Glioblastoma (GBM) is the most common intracranial tumor with characteristic of malignancy. Resveratrol, a natural originated polyphenolic compound, has been reported to act as a potential radiosensitizer in cancer therapy. Magnetic resonance imaging (MRI) is the first choice for the diagnosis, pathological grading, and efficacy evaluation of GBM. In this study, MRI was applied to observe whether resveratrol could intensify the anti-GBM tumor effect by enhancing antitumor immunity during radiotherapy. We established an intracranial C6 GBM model in SD rats, treated with radiation and resveratrol. The increased body weight, the inhibition on mortality, and tumor volume in radiated- GBM rats were further enhanced by resveratrol addition, while the pathological damage of brain was alleviated. The modulation of radiation on inflammation, cell cycle, and apoptosis was strengthened by resveratrol; and Ki-67, PD-L1, and cell cycle- and apoptosis-related protein expressions were also improved by cotreatment. Besides, cotreatment attenuated DNA damage and induced G0/G1-phase cell arrest of GBM rats, accompanied with the changed expression of ATM-AKT-STAT3 pathway-related proteins. Moreover, the percentages of CD3+CD8+T cells and IFN-γ+CD8+T cells were enhanced, while (CD4+CD25+Foxp3)/CD4+T cells were decreased by radiation or resveratrol, which was strengthened by cotreatment. The modulation effect of cotreatment on CD3, Foxp3, and IFN-γ levels was also stronger than radiation or resveratrol alone. To conclude, resveratrol enhanced the effect of radiotherapy by inducing DNA damage and antitumor immunity in the intracranial C6 GBM.
This article has been peer reviewed and published immediately upon acceptance.It is an open access article, which means that it can be downloaded, printed, and distributed freely, provided the work is properly cited. Articles in "Folia Histochemica et Cytobiologica" are listed in PubMed.
Objectives To develop and validate a diagnostic scoring system to differentiate intrahepatic mass-forming cholangiocarcinoma (IMCC) from solitary colorectal liver metastasis (CRLM). Methods A total of 366 patients (263 in the training cohort, 103 in the validation cohort) who underwent MRI examination with pathologically proven either IMCC or CRLM from two centers were included. Twenty-eight MRI features were collected. Univariate analyses and multivariate logistic regression analyses were performed to identify independent predictors for distinguishing IMCC from solitary CRLM. The independent predictors were weighted over based on regression coefficients to build a scoring system. The overall score distribution was divided into three groups to show the diagnostic probability of CRLM. Results Six independent predictors, including hepatic capsular retraction, peripheral hepatic enhancement, vessel penetrating the tumor, upper abdominal lymphadenopathy, peripheral washout at the portal venous phase, and rim enhancement at the portal venous phase were included in the system. All predictors were assigned 1 point. At a cutoff of 3 points, AUCs for this score model were 0.948 and 0.903 with sensitivities of 96.5% and 92.0%, specificities of 84.4% and 71.7%, positive predictive values of 87.7% and 75.4%, negative predictive values of 95.4% and 90.5%, and accuracies of 90.9% and 81.6% for the training and validation cohorts, respectively. An increasing trend was shown in the diagnostic probability of CRLM among the three groups based on the score. Conclusions The established scoring system is reliable and convenient for distinguishing IMCC from solitary CRLM using six MRI features. Clinical relevance statement A reliable and convenient scoring system was developed to differentiate between intrahepatic mass-forming cholangiocarcinoma from solitary colorectal liver metastasis using six MRI features. Key Points • Characteristic MRI features were identified to distinguish intrahepatic mass-forming cholangiocarcinoma (IMCC) from solitary colorectal liver metastasis (CRLM). • A model to distinguish IMCC from solitary CRLM was created based on 6 features, including hepatic capsular retraction, upper abdominal lymphadenopathy, peripheral washout at the portal venous phase, rim enhancement at the portal venous phase, peripheral hepatic enhancement, and vessel penetrating the tumor.
Colon cancer (CC) is a malignancy of the digestive tract, and computed tomography (CT)-guided radiofrequency ablation (RFA) has been extensively adopted in cancer treatment. We aimed to explore the changes in fecal metabolism after CT-guided RFA in CC mice. The orthotopic CC mice received CT-guided RFA upon modeling. Subsequently, we quantified tumor volumes and weights to assess treatment efficacy. Next, because metabolomics is useful for evaluating therapeutic validity, feces were collected for metabolomics analysis. CT-guided RFA inhibited tumor growth effectively. Additionally, metabolomics results showed that the contents of bile acids and fatty acids were downregulated in CC mouse feces. Moreover, the levels of amino acids and carbohydrates were decreased while the levels of fatty acids, organic acids, phenols, pyridines and short-chain fatty acids were elevated in feces after CC mice received CT-guided RFA. Pathway enrichment analysis revealed that those differential metabolites were closely related to fatty acids degradation and synthesis.CT-guided RFA possesses a strong ability to suppress CC development in mice, accompanied by a significant increase of fatty acid content in feces. This study proposes a novel approach and target for CC treatment, which provides hope for CC patients and establishes a solid basis for future in-depth studies.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.