RESUMEN
Background: Stroke is a common and damaging disease of brain tissue, and has high morbidity, disability, and mortality rates. Ozone (O3) is an isomer of oxygen and can be applied to ozonate the isolated blood in specific containers outside the body and return it to the body. O3 can also alter the activity and function of multiple cellular components, thus affecting blood viscosity and altering hemodynamics. However, the question of whether O3 has clinical value in the treatment of stroke requires further investigation. This study sought to evaluate the protective effect and possible mechanism of O3 in brain injury after stroke. Methods: First, oxygen-glucose deprivation/reoxygenation (OGD/R)-induced human glioblastoma cell (A172) and middle cerebral artery occlusion (MCAO) rat models were established. Second, the levels of the associated ribonucleic acids and proteins were analyzed using reverse-transcription real-time-quantitative polymerase chain reaction, Western Blot, or immunofluorescence assays. Third, the concentration of glutamate and lactate dehydrogenase (LDH) were assessed using enzyme-linked immunoassays. Results: The results showed that glial fibrillary acidic protein was upregulated in the OGD/R A172 cells. O3 decreased LDH and increased glutamate levels in the OGD/R A172 cells, which suggests that O3 reduced brain damage in the in vitro stroke model. We also showed that O3 attenuated brain infarction in the in-vivo stroke model. Further, we found that O3 alleviated stroke-induced brain damage by reducing the apoptosis of astrocytes. Further, the B-cell lymphoma 2 inhibitor propofol alleviated stroke-induced brain damage. Conclusions: Thus, O3 notably alleviated stroke-induced brain damage by inhibiting the apoptosis of astrocytes in the OGD/R-induced human glioblastoma cell and MACO rat models.
RESUMEN
Background: Low-grade gliomas (LGG) account for 20-25% of all gliomas. In this study, we assessed whether metabolic status was correlated with clinical outcomes in LGG patients using data from The Cancer Genome Atlas (TCGA). Methods: LGG patient data were collected from TCGA, and the Molecular Signature Database was used to extract gene sets related to energy metabolism. After performing a consensus-clustering algorithm, the LGG patients were divided into four clusters. We then compared the tumor prognosis, function, immune cell infiltration, checkpoint proteins, chemo-resistance, and cancer stem cells (CSC) between the two groups with the greatest prognostic difference. Using least absolute shrinkage and selection operator (LASSO) analysis, an energy metabolism-related signature was further developed. Results: Energy metabolism-related signatures were applied to identify four clusters (C1, C2, C3, and C4) using a consensus-clustering algorithm. C1 LGG patients were more related to the synapse and had higher CSC scores, more chemo-resistance, and a better prognosis. C4 LGG was observed to have more immune-related pathways and better immunity. We then identified six energy metabolism-related genes (PYGL, HS3ST3B, NNMT, FMOD, CHST6, and B3GNT7) that can accurately predict LGG prognosis not only as a whole but also based on the independent predictions of each of these six genes. Conclusions: The energy metabolism-related subtypes of LGG were identified, which were strongly related to the immune microenvironment, immune checkpoint proteins, CSCs, chemo-resistance, prognosis, and LGG advancement. A signature of genes involved in energy metabolism could help to distinguish and predict the prognosis of LGG patients, and a promising method to discover patients that may benefit from LGG therapy.
