Comparative RNA-Seq transcriptome analyses reveal dynamic time-dependent effects of 56Fe, 16O, and 28Si irradiation on the induction of murine hepatocellular carcinoma

PRECLINICAL MODELS OF LIVER CÂNCER

•In this review, we described different murine models of carcinogenesis: classic models, new transgenic and combined models, that reproduce the key points for HCC and CCA genesis allowing a better understanding of its genetic physiopathological, and environmental abnormalities. •Each model has its advantages, disadvantages, similarities, and differences with the corresponding human disease and should be chosen according to the specificity of the study. Ultimately, those models can also be used for testing new anticancer therapeutic approaches. •Cholangiocarcinoma has been highlighted, with an increase in prevalence. This review has an important role in understanding the pathophysiology and the development of new drugs. Background - This manuscript provides an overview of liver carcinogenesis in murine models of hepatocellular carcinoma (HCC) and cholangiocarcinoma (CCA). Objective - A review through MEDLINE and EMBASE was performed to assess articles until August 2022.Methods - Search was conducted of the entire electronic databases and the keywords used was HCC, CCA, carcinogenesis, animal models and liver. Articles exclusion was based on the lack of close relation to the subject. Carcinogenesis models of HCC include HCC induced by senescence in transgenic animals, HCC diet-induced, HCC induced by chemotoxicagents, xenograft, oncogenes, and HCC in transgenic animals inoculated with B and C virus. The models of CCA include the use of dimethylnitrosamine (DMN), diethylnitrosamine (DEN), thioacetamide (TAA), and carbon tetrachloride (CCl4). CCA murine models may also be induced by: CCA cells, genetic manipulation, Smad4, PTEN and p53 knockout, xenograft, and DEN-left median bile duct ligation. Results - In this review, we described different murine models of carcinogenesis that reproduce the key points for HCC and CCA genesis allowing a better understanding of its genetic, physiopathological, and environmental abnormalities. Conclusion - Each model has its advantages, disadvantages, similarities, and differences with the corresponding human disease and should be chosen according to the specificity of the study. Ultimately, those models can also be used for testing new anticancer therapeutic approaches.

Development of a machine learning framework for radiation biomarker discovery and absorbed dose prediction

Background

Molecular radiation biomarkers are an emerging tool in radiation research with applications for cancer radiotherapy, radiation risk assessment, and even human space travel. However, biomarker screening in genome-wide expression datasets using conventional tools is time-consuming and underlies analyst (human) bias. Machine Learning (ML) methods can improve the sensitivity and specificity of biomarker identification, increase analytical speed, and avoid multicollinearity and human bias.

Aim

To develop a resource-efficient ML framework for radiation biomarker discovery using gene expression data from irradiated normal tissues. Further, to identify biomarker panels predicting radiation dose with tissue specificity.

Methods

A strategic search in the Gene Expression Omnibus database identified a transcriptomic dataset (GSE44762) for normal tissues radiation responses (murine kidney cortex and medulla) suited for biomarker discovery using an ML approach. The dataset was pre-processed in R and separated into train and test data subsets. High computational cost of Genetic Algorithm/k-Nearest Neighbor (GA/KNN) mandated optimization and 13 ML models were tested using the caret package in R. Biomarker performance was evaluated and visualized via Principal Component Analysis (PCA) and dose regression. The novelty of ML-identified biomarker panels was evaluated by literature search.

Results

Caret-based feature selection and ML methods vastly improved processing time over the GA approach. The KNN method yielded overall best performance values on train and test data and was implemented into the framework. The top-ranking genes were Cdkn1a, Gria3, Mdm2 and Plk2 in cortex, and Brf2, Ccng1, Cdkn1a, Ddit4l, and Gria3 in medulla. These candidates successfully categorized dose groups and tissues in PCA. Regression analysis showed that correlation between predicted and true dose was high with R² of 0.97 and 0.99 for cortex and medulla, respectively.

Conclusion

The caret framework is a powerful tool for radiation biomarker discovery optimizing performance with resource-efficiency for broad implementation in the field. The KNN-based approach identified Brf2, Ddit4l, and Gria3 mRNA as novel candidates that have been uncharacterized as radiation biomarkers to date. The biomarker panel showed good performance in dose and tissue separation and dose regression. Further training with larger cohorts is warranted to improve accuracy, especially for lower doses.

Carcinogenesis induced by space radiation: A systematic review

The carcinogenic risk from space radiation has always been a health risk issue of great concern during space exploration. In recent years, a large number of cellular and animal experiments have demonstrated that space radiation, composed of high-energy protons and heavy ions, has shown obvious carcinogenicity. However, different from radiation on Earth, space radiation has the characteristics of high energy and low dose rate. It is rich in high-atom-number and high-energy particles and, as it is combined with other space environmental factors such as microgravity and a weak magnetic field, the study of its carcinogenic effects and mechanisms of action is difficult, which leads to great uncertainty in its carcinogenic risk assessment. Here, we review the latest progress in understanding the effects and mechanisms of action related to cell transformation and carcinogenesis induced by space radiation in recent years and summarize the prediction models of cancer risk caused by space radiation and the methods to reduce the uncertainty of prediction to provide reference for the research and risk assessment of space radiation.

Heavy-Ion-Induced Lung Tumors: Dose- & LET-Dependence

There is a limited published literature reporting dose-dependent data for in vivo tumorigenesis prevalence in different organs of various rodent models after exposure to low, single doses of charged particle beams. The goal of this study is to reduce uncertainties in estimating particle-radiation-induced risk of lung tumorigenesis for manned travel into deep space by improving our understanding of the high-LET-dependent dose-response from exposure to individual ion beams after low particle doses (0.03–0.80 Gy). Female CB6F1 mice were irradiated with low single doses of either oxygen, silicon, titanium, or iron ions at various energies to cover a range of dose-averaged LET values from 0.2–193 keV/µm, using ¹³⁷Cs γ-rays as the reference radiation. Sham-treated controls were included in each individual experiment totally 398 animals across the 5 studies reported. Based on power calculations, between 40–156 mice were included in each of the treatment groups. Tumor prevalence at 16 months after radiation exposure was determined and compared to the age-matched, sham-treated animals. Results indicate that lung tumor prevalence is non-linear as a function of dose with suggestions of threshold doses depending on the LET of the beams. Histopathological evaluations of the tumors showed that the majority of tumors were benign bronchioloalveolar adenomas with occasional carcinomas or lymphosarcomas which may have resulted from metastases from other sites.

Effects of Low Dose Space Radiation Exposures on the Splenic Metabolome

Future space missions will include a return to the Moon and long duration deep space roundtrip missions to Mars. Leaving the protection that Low Earth Orbit provides will unavoidably expose astronauts to higher cumulative doses of space radiation, in addition to other stressors, e.g., microgravity. Immune regulation is known to be impacted by both radiation and spaceflight and it remains to be seen whether prolonged effects that will be encountered in deep space can have an adverse impact on health. In this study, we investigated the effects in the overall metabolism of three different low dose radiation exposures (γ-rays, 16O, and 56Fe) in spleens from male C57BL/6 mice at 1, 2, and 4 months after exposure. Forty metabolites were identified with significant enrichment in purine metabolism, tricarboxylic acid cycle, fatty acids, acylcarnitines, and amino acids. Early perturbations were more prominent in the γ irradiated samples, while later responses shifted towards more prominent responses in groups with high energy particle irradiations. Regression analysis showed a positive correlation of the abundance of identified fatty acids with time and a negative association with γ-rays, while the degradation pathway of purines was positively associated with time. Taken together, there is a strong suggestion of mitochondrial implication and the possibility of long-term effects on DNA repair and nucleotide pools following radiation exposure.