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Gene expression signatures that predict radiation exposure

ABSTRACT: This SuperSeries is composed of the following subset Series: GSE6871: Gene expression signatures that predict radiation exposure (human) GSE6873: Gene expression signatures that predict radiation exposure (mouse) Keywords: SuperSeries Refer to individual Series

ORGANISM(S): Mus musculus

SUBMITTER: Holly Dressman

PROVIDER: E-GEOD-6874 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Gene expression signatures that predict radiation exposure in mice and humans.

Dressman Holly K HK Muramoto Garrett G GG Chao Nelson J NJ Meadows Sarah S Marshall Dawn D Ginsburg Geoffrey S GS Nevins Joseph R JR Chute John P JP

PLoS medicine 20070401 4

<h4>Background</h4>The capacity to assess environmental inputs to biological phenotypes is limited by methods that can accurately and quantitatively measure these contributions. One such example can be seen in the context of exposure to ionizing radiation.<h4>Methods and findings</h4>We have made use of gene expression analysis of peripheral blood (PB) mononuclear cells to develop expression profiles that accurately reflect prior radiation exposure. We demonstrate that expression profiles can be ...[more]

PMID: 17407386

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Gene expression signatures that predict radiation exposure (mouse)

Project description:Gene expression analysis of peripheral blood leukocytes (PB MNCs) to develop expression profiles that accurately reflect prior radiation exposure. Keywords: Comparative, exposure dosage, C57BI6 Murine Irradiation Studies We have made use of gene expression analysis of peripheral blood mononuclear cells (PB MNCs) to develop expression profiles that accurately reflect prior radiation exposure. Importantly, we demonstrate that expression profiles can be developed that not only predict radiation exposure in mice but also distinguish the level of radiation exposure, ranging from 50 cGy to 1000 cGy. Likewise, a molecular signature of radiation response developed solely from irradiated human patient samples can predict and distinguish irradiated human PB samples from non-irradiated samples with an accuracy of 90%, sensitivity of 85% and specificity of 94%. We further demonstrate that a radiation profile developed in the mouse can correctly distinguish PB samples from irradiated and non-irradiated human patients with an accuracy of 77%, sensitivity of 82% and specificity of 75%. Taken together, these data demonstrate that molecular profiles can be generated which are highly predictive of different levels of radiation exposure in mice and humans. Mouse Dataset only

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