Identifying radiation exposure biomarkers from mouse blood transcriptome

  • Authors:

  • Daniel R. Hyduke;Evagelia C. Laiakis;Heng-Hong Li;Albert J. Fornace Jr.

  • Affiliations:

  • Department of Biochemistry and Molecular and Cellular Biology, and Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, Washington, DC 20057, USA/ John B. Little Center for ...;Department of Biochemistry and Molecular and Cellular Biology, and Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, Washington, DC 20057, USA;Department of Biochemistry and Molecular and Cellular Biology, and Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, Washington, DC 20057, USA/ John B. Little Center for ...;Department of Biochemistry and Molecular and Cellular Biology, and Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, Washington, DC 20057, USA/ John B. Little Center for ...

  • Venue:
  • International Journal of Bioinformatics Research and Applications
  • Year:
  • 2013

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Abstract

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.