Purpose: Based on the conserved features of radiation response, we integrated the human and plant genomes to identify human ionizing radiation-responsive genes, aiming to identify novel radiation indicators and develop dose reconstruction models for radiation exposure assessment.
Methods and materials: We proposed a method employing homologous gene comparisons between 53 plant species and human genomes to identify the potential human ionizing radiation-responsive genes. Multiple linear regression models (optimized via stepwise regression), lasso regression model, ridge regression model, and elastic net regression model were constructed to predict radiation doses based on the expression profiles of these genes from four independent datasets. Model training and validation were performed using the leave-one-out-cross-validation (LOOCV) approach. The predictive performances were evaluated using the correlation coefficient (R) and root mean square error (RMSE).
Results: We identified a total of 39 plant-based human ionizing radiation-responsive genes as potential radiation indicators, comprising 23 previously known human genes and 16 potential candidates derived from plants. The linear model outperformed the other three models in radiation dose reconstruction across multiple radiation exposure scenarios, as evaluated by the performance metrics R and RMSE. The dose reconstruction models achieved high predictive accuracy for radiation exposure doses in both training and test sets at different dose rate conditions and time points after irradiation.
Conclusions: In conclusion, we identified a panel of human ionizing radiation-responsive genes as promising indicators and developed dose reconstruction models with potential applications in radiation exposure assessment. These findings provide a new strategy for expanding the pool of human ionizing radiation biomarkers and hold promise for improving dose estimation during radiological emergencies.
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