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Improved gene expression biodosimetry for dose estimation using an expanded panel of radiation-responsive genes

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Figshare2026-03-19 更新2026-04-28 收录
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https://figshare.com/articles/dataset/Improved_gene_expression_biodosimetry_for_dose_estimation_using_an_expanded_panel_of_radiation-responsive_genes/31813559
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Gene expression analysis provides a minimally invasive approach for biological dosimetry. To advance point-of-care applications, this study aimed to establish and validate an improved gene expression biodosimetry system by employing an expanded panel of radiation-responsive genes in human peripheral blood. Human B lymphoblastoid cells (AHH-1) and peripheral blood from 10 healthy donors were irradiated with 60Co γ-rays at doses of 0, 1, 2, 4, 6, and 8 Gy (dose rate: 1 Gy/min). The expression patterns of four candidate transcriptional biomarkers (ZMAT3, SESN1, AEN, and TRIAP1) and a panel of radiation-responsive genes were characterized at 6–48 h post-irradiation. The impact of different dose rates (0.2, 1, and 2 Gy/min) on these gene expressions was also investigated. For each gene, calibration curves were established by fitting a linear regression between the logarithm of absorbed dose and ΔCt values. Gene selection and model construction were performed using stepwise regression to obtain optimized multi-gene models. The accuracy of these dosimetry models for dose prediction was validated in independent ex vivo and in vivo cohorts. The four candidate genes exhibited robust, dose-dependent expression from 6 to 48 h post-irradiation, independent of dose-rate variations (0.2–2 Gy/min). Most genes in the expanded panel, including the candidates, showed strong linear relationships between log2 of dose and ΔCt values across all time points when the 0 Gy point was excluded from regression (R2 > 0.90, S R2 = 0.81–0.89) with fewer genes. Furthermore, these improved models demonstrated accurate dose estimation capabilities when validated with both ex vivo- and in vivo-irradiated peripheral blood samples. Our study expanded the panel of reliable radiation biomarkers and developed optimized multi-gene models for accurate dose estimation, thereby advancing the standardization and practicality of gene expression biodosimetry.
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2026-03-19
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