h). Analyses identified 61, 512 and 1310 genes with significant linear dose-dependent expression changes at 3, 8 and 24 h, respectively. Using a stepwise regression procedure, a model was developed to estimate in vitro radiation exposures using the expression of three genes (CDKN1A, PSRC1 and TNFSF4) and validated in an independent test set with 86% accuracy. These findings suggest that RNA-based expression assays for a small subset of genes can be employed to develop clinical biodosimetry assays to be used in assessments of radiation exposure and toxicity.
在辐射事故或袭击的情况下,快速评估辐射暴露量对于准确对受害者进行分类并提供适当的护理至关重要。基于 RNA 的辐射剂量测定分析有潜力以高效且具有成本效益的方式快速筛选数千人。然而,在开发这些分析方法之前,确定那些最有助于区分不同辐射剂量的基因将是至关重要的。我们使用全局表达谱分析,研究了非永生化 T 细胞在广泛剂量范围内(0.15-12Gy)的表达变化。由于许多辐射反应高度依赖于时间,因此在三个不同时间点(3、8 和 24 小时)检查了表达变化。分析分别在 3、8 和 24 小时时确定了 61、512 和 1310 个具有显著线性剂量依赖性表达变化的基因。使用逐步回归程序,开发了一种使用三个基因(CDKN1A、PSRC1 和 TNFSF4)的表达来估计体外辐射暴露的模型,并在具有 86%准确性的独立测试集中进行了验证。这些发现表明,可以使用基于 RNA 的少数基因表达分析来开发临床生物剂量测定分析,以用于评估辐射暴露和毒性。