Pulsed Electromagnetic Fields Protect Against Brain Ischemia by Modulating the Astrocytic Cholinergic Anti-inflammatory Pathway.

Neuroinflammation is one of the most important pathological processes following brain ischemia. Pulsed electromagnetic fields (PEMFs) protect against brain ischemia, but their role in regulating neuroinflammation remains unclear. In the present study, we investigated the biological effects of PEMF exposure on brain ischemia-induced neuroinflammation through the astrocytic cholinergic anti-inflammatory pathway. PEMF exposure reduced the activation of astrocytes and neuroinflammation following brain ischemia by directly modulating astrocytic injury and inflammatory cytokine release. Inhibition of nicotinic acetylcholine receptor alpha 7 subunit (α7nAChR) by a specific antagonist reversed the regulatory effects of PEMF on astrocytes. Furthermore, negative regulation of signal transducer and activator of transcription 3 (STAT3) by α7nAChR was found to be an important downstream mechanism through which PEMF regulates astrocyte-related neuroinflammation. PEMF suppressed STAT3 phosphorylation and nuclear translocation by activating α7nAChR. These results demonstrate that PEMF exerts anti-inflammatory effects in the context of brain ischemia by modulating astrocytic α7nAChR/STAT3 signaling.

Identification of a Fibroblast-Related Prognostic Model in Glioma Based on Bioinformatics Methods.

BACKGROUND: Glioma is the most common primary tumor of the central nervous system with a high lethality rate. This study aims to mine fibroblast-related genes with prognostic value and construct a corresponding prognostic model. METHODS: A glioma-related TCGA (The Cancer Genome Atlas) cohort and a CGGA (Chinese Glioma Genome Atlas) cohort were incorporated into this study. Variance expression profiling was executed via the "limma" R package. The "clusterProfiler" R package was applied to perform a GO (Gene Ontology) analysis. The Kaplan-Meier (K-M) curve, LASSO regression analysis, and Cox analyses were implemented to determine the prognostic genes. A fibroblast-related risk model was created and affirmed by independent cohorts. We derived enriched pathways between the fibroblast-related high- and low-risk subgroups using gene set variation analysis (GSEA). The immune infiltration cell and the stromal cell were calculated using the microenvironment cell populations-counter (MCP-counter) method, and the immunotherapy response was assessed with the SubMap algorithm. The chemotherapy sensitivity was estimated using the "pRRophetic" R package. RESULTS: A total of 93 differentially expressed fibroblast-related genes (DEFRGs) were uncovered in glioma. Seven prognostic genes were filtered out to create a fibroblast-related gene signature in the TCGA-glioma cohort training set. We then affirmed the fibroblast-related risk model via TCGA-glioma cohort and CGGA-glioma cohort testing sets. The Cox regression analysis proved that the fibroblast-related risk score was an independent prognostic predictor in prediction of the overall survival of glioma patients. The fibroblast-related gene signature revealed by the GSEA was applicable to the immune-relevant pathways. The MCP-counter algorithm results pointed to significant distinctions in the tumor microenvironment between fibroblast-related high- and low-risk subgroups. The SubMap analysis proved that the fibroblast-related risk score could predict the clinical sensitivity of immunotherapy. The chemotherapy sensitivity analysis indicated that low-risk patients were more sensitive to multiple chemotherapeutic drugs. CONCLUSION: Our study identified prognostic fibroblast-related genes and generated a novel risk signature that could evaluate the prognosis of glioma and offer a theoretical basis for clinical glioma therapy.