• FTO was upregulated in radioresistant NPC and enhances radioresistance of NPC by repressing radiation-induced ferroptosis. • FTO erases the m6A modification of the OTUB1 transcript and promote the expression of OTUB1 in NPC. • FTO inhibitor FB23-2 and the ferroptosis activator erastin altered NPC responsiveness to radiotherapy.

Stereotactic ablative radiotherapy (SABR) shows a remarkable local control of non-small cell lung cancer (NSCLC) metastases, partially as a result of host immune status. However, the predictors of immune cells for tumor response after SABR are unknown. To that effect, we investigated the ability of pre-SABR immune cells in peripheral blood to predict early tumor response to SABR in patients with lung metastases from NSCLC. This study included 70 patients with lung metastases from NSCLC who were undergoing SABR. We evaluated the early tumor response 1 month and 6 months after SABR in these patients following RECIST 1.1 guidelines. Pre-SABR peripheral CD8+ T cell count, CD8+CD28+ T-cell count, CD8+CD28− T-cell count, CD4+ T-cell count, and Treg-cell count were measured using flow cytometry. Increased CD8+CD28+ T-cell counts (14.43 ± 0.65 vs. 10.21 ± 0.66; P = 0.001) and CD4/Treg ratio (16.96 ± 1.76 vs. 11.91 ± 0.74; P = 0.011) were noted in 1-month responsive patients, compared with non-responsive patients. In univariate logistic analyses, high CD8+CD28+ T-cell counts (OR 0.12, 95% CI 0.03–0.48; P = 0.003), CD4/Treg ratio (OR 0.24, 95% CI 0.06–0.90; P = 0.035), and BED10 (OR 0.91, 95% CI 0.84–0.99; P = 0.032) predicted a 1-month tumor response to SABR. According to multivariate logistic analyses, the CD8+CD28+ T-cell count predicted a 1-month tumor response to SABR (OR 0.19, 95% CI 0.04–0.90; P = 0.037) independently. Furthermore, we confirmed the independent predictive value of the CD8+CD28+ T-cell count in predicting tumor response to SABR in 41 patients 6 months after treatment (OR 0.08, 95% CI 0.01–0.85; P = 0.039). A pre-SABR CD8+CD28+ T-cell count could predict early tumor response to SABR in patients with lung metastases from NSCLC. Larger, independently prospective analyses are warranted to verify our findings.

Objective: Transfer RNA-derived small RNAs (tsRNAs) are a novel type of non-coding RNA with various regulatory functions. They are associated with oxidative stress in various diseases, but their potential functions in radiation-induced lung injury (RILI) remain uncertain. Methods: To explore the role of tsRNAs in RILI, we used X-rays to irradiate human bronchial epithelial cells and examined the expression profile of altered tsRNAs by RNA sequencing and bioinformatics analysis. Sequencing results were verified by qRT-PCR. tsRNA functions were explored using several methods, including CCK-8, reactive oxygen species (ROS) assays, cell transfection, and western blotting. Results: Eighty-six differentially expressed tRNA-derived fragments (tRFs) were identified: 64 were upregulated, and 22 were downregulated. Among them, the regulation of tRF-Gly-GCC, associated with oxidative stress, may be mediated by the inhibition of cell proliferation, promotion of ROS production, and apoptosis in the occurrence and development of RILI. A Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis suggested that the underlying molecular mechanism may involve the PI3K/AKT and the FOXO1 signaling pathways. Conclusion: Our findings provide new insights into the molecular mechanisms underpinning RILI, advancing the clinical prevention and treatment of this disease.

An increasing number of nasopharyngeal carcinoma (NPC) patients benefit from immunotherapy with chemotherapy as an induction treatment. Currently, there isn't a reliable method to assess the efficacy of this regimen, which hinders informed decision-making for follow-up care. To establish and evaluate a model for predicting the efficacy of programmed death-1 (PD-1) inhibitor combined with GP (gemcitabine and cisplatin) induction chemotherapy based on deep learning features (DLFs) and radiomic features. Ninety-nine patients diagnosed with advanced NPC were enrolled and randomly divided into training set and test set in a 7:3 ratio. From MRI scans, DLFs and conventional radiomic characteristics were recovered. The random forest algorithm was employed to identify the most valuable features. A prediction model was then created using these radiomic characteristics and DLFs to determine the effectiveness of PD-1 inhibitor combined with GP chemotherapy. The model's performance was assessed using Receiver Operating Characteristic (ROC) curve analysis, area under the curve (AUC), accuracy (ACC), and negative predictive value (NPV). Twenty-one prediction models were constructed. The Tf_Radiomics+Resnet101 model, which combines radiomic features and DLFs, demonstrated the best performance. The model's AUC, ACC, and NPV values in the training and test sets were 0.936 (95%CI: 0.827-1.0), 0.9, and 0.923, respectively. The Tf_Radiomics+Resnet101 model, based on MRI and Resnet101 deep learning, shows a high ability to predict the clinically complete response (cCR) efficacy of PD-1 inhibitor combined with GP in advanced NPC. This model can significantly enhance the treatment management of patients with advanced NPC.

Background: Overexpression of the membrane protein SEC61 translocon gamma subunit (SEC61G) has been observed in a variety of cancers; however, its role in head and neck squamous cell carcinomas (HNSCC) is unknown. This study aimed to elucidate the relationship between SEC61G and HNSCC based on data from The Cancer Genome Atlas (TCGA) database.