The study aimed to identify key genes involved in cerebral vasospasm (CVS) after subarachnoid haemorrhage (SAH). GSE46696 of basilar arteries of SAH mice and normal controls were downloaded from the Gene Expression Omnibus (GEO). Integrated microarray analysis was performed to identify differentially expressed genes (DEGs). GO and KEGG pathway enrichment analyses of DEGs were performed with ClueGO. The protein–protein interaction (PPI) networks were constructed using Cytoscape software. A total of 4103 DEGs were identified; among them, 254 DEGs (63 up-regulated genes and 191 down-regulated genes) showed significant differences at p < 0.05. GO analysis showed that the identified DEGs were over-represented in 16 GO terms. KEGG pathway analysis showed that pathways in immune inflammation were significantly enriched pathways for DEGs. DEGs with relatively frequent interactions after CVS secondary to SAH included MIKI, Cmpk2, TIr3, Psmb9, Ddx58, Lgals9, Ifi44, Stat2, Rsad2, Oas2, Usp18, H28, Irf7 and als3bp. Multiple genes were involved in the regulation of the immune response in the pathogenesis of SAH, including Ripk3, Ifih1, IL10, Reg3g, SIc11a1, NF-κB, Tlr7, Parp9, Rab7b, Dhx58, Gpx2, Zbp1, Aim2, Rsad2, Lgals9, TLR4, Adar, Zc3hav1, KIrk1, Irf7, IL-1β, Trafd1, Ddx58 and Trim5. Our findings revealed the gene expression profiles of the cerebral arteries in SAH mouse models, and speculated that DHx58 gene plays an important role in the immune response through regulating inflammatory cytokines expression, which may be a potential target in the treatment of CVS after SAH. Our finding provided new clues for understanding the mechanism of SAH.
Predicting the prognosis of glioblastoma (GBM) has always been important for improving survival. An understanding of the prognostic factors for patients with GBM can help guide treatment. Herein, we aimed to construct a prognostic model for predicting overall survival (OS) for patients with GBM. We identified 11,375 patients with pathologically confirmed GBM from the Surveillance, Epidemiology, and End Results database between 2004 and 2015. The 1-, 2-, and 3-year survival probabilities were 48.8%, 22.5%, and 13.1%, respectively. The patients were randomly divided into the training cohort (n = 8531) and the validation cohort (n = 2844). A Cox proportional risk regression model was used to analyze the prognostic factors of patients in the training cohort, and a nomogram was constructed. Then concordance indexes (C-indexes), calibration curves, and receiver operating characteristic (ROC) curves were used to assess the performance of the nomograms by internal (training cohort) and external validation (validation cohort). Log-rank test and univariate analysis showed that age, race, marital status, extent of surgical resection, chemotherapy, and radiation were the prognostic factors for patients with GBM (p < 0.05), which were used to construct nomogram. The C-index of the nomogram was 0.717 (95% confidence interval (CI), 0.710–0.724) in the training cohort, and 0.724 (95% CI, 0.713–0.735) in the validation cohort. The nomogram had a higher areas under the ROC curve value. The nomogram was well validated, which can effectively predict the OS of patients with GBM. Thus, this nomogram could be applied in clinical practice.
ABSTRACT Background Glioma stem cells (GSCs) contribute to the initiation, recurrence, metastasis, and drug resistance of glioblastoma multiforme (GBM). Long noncoding RNAs (lncRNAs) are critical modulators in the development and progression of GBM; however, specific lncRNAs related to GSCs remain largely unexplored. This study aims to identify dysregulated lncRNAs in GSCs, unravel their contributions to GBM progression, and propose new targets for diagnosis and treatment. Methods GeneChip analysis was utilized to identify lncRNAs in GSCs. The expression of RNAs was examined using quantitative real‐time PCR. Cell Counting Kit‐8, tmorsphere formation assay, limiting dilution assay, apoptosis detection and intracranial xenograft models were performed to assess the stemness and radioresistance of GSCs. Transcriptomics analysis, RNA immunoprecipitation and dual‐luciferase experiments were conducted for mechanistic studies. Results NONHSAT141192.2 exhibited elevated expression levels in aggressive GBM tissues compared to lower‐grade gliomas. Silencing NONHSAT141192.2 resulted in a considerable decrease in GSC proliferation, tumor sphere formation, self‐renewal and the expression of key stem cell markers. Furthermore, depletion of NONHSAT141192.2 enhanced GSC sensitivity to radiation, indicated by diminished viability and tumorsphere formation, increased cell apoptosis, and decreased tumor growth in intracranial xenograft models. Mechanistically, NONHSAT141192.2 upregulates the expression of SOX2 and PIK3R3 by sponging miR‐4279, influencing GSC characteristics and their resistance to radiation. Conclusion The study highlights a significant relationship between NONHSAT141192.2, GSC stemness, and radioresistance, emphasizing its potential as a therapeutic target for GBM treatment and radiosensitization.
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