Microarray analysis of hub genes, non-coding RNAs and pathways in lung after whole body irradiation in a mouse model.

Overview

abstract

Purpose: Previous research has highlighted the impact of radiation damage, with cancer patients developing acute disorders including radiation induced pneumonitis or chronic disorders including pulmonary fibrosis months after radiation therapy ends. We sought to discover biomarkers that predict these injuries and develop treatments that mitigate this damage and improve quality of life.Materials and Methods: Six- to eight-week-old female C57BL/6 received 1, 2, 4, 8 12 Gy or sham whole body irradiation. Animals were euthanized 48 hours post exposure and lungs removed, snap frozen and underwent RNA isolation. Microarray analysis was performed to determine dysregulation of messenger RNA (mRNA), microRNA (miRNA), and long non-coding RNA (lncRNA) after radiation injury.Results: We observed sustained dysregulation of specific RNA markers including: mRNAs, lncRNAs, and miRNAs across all doses. We found gene dysregulation which can be used to develop a decision tree model to identify no-exposure vs radiation exposure which includes Hba and Hbb mRNA that were dysregulated even at 1 Gy. We also identified significantly upregulated genes that can indicate high dose exposure, including Cpt1c, Pdk4, Gdf15, and Eda2r, which are markers of senescence and fibrosis. Only three miRNAs were significantly dysregulated across all radiation doses: miRNA-142-3p and miRNA-142-5p were downregulated and miRNA-34a-5p was upregulated. IPA analysis predicted inhibition of several molecular pathways with increasing doses of radiation, including: T cell development, Quantity of leukocytes, Quantity of lymphocytes, and Cell viability.Conclusions: These RNA biomarkers might be highly relevant in the development of treatments and in predicting normal tissue injury in patients undergoing radiation treatment. We are conducting further experiments in our laboratory, which includes a human lung-on-a-chip model, to develop a decision tree model using RNA biomarkers.