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. 2014 Sep 5:4:6293.
doi: 10.1038/srep06293.

Identification of gene expression biomarkers for predicting radiation exposure

Tzu-Pin Lu  1 , Yi-Yao Hsu  2 , Liang-Chuan Lai  3 , Mong-Hsun Tsai  4 , Eric Y Chuang  5
Affiliations

Identification of gene expression biomarkers for predicting radiation exposure

Tzu-Pin Lu et al. Sci Rep. .

Abstract

A need for more accurate and reliable radiation dosimetry has become increasingly important due to the possibility of a large-scale radiation emergency resulting from terrorism or nuclear accidents. Although traditional approaches provide accurate measurements, such methods usually require tedious effort and at least two days to complete. Therefore, we provide a new method for rapid prediction of radiation exposure. Eleven microarray datasets were classified into two groups based on their radiation doses and utilized as the training samples. For the two groups, Student's t-tests and resampling tests were used to identify biomarkers, and their gene expression ratios were used to develop a prediction model. The performance of the model was evaluated in four independent datasets, and Ingenuity pathway analysis was performed to characterize the associated biological functions. Our meta-analysis identified 29 biomarkers, showing approximately 90% and 80% accuracy in the training and validation samples. Furthermore, the 29 genes significantly participated in the regulation of cell cycle, and 19 of them are regulated by three well-known radiation-modulated transcription factors: TP53, FOXM1 and ERBB2. In conclusion, this study demonstrates a reliable method for identifying biomarkers across independent studies and high and reproducible prediction accuracy was demonstrated in both internal and external datasets.

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Figures

Figure 1
Figure 1. Flowchart for identification of differentially expressed genes associated with radiation doses and development of a prediction model.
The number of genes shown in the right dotted box denotes the union of genes across multiple signatures.
Figure 2
Figure 2. Prediction performance of the three sets of biomarkers.
A 10-fold cross-validation was repeated 10,000 times and the accuracies in the samples treated with higher and/or lower radiation doses were plotted. (A) Training samples (46 higher-dose and 34 lower-dose). (B) External independent datasets (64 higher-dose and 30 lower-dose).
Figure 3
Figure 3. Gene-gene interaction networks of the 29 biomarkers.
Three possible upstream regulators were enriched. Direct evidence between two genes from previous literature reports is shown as a solid line and indirect evidence is depicted as a dashed line.
See this image and copyright information in PMC

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