Radiation research最新文献

This study investigated whether therapeutic doses of X rays can affect the expression of mismatch repair (MMR) genes and proteins using Lynch syndrome-associated human colorectal cancer cell lines. MMR-deficient cell lines (HCT116, SW48, LoVo) and an MMR-proficient control cell line (HT29) were exposed to X rays [a 2 Gy dose or 2 Gy daily for five consecutive days (10 Gy)]. Reverse transcription quantitative real-time PCR (RT-qPCR) and Western blotting were used to detect the radiation-induced changes in the expression of RNAs and proteins, respectively. RT-qPCR revealed that MLH1 and MSH6 genes were stably expressed regardless of the MMR status of the cell line and the radiation dose. In contrast, the MSH2 gene was either up-regulated or down-regulated after 2 Gy or 10 Gy or both. The expression of PMS2 increased after 10 Gy irradiation in all MMR-deficient cell lines, even though the data were not statistically significant compared to other doses, except for the LoVo cell line. Protein expression analysed using Western blotting demonstrated that MLH1 protein expression was stable, whereas the expression of MSH2 was significantly affected by radiation exposure in both MLH1-deficient cell lines. No correlation between the expression of RNA and protein could be identified. In conclusion, radiation may have significantly differential effects on MMR RNA and protein expression when different cell lines, doses, and specific genes are considered.

p53 gene mutations are common in various cancers and may provide insights in predicting tumor radiosensitivity. This study aimed to assess the effect of p53 mutations on radiosensitivity using the intrinsic radiosensitivity index (RSI) across publicly available cancer cohorts. Gene expression data, mutation data, and clinical information were obtained from the Cancer Genome Atlas dataset. RSI, calculated from the expression of 10 specific genes, was used to evaluate radiosensitivity. Additional models were used to assess the tumor microenvironment status. p53 mutations were prevalent in several types of cancer. Notably, RSI models indicated reduced predicted radiosensitivity in patients with p53 mutations compared to those without mutations, only in head and neck squamous cell carcinoma (HNSC). In contrast, p53 mutations did not significantly decrease predicted radiosensitivity in other cancers. The association between p53 mutations and the predicted radioresistant phenotype disappeared when the cohort was controlled for p53 and p16 status in HNSC. Similarly, the estimated tumor microenvironment status was unaffected by p53 mutations. These findings suggest that predicted radiosensitivity is more strongly influenced by p16 status than by p53 mutations, indicating that p53 status alone may not be a reliable predictive marker for radiosensitivity in HNSC.

The radiation environment in space consists of a complex mixture of particles and energies that are characteristically different from any natural Earth radiation source. Projections of space radiation cancer risk are obtained by scaling or adjusting epidemiological models derived from terrestrially exposed cohorts to account for differences in radiation quality, dose rate, and other factors. Radiation quality and dose-rate effects introduce significant uncertainty, thereby obfuscating risk communication and hindering the ability to evaluate the efficacy of mitigation strategies such as medical countermeasures. Space radiation quality factors are developed through a multi-step process that requires computational models and experimental data. The first step in this process involves developing dose-response models and fitting them to data from ground-based experiments involving acute irradiation of animals or cells. There is limited ground-based data compared to the range of ions and energies found in space; thus, dose-response models must be able to reproduce available data and predict responses where no data exist. This work focuses on developing a microdosimetric (μD) dose-response model applicable to experimental datasets relevant to space radiation cancer induction. Three experimental datasets, encompassing murine Harderian gland tumorigenesis and chromosome aberrations in human skin fibroblasts and blood lymphocytes, are utilized to demonstrate key features and overall performance of the μD model. The model generates non-linear dose-responses and can predict charge and energy dependence observed in experimental data without the use of empirical functions or corrections. Additionally, the μD model identifies the critical microscopic target population and target size that drive the observed biological effects.

Radiation therapy is one of the most critical methods for the comprehensive treatment of nasopharyngeal carcinoma (NPC). However, radiation resistance limits the effectiveness of radiotherapy. MicroRNAs (miRNAs) are associated with the radiosensitivity of NPC, but their impacts and mechanisms of action require further investigation. Aberrantly expressed miRNAs were screened in NPC and normal tissue. A series of gain-of-function and loss-of-function experiments were conducted to evaluate the biological behavior of miR-144-3p in NPC cells. The role of miR-144-3p in the proliferation and apoptosis of NPC cells was studied. Downstream mechanisms of miR-144-3p were explored through bioinformatics analysis and RNA sequencing, confirmed by dual-luciferase reporter gene assays. We observed downregulation of miR-144-3p in NPC tissue and radiation-resistant cells. Furthermore, upregulation of miR-144-3p in radiation-resistant cells suppressed the enhancement of radiosensitivity in NPC cells. Conversely, inhibiting miR-144-3p decreased radiosensitivity. We also found that miR-144-3p directly targets nuclear factor erythroid 2-related factor 2 (NFE2L2) and inhibits its expression. The results of this study indicate that the miR-144-3p/Nrf2 pathway contributes to reducing the radioresistance of NPC, making it a potential therapeutic target.

Models used to calculate the relative biological effectiveness (RBE) of carbon-ion radiotherapy include the microdosimetric kinetic model (MKM), stochastic MKM (SMKM), repair-misrepair-fixation (RMF) model, and local effect model I (LEM). We compared the sensitivities of these models to variations in input biological and reference parameters. We used Monte Carlo simulations of clinically realistic carbon-ion beams incident on a phantom and scored input parameters for RBE models (kinetic energy, microdosimetric spectra, double-strand break yield, and physical dose). We combined data with cell- and model-specific parameters to calculate the linear (α) and quadratic (β) components of the carbon-ion beam, which were used along with the reference α and β values and dose to calculate RBE. Model sensitivity to parameters was quantified by statistically introducing uncertainty into independent parameters and sampling the resultant RBE. To assess histological differences contributing to variations in the RBE, we also used various reference cell lines. We recalculated the RBE using different reported datasets within individual cell lines to compare inter- and intra-cell line variability. The variability introduced by inherent measurement and estimation uncertainty was typically 26% for the microdosimetric models, 25% for the RMF model, and 30% for the LEM at the 1-σ level. The variability across cell lines, which averaged 27% for the microdosimetric models and 2.5% for the RMF model, was similar to the intra-cell line variability in the RBE as calculated with unique datasets for an individual cell line. While the focus is largely on comparing models, the results of this study indicate that the variation in RBE within each model, based solely on reference parameters, is substantial. Our findings indicate that the selection of input parameters is of comparable importance to the choice of cell line and even the RBE model. This study provides insight into model robustness and emphasizes the need for continued computational and in-vitro RBE research.

Human occupational exposure to ionizing radiation has been linked to increased risks of developing cancers, including solid tumors. In particular, 239Pu, used in the production of nuclear weapons, has been associated with a higher risk of malignancies of the lungs, liver, and bones, but the specific patterns of malignant histology have not been well described in humans. We assessed the pathological characteristics of liver cancers that occurred in a Russian cohort of nuclear workers from the Mayak Production Association, with a special emphasis on angiosarcoma, and studied the relationships between dosimetry, sex, and histology. The subjects included two main groups of workers whose biological specimens were collected during autopsies: thirty-one were diagnosed with liver cancers (cases), and 38 workers were cancer-free (controls). An independent pathologist reviewed all liver tissues from these cancer cases and performed immunohistochemistry to confirm the diagnoses (angiosarcoma, hepatocellular carcinoma, or cholangiocarcinoma). A third group consisted of 36 workers who developed liver cancer but for whom no biological samples were available. Radiation dose levels, along with sex and age distributions, were compared statistically among the three types of liver tumors and the control groups. There was a predominance of females (9 of 13, 69%) among the workers who developed angiosarcoma of the liver, whereas a male predominance characterized both hepatocellular carcinoma (9 of 9, 100%) and cholangiocarcinoma (8 of 9, 89%). A male predominance was also observed in the group of workers with liver cancer but without biological samples (22 of 36, 61%) and in the group of workers without liver cancer (30 of 38, 79%). Occupational differences were evident, with angiosarcoma patients who had biological samples representing the largest proportion (9 of 13) of plutonium metallurgical plant workers (the most highly exposed occupation to plutonium in the cohort), while the remainder (4 of 13) occurred among the radiochemical plant workers. Compared to other groups, those workers with biological samples who developed angiosarcoma had the largest accumulated and widest range of external doses absorbed by the liver, as well as the highest absorbed doses of 239Pu to the liver. Females with biological samples who developed liver cancer also had some of the highest accumulated doses from 239Pu, exceeding 1 Gy in some instances. Our observations of histology, sex, occupation, and dose patterns provide possible clues to the unusual pattern of liver malignancies, particularly angiosarcoma, related to aspects of plutonium exposure.

This workshop examined the effects of ionizing radiation on certain understudied populations, including pregnant/lactating, in utero, pediatric, and geriatric individual. Research using animal models has revealed significant age- and condition-related differences in radiation-induced injuries, highlighting the need for tailored triage and treatment strategies. Historical data from Hiroshima, Nagasaki, and Chernobyl further support these findings, demonstrating that radiation effects lead to wide-ranging issues with unique profiles during pregnancy, childhood and elderly age. While some research has been conducted on these groups, ethical and logistical challenges make it difficult to study these populations extensively. Therefore, developing alternative approaches that offer promising avenues for further research is critical. Radiation-induced biomarkers and biodosimetry also show age-related differences, including distinctive metabolic disruptions, necessitating further validation of biodosimetry tools. These findings emphasize the importance of considering age, sex, and demographic factors in preclinical and clinical radiation research to develop treatments that improve outcomes of understudied populations after a radiological or nuclear public health emergency.

Accurate prediction of symptomatic radiation therapy-induced pneumonitis (RT-IP) remains an important clinical challenge. Currently, mean lung dose and volume of the lungs receiving a 20 Gy threshold of ≤20 Gy and ≤35%, respectively, are utilized to reduce the incidence of pneumonitis to 20%. However, its occurrence is not entirely predictable even at the recommended threshold levels. Hence, in this study, we aimed to evaluate several biological markers, specifically chromosome aberrations by peptide nucleic acid fluorescence in situ hybridization (PNA-FISH), γH2AX, serum IL-6, and IL-17, as potential predictors of symptomatic (grade ≥2) radiation therapy-induced pneumonitis. We prospectively enrolled patients with locally advanced lung cancer. Peripheral blood samples were collected from eleven patients before, during (2 Gy, 20 Gy, 60/66 Gy), and one month after chemoradiotherapy. We then compared these biomarkers between overreactors (grade ≥2 RT-IP) and non-overreactors (grade 0 to 1 RT-IP). Our findings show that chromosome aberration frequency, serum IL-6, and IL-17 after 20 Gy are higher in overreactors than in non-overreactors. Moreover, overreactors accumulated more complex aberrations, such as tricentrics, quadricentrics, and quintacentrics. While chromosome aberration frequency correlated with mean lung dose and IL-17, a pneumonitis marker, IL-6 correlated with the irradiated volume after 20 Gy. Receiver operating characteristic curve analysis further showed that chromosome aberration frequency and IL-6 have the highest specificity for predicting grade ≥2 RT-IP among the assays. In conclusion, we demonstrated the superior predictive capability of PNA-FISH-based chromosome aberration frequency and serum IL-6 for radiation therapy-induced pneumonitis in lung cancer patients. This supports the usefulness of these biomarkers for predicting radiation therapy-induced pneumonitis.

The DOE-NIH Joint Workshop on Computational Modeling to Advance Novel Medical Isotopes for Radiotheranostics, held on September 27, 2024, brought together experts from government, academia, and industry to address critical challenges in radionuclide production and clinical translation. The workshop emphasized interdisciplinary collaboration, particularly between the Department of Energy (DOE) and the National Institutes of Health (NIH), to strengthen the domestic isotope supply, streamline regulatory pathways, and further integrate computational tools into radiopharmaceutical therapy (RPT). Key discussions explored the role of AI-driven modeling, machine learning, and digital twin technologies in optimizing dosimetry, dynamically personalizing treatments, and reducing time to clinical adoption. Advances in predictive computational modeling were highlighted as essential for improving radionuclide yield, purity, and synthesis efficiency. Regulatory considerations and equitable access were central themes, with participants advocating for harmonized global standards, adaptive trial designs, and expanded infrastructure for clinical implementation. DOE computational and production infrastructure was emphasized. Future priorities identified include increased investment in radionuclide production infrastructure, expanded workforce development in radiopharmaceutical sciences and computational modeling, and the creation of robust public-private partnerships. The workshop concluded that continued strategic collaboration and sustained resources will be vital for advancing next-generation radiotheranostics, ensuring safe and effective therapies accessible to all patients.

Radiation damage and deposition caused by radiological or nuclear public health incidents (e.g., accidents or attacks) may lead to acute radiation syndrome and other complications. Accurate and effective radiation dose assessment is necessary for triaging irradiated patients and determining treatment plans. However, there is no systematic evaluation of whether radiation biodosimetry is affected by comorbidities. The weighted gene co-expression network analysis (WGCNA) and differentially expressed genes (DEG) co-analysis of the RNA-sequencing data in human peripheral blood after irradiation from the Gene Expression Omnibus (GEO) database identified seven radiation-specific genes, including five upregulated genes and two downregulated genes. Five radiation-specific genes (CCNG1, CDKN1A, GADD45A, GZMB, PHLDA3) showed a strong linear correlation with the total-body X-ray radiation model. The above five genes were used to validate further several radiation combined injury models, including infection, trauma, and burns, while considering different sexes and ages in animal studies on the radiation response from 0 to 10 Gy. The receiving operator characteristic (ROC) curve analysis revealed that the CCNG1 and CDKN1A genes performed the best in radiation dose-response across both mice and humans. Moreover, the CCNG1 protein could accurately predict the absorbed doses for up to 28 days after exposure (>95%). Our findings suggested that the CCNG1 and CDKN1A mRNA performed optimally in radiation dose response, independent of trauma, burns, age, and sex. Additionally, the CCNG1 protein revealed a strong linear correlation between radiation dose and time postirradiation. Our study demonstrated the potential feasibility of using CCNG1 and CDKN1A as injury biomarkers in radiation accident management.