The present study aimed to explore the expression profiles of circular RNAs (circRNAs) in glioblastoma multiforme (GBM) in an attempt to identify potential core genes in the pathogenesis of this tumor. Differentially expressed circRNAs were screened between tumor tissues from five GBM patients and five normal brain samples using Illumina Hiseq. Bioinformatics analysis was used to analyze their potential function. CircBRAF was further detected in different WHO grades glioma tissues and normal brain tissues. Kaplan-Meier curves and multivariate Cox's analysis were used to analyze the association between circBRAF expression level and prognosis of glioma patients. A total of 1411 differentially expressed circRNAs were identified in GBM patients including 206 upregulated circRNAs and 1205 downregulated circRNAs. Differential expression of circRNAs was closely associated with the biological process and molecular function. The downregulated circRNAs were mainly associated with ErbB and Neurotrophin signaling pathways. Moreover, the expression level of circBRAF in normal brain tissues was significantly higher than that in glioma tissues (P<.001). CircBRAF was significantly lower in glioma patients with high pathological grade (WHO III & IV) than those with low grade (WHO I & II) (P<.001). Cox analysis revealed that high circBRAF expression was an independent biomarker for predicting good progression-free survival and overall survival in glioma patients (HR=0.413, 95% CI 0.201-0.849; HR=0.299, 95% CI 0.135-0.661; respectively). The present study identified a profile of dysregulated circRNAs in GBM. Bioinformatics analysis showed that dysregulated circRNAs might be associated with tumorigenesis and development of GBM. In addition, circBRAF could severe as a biomarker for predicting pathological grade and prognosis in glioma patients.

Solid solutions (LaNbON ₂ ) _1−x (LaMg _2/3 Nb _1/3 O ₃ ) _x (0.0 ≤ x ≤ 1.0) show promising photocatalytic activity for water oxidation into oxygen under visible light illumination ( λ ≥ 420 nm).

CaIn₂S₄–In₂O₃hybrid fibers deliver exceptionally high photocatalytic activity for bacterial inactivation due to a strong interplay between CaIn₂S₄and In₂O₃.

Background Methylation-related signatures play crucial roles in tumorigenesis and progression. However, their roles in the immune response in primary glioblastoma (GBM) remains unclear. Methods We analyzed the differential expression of specific members of T cell exhaustion-related pathways in GBM from the perspective of T cell exhaustion. We further screened for significantly negatively correlated methylation sites as candidate methylation markers for T cell exhaustion. Using consensus clustering, we divided the samples into two categories with significant differences in overall survival (OS). We then performed univariate and multivariate Cox regression analyses to construct the T Cell Exhaustion Methylation (TEXM) signature. Finally, we confirmed that this signature served as an independent prognostic factor, and further characterized it in terms of drug resistance and immunotherapy. Results We identified 95 significantly differentially expressed T cell exhaustion-related genes and 51 methylation markers associated with T cell exhaustion. The cancer samples were classified according to methylation site markers, thus indicating two subtypes with significant differences in OS: subtype A and subtype B. Tumor scores, stromal scores, tumor purity, and ESTIMATE scores all showed significant differences between subtypes (P < 0.05). Univariate Cox regression analysis identified five methylation sites significantly associated with OS, and multivariate Cox regression analysis was used to construct the TEXM signature model by using these five methylation sites. Significant differences in OS were found between the groups with high and low TEXM signature scores, on the basis of calculation of the TEXM signature scores of tumor samples and using the median score to divide them into high and low score groups. Survival analysis revealed that the high score group had poorer OS and DFS than the low score group in the validation set. Notably, we observed a significant difference in drug sensitivity between the high and low TEXM signature score groups, with the high score group showing higher drug resistance and poorer prognosis. The tumor immune state, as predicted with Tracking Tumor Immunophenotype (TIP), revealed significant differences in antitumor immune scores between the high and low TEXM signature score groups. Finally, we identified 43 significantly differentially regulated metabolism-associated biological processes. Conclusion The epigenetic methylation-related TEXM signature plays a key role in driving differential immune responses in GBM.