Developing Gender-Specific Gene Expression Biodosimetry Using a Panel of Radiation-Responsive Genes for Determining Radiation Dose in Human Peripheral Blood

In a large-scale radiological incident, rapid and high-throughput biodosimetry would be needed. Gene expression-based biodosimetry is a promising approach to determine the dose received after radiation exposure. We previously identified 35 candidate genes as biodosimetry markers based on a systematic review. The goal of the current study was to establish and validate a specific gene expression-based radiological biodosimetry using a panel of highly radio-responsive genes in human peripheral blood for improving the accuracy of dose estimation. Human peripheral blood samples from 30 adult donors were irradiated to 0, 0.5, 1, 2, 3, 4, 6 and 8 Gy with Co-60 gamma rays at a dose rate of 1 Gy/min. We examined the expression patterns of candidate genes using real-time polymerase chain reaction (qRT-PCR) at 6, 12, 24 and 48 h postirradiation. Stepwise regression analysis was employed to develop the gene expression-based dosimetry models at each time point. Samples from another 10 healthy donors (blind samples) and four total-body irradiated (TBI) patients were used to validate the radiation dosimetry models. We observed significant linear dose-response relationships of CDKN1A, BAX, MDM2, XPC, PCNA, FDXR, GDF-15, DDB2, TNFRSF10B, PHPT1, ASTN2, RPS27L, BBC3, TNFSF4, POLH, CCNG1, PPM1D and GADD45A in human peripheral blood at the various time points. However, the expression levels of these genes were affected by interindividual variations and gender. We found that the genderdependent regression models could explain 0.85 of variance at 24 h postirradiation and could also accurately estimate the absorbed radiation doses with dose range of 0-5 Gy, in human peripheral blood samples irradiated ex vivo and from TBI patients, respectively. This study demonstrates that developing gender-specific biodosimetry based on a panel of highly radioresponsive genes may help advance the application of gene expression signature for dose estimation in the event of a radiological accident or in clinical treatment. (C) 2019 by Radiation Research Society