Background Glioblastoma (GBM) is the most malignant grade of glioma. Highly aggressive characteristics of GBM and poor prognosis cause GBM-related deaths. The potential prognostic biomarkers remain to be demonstrated. This research builds up predictive gene targets of expression alterations in GBM utilizing bioinformatics analysis. Methods and results The microarray datasets (GSE15824 and GSE16011) associated with GBM were obtained from Gene Expression Omnibus (GEO) database to identify the differentially expressed genes (DEGs) between GBM and non-tumor tissues. In total, 719 DEGs were obtained and subjected to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) for function enrichment analysis. Furthermore, we constructed protein–protein Interaction (PPI) network among DEGs utilizing Search Tool for the Retrieval of Interacting Genes (STRING) online tool and Cytoscape software. The DEGs of degree > 10 was selected as hub genes, including 73 upregulated genes and 21 downregulated genes. Moreover, MCODE application in Cytoscape software was employed to identify three key modules involved in GBM development and prognosis. Additionally, we used the Gene expression profiling and interactive analyses (GEPIA) online tool to further confirm four genes involving in poor prognosis of GBM patients, including interferon-gamma-inducible protein 30 (IFI30), major histocompatibility complex class II-DM alpha (HLA-DMA), Prolyl 4-hydroxylase beta polypeptide (P4HB) and reticulocalbin-1 (RCN1). Furthermore, the correlation analysis indicated that the expression of IFI30, an acknowledged biomarker in glioma, was positively correlated with HLA-DMA, P4HB and RCN1. RCN1 expression was positively correlated with P4HB and HLA-DMA. Moreover, qRT-PCR and immunohistochemistry analysis further validated the upregulation of four prognostic markers in GBM tissues. Conclusions Analysis of multiple datasets combined with global network information and experimental verification presents a successful approach to uncover the risk hub genes and prognostic markers of GBM. Our study identified four risk- and prognostic-related gene signatures, including IFI30, HLA-DMA, P4HB and RCN1. This gene sets contribute a new perspective to improve the diagnostic, prognostic, and therapeutic outcomes of GBM.
Kainic acid (KA) induced epileptic seizures in mice is a commonly used experimental model of epilepsy. Previous studies have suggested the roles of various neurotransmitters and oxidative stress in KA-induced seizures. An important role of hypothyroidism has also been suggested in epilepsy. Thiamazole (TZ) is an anti-hyperthyroid drug with antioxidant property. This study reports the effect of TZ on KA-induced epileptic seizures in mice, produced by intraperitoneal (IP) injection of KA (18 mg/kg). Prior to KA injection, the animals were treated with TZ (12.5, 25 and 50 mg/kg IP). Our results showed that in KA alone group, about half of the animals developed seizures. Pre-treatment of mice with TZ significantly increased the frequency of seizures in dose-dependent manner. Administration of TZ significantly reduced the latency time and aggravated the severity of seizures. TZ also increased the mortality in KA-treated mice. Striatal dopamine and serotonin levels were markedly increased in KA alone treated mice, which were not significantly affected by TZ treatment. Among the indices of oxidative stress, we observed a significant reduction in cerebral vitamin E whereas the levels of cerebral malondialdehyde and conjugated dienes were significantly increased in animals with high severity of seizures. In conclusion, TZ potentiated the frequency and severity of experimental seizure in mice. There is a possibility of altered metabolism of KA in presence of TZ that might have potentiated the toxicity of KA. These findings suggest a caution while administering anti-hyperthyroid drugs in epileptic seizures.
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