Search Results (10)

Background/Objectives: Late epilepsy occurring in the late stage after glioblastoma (GBM) resection is suggested to be caused by increased extracellular glutamate (Glu). To elucidate the mechanism underlying postoperative late epilepsy, the present study aimed to investigate the expressions and relations of molecules related to Glu metabolism in tumor tissues from GBM patients and cultured glioma stem-like cells (GSCs). Methods: Expressions of CD44, xCT and excitatory amino acid transporter (EAAT) 2 and extracellular Glu concentration in GBM patients with and without epilepsy were examined and their relationships were analyzed. For the study using GSCs, expressions and relationships of the same molecules were analyzed and the effects of CD44 knock-down on xCT, EAAT2, and Glu were investigated. In addition, the effects of hypoxia on the expressions of these molecules were investigated. Results: Tumor tissues highly expressed CD44 and xCT in the periphery of GBM with epilepsy, whereas no significant difference in EAAT2 expression was seen between groups with and without epilepsy. Extracellular Glu concentration was higher in patients with epilepsy than those without epilepsy. GSCs displayed reciprocal expressions of CD44 and xCT. Concentrations of extracellular Glu coincided with the degree of xCT expression, and CD44 knock-down elevated xCT expression and extracellular Glu concentrations. Hypoxia of 1% O₂ elevated expression of CD44, while 5% O₂ increased xCT and extracellular Glu concentration. Conclusions: Late epilepsy after GBM resection was related to extracellular Glu concentrations that were regulated by reciprocal expression of CD44 and xCT, which were stimulated by differential hypoxia for each molecule. Full article

Glioblastoma is a highly aggressive brain tumor that has a poor prognosis despite various treatments like surgery, chemotherapy, and irradiation. However, a restricted ketogenic diet (RKD), which has been proven to be effective in treating drug-resistant epilepsy, could be a potential adjunct in the treatment of certain GBM cases. Our study aimed to highlight the existing knowledge, identify collaboration networks, and emphasize the ongoing research based on highly cited studies. During the literature search, we found 119 relevant articles written between 2010 and 2023. Among the top 20 most cited articles, there were seven laboratory and five clinical studies. The works of Olson LK, Chang HT, Schwartz KA, and Nikolai M from the Michigan State University, followed by Seyfried TN and Mukherjee P from Boston College, and Olieman JF, and Catsman-Berrevoets CE from the University Medical Center of Rotterdam, were significant contributions. The laboratory studies showed that RKD had a significant antitumor effect and could prolong survival in mouse glioblastoma models. The clinical studies verified the tolerability, efficacy, and safety of RKD in patients with GBM, but raised concerns about whether it could be used as a single therapy. The current research interest is focused on the efficacy of using RKD as an adjunct in selected chemotherapy regimens and demonstrates that it could provide GBM patients with better treatment options. Full article

Glioblastoma (GBM) is characterized by fast-growing cells, genetic and phenotypic heterogeneity, and radio-chemo-therapy resistance, contributing to its dismal prognosis. Various medical comorbidities are associated with the natural history of GBM. The most disabling and greatly affecting patients’ quality of life are neurodegeneration, cognitive impairment, and GBM-related epilepsy (GRE). Hallmarks of GBM include molecular intrinsic mediators and pathways, but emerging evidence supports the key role of non-malignant cells within the tumor microenvironment in GBM aggressive behavior. In this context, hyper-excitability of neurons, mediated by glutamatergic and GABAergic imbalance, contributing to GBM growth strengthens the cancer-nervous system crosstalk. Pathogenic mechanisms, clinical features, and pharmacological management of GRE with antiepileptic drugs (AEDs) and their interactions are poorly explored, yet it is a potentially promising field of research in cancer neuroscience. The present review summarizes emerging cooperative mechanisms in oncogenesis and epileptogenesis, focusing on the neuron-to-glioma interface. The main effects and efficacy of selected AEDs used in the management of GRE are discussed in this paper, as well as their potential beneficial activity as antitumor treatment. Overall, although still many unclear processes overlapping in GBM growth and seizure onset need to be elucidated, this review focuses on the intriguing targeting of GBM-neuron mutual interactions to improve the outcome of the so challenging to treat GBM. Full article

Epilepsy is reported in 29–52% of patients with glioblastoma (GBM) and has an important role in the natural history of this tumor and patients’ life quality. Although GBM is less epileptogenic than lower-grade gliomas, seizures are usually more difficult to control with common antiseizure medications; drug resistance is found in 20% of cases. Recent studies suggest that seizures at the onset of GBM could be a possible favorable independent prognostic factor in patients. Moreover, a growing body of evidence shows that many molecular mechanisms that influence epileptogenesis often regulate GBM growth and invasiveness, sometimes favoring or counteracting the tumor, respectively. The better-characterized players include glutamate, γ-aminobutyric acid, aquaporin-4, and hypoxia-activated molecules. However, currently available data on the molecular basis of epileptogenesis, tumorigenesis, and their relationship is incomplete or discordant and further research is urgently needed on this topic. Full article

Psychogenic non-epileptic seizures (PNES) may resemble epileptic seizures but are not caused by epileptic activity. However, the analysis of electroencephalogram (EEG) signals with entropy algorithms could help identify patterns that differentiate PNES and epilepsy. Furthermore, the use of machine learning could reduce the current diagnosis costs by automating classification. The current study extracted the approximate sample, spectral, singular value decomposition, and Renyi entropies from interictal EEGs and electrocardiograms (ECG)s of 48 PNES and 29 epilepsy subjects in the broad, delta, theta, alpha, beta, and gamma frequency bands. Each feature-band pair was classified by a support vector machine (SVM), k-nearest neighbour (kNN), random forest (RF), and gradient boosting machine (GBM). In most cases, the broad band returned higher accuracy, gamma returned the lowest, and combining the six bands together improved classifier performance. The Renyi entropy was the best feature and returned high accuracy in every band. The highest balanced accuracy, 95.03%, was obtained by the kNN with Renyi entropy and combining all bands except broad. This analysis showed that entropy measures can differentiate between interictal PNES and epilepsy with high accuracy, and improved performances indicate that combining bands is an effective improvement for diagnosing PNES from EEGs and ECGs. Full article

Glioblastoma multiforme (GBM) is a fatal brain tumor without effective drug treatment. In this study, we highlight, for the first time, the contribution of chromatin remodeling gene Lysine (K)-specific demethylase 5C (KDM5C) in GBM via an extensive analysis of clinical, expression, and functional data, integrated with publicly available omic datasets. The expression analysis on GBM samples (N = 37) revealed two informative subtypes, namely KDM5C^High and KDM5C^Low, displaying higher/lower KDM5C levels compared to the controls. The former subtype displays a strong downregulation of brain-derived neurotrophic factor (BDNF)—a negative KDM5C target—and a robust overexpression of hypoxia-inducible transcription factor-1A (HIF1A) gene, a KDM5C modulator. Additionally, a significant co-expression among the prognostic markers HIF1A, Survivin, and p75 was observed. These results, corroborated by KDM5C overexpression and hypoxia-related functional assays in T98G cells, suggest a role for the HIF1A-KDM5C axis in the hypoxic response in this tumor. Interestingly, fluorescence-guided surgery on GBM sections further revealed higher KDM5C and HIF1A levels in the tumor rim niche compared to the adjacent tumor margin, indicating a regionally restricted hyperactivity of this regulatory axis. Analyzing the TCGA expression and methylation data, we found methylation changes between the subtypes in the genes, accounting for the hypoxia response, stem cell differentiation, and inflammation. High NANOG and IL6 levels highlight a distinctive stem cell-like and proinflammatory signature in the KDM5C^High subgroup and GBM niches. Taken together, our results indicate HIF1A-KDM5C as a new, relevant cancer axis in GBM, opening a new, interesting field of investigation based on KDM5C as a potential therapeutic target of the hypoxic microenvironment in GBM. Full article

High-grade gliomas are the most fatal brain tumors. Grade 4 gliomas are called glioblastoma multiforme (GBM), which are associated with the poorest survival and a 5-year survival rate of less than 4%. Many patients with GBM developed concomitant cognitive dysfunctions and epilepsy. Although the cognitive decline is well defined in glioblastomas, the neurotoxic factors underlying this pathology are not well understood in GBM patients. In this study, we aimed to investigate whether GBM-derived exosomes play a role in neuronal toxicity. For this purpose, exosomes obtained from T98G and U373 GBM cells were applied to primary neuron culture at different concentrations. Subsequently, MTT, LDH, GSH, TAS, and TOS tests were performed. Both GBM-derived exosomes induced a dose-dependent and statistically significant increase of LDH release in cerebellar neurons. MTT assay revealed as both T98G and U373 GBM-derived exosomes induced dose-dependent neurotoxic effects in cerebellar neurons. To the best of our knowledge, this study is the first study demonstrating the toxic potential of GBM-derived exosomes to primary neurons, which may explain the peritumoral edema and cognitive decline in GBM patients. Full article

Epilepsy is one of the most common brain diseases that affects more than 1% of the world’s population. It is characterized by recurrent seizures, which come in different types and are treated differently. Electroencephalography (EEG) is commonly used in medical services to diagnose seizures and their types. The accurate identification of seizures helps to provide optimal treatment and accurate information to the patient. However, the manual diagnostic procedures of epileptic seizures are laborious and require professional skills. This paper presents a novel automatic technique that involves the extraction of specific features from epileptic seizures’ EEG signals using dual-tree complex wavelet transform (DTCWT) and classifying them into one of the seven types of seizures, including absence, complex-partial, focal non-specific, generalized non-specific, simple-partial, tonic-clonic, and tonic seizures. We evaluated the proposed technique on the TUH EEG Seizure Corpus (TUSZ) ver.1.5.2 dataset and compared the performance with the existing state-of-the-art techniques using the overall F1-score due to class imbalance of seizure types. Our proposed technique achieved the best results of a weighted F1-score of 99.1% and 74.7% for seizure-wise and patient-wise classification, respectively, thereby setting new benchmark results for this dataset. Full article

The design of a computer-aided system for identifying the seizure onset zone (SOZ) from interictal and ictal electroencephalograms (EEGs) is desired by epileptologists. This study aims to introduce the statistical features of high-frequency components (HFCs) in interictal intracranial electroencephalograms (iEEGs) to identify the possible seizure onset zone (SOZ) channels. It is known that the activity of HFCs in interictal iEEGs, including ripple and fast ripple bands, is associated with epileptic seizures. This paper proposes to decompose multi-channel interictal iEEG signals into a number of subbands. For every 20 s segment, twelve features are computed from each subband. A mutual information (MI)-based method with grid search was applied to select the most prominent bands and features. A gradient-boosting decision tree-based algorithm called LightGBM was used to score each segment of the channels and these were averaged together to achieve a final score for each channel. The possible SOZ channels were localized based on the higher value channels. The experimental results with eleven epilepsy patients were tested to observe the efficiency of the proposed design compared to the state-of-the-art methods. Full article

Epilepsy at presentation is an independent favorable prognostic factor in glioblastoma (GBM). In this study, we analyze the oncologic signaling pathways that associate with epilepsy in human GBMs, and that can underlie this prognostic effect. Following ethical approval and patient consent, fresh frozen GBM tissue was obtained from 76 patient surgeries. Hospital records were screened for the presence of seizures at presentation of the disease. mRNA and miRNA expression-based and gene set enrichment analyses were performed on these tissues, to uncover candidate oncologic pathways that associate with epilepsy. We performed qPCR experiments and immunohistochemistry on tissue microarrays containing 286 GBMs to further explore the association of these candidate pathways and of markers of mesenchymal transformation (NF-κB, CEBP-β, STAT3, STAT5b, VEGFA, SRF) with epilepsy. Gene sets involved in hypoxia/HIF-1α, STAT5, CEBP-β and epithelial-mesenchymal transformation signaling were significantly downregulated in epileptogenic GBMs. On confirmatory protein expression analyses, epileptogenic tumors were characterized by a significant downregulation of phospho-STAT5b, a target of HIF-1α. Epilepsy status did not associate with molecular subclassification or miRNA expression patterns of the tumors. Epileptogenic GBMs correlate with decreased hypoxia/ HIF-1α/STAT5b signaling compared to glioblastomas that do not present with epilepsy. Full article