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The application of chimeric antigen receptor T-cell therapy in central nervous system tumors has significantly advanced; however, challenges pertaining to the blood-brain barrier, immunosuppressive microenvironment, and antigenic heterogeneity continue to be encountered, unlike its success in hematological malignancies such as acute lymphoblastic leukemia and diffuse large B-cell lymphomas. This review examined the research progress of chimeric antigen receptor T-cell therapy in gliomas, medulloblastomas, and lymphohematopoietic tumors of the central nervous system, focusing on chimeric antigen receptor T-cells targeting antigens such as EGFRvIII, HER2, B7H3, GD2, and CD19 in preclinical and clinical studies. It synthesized current research findings to offer valuable insights for future chimeric antigen receptor T-cell therapeutic strategies for central nervous system tumors and advance the development and application of this therapeutic modality in this domain. [ABSTRACT FROM AUTHOR]

Glioblastomas (GBM) are most common types of primary brain tumors and miRNAs play an important role in pathogenesis of glioblastomas. Here, we reported a new miRNA, miR-3154, which regulates glioblastoma proliferation and metastasis. miR-3154 was elevated in glioblastoma tissue and cell lines, and its elevation was associated with grade of glioblastomas. Knockdown of miR-3154 in cell lines weakened ability of proliferation and colony formation, and caused cell cycle arrested and higher percentage of apoptosis. Knockdown of miR-3154 also impaired ability of migration and invasion in glioblastoma cells. In mechanism, miR-3154 bound directly to Tumor Protein P53 Inducible Nuclear Protein 1 (TP53INP1), down-regulating TP53INP1 expression at both mRNA and protein level. Silence of TP53INP1 reversed the effect of miR-3154 knockdown on proliferation and metastasis of glioblastoma cells. These findings show that miR-3154 promotes glioblastoma proliferation and metastasis via targeting TP53INP1. [ABSTRACT FROM AUTHOR]

Detecting brain tumors early on is critical for effective treatment and life-saving efforts. The analysis of the brain with MRI scans is fundamental to the diagnosis because it contains detailed structural views of the brain, which is vital in identifying any of its abnormalities. The other option of performing an invasive biopsy is very painful and uncomfortable, which is not the case with MRI as it is free from surgically invasive margins and pieces of equipment. This helps patients to feel more at ease and hasten the diagnostic procedure, allowing physicians to formulate and practice action plans quicker. It is very difficult to locate a human brain tumor by manual because MRI scans produce large numbers of three-dimensional images. Complete applicability of pre-written computerized diagnostics, affords high possibilities in providing areas of interest earlier through the application of machine learning techniques and algorithms. The proposed work in the present study was to develop a deep learning model which will classify brain tumor grade images (BTGC), and hence enhance accuracy in diagnosing patients with different grades of brain tumors using MRI. A MobileNetV2 model, was used to extract the features from the images. This model increases the efficiency and generalizability of the model further. In this study, six standard Kaggle brain tumor MRI datasets were used to train and validate the developed and tested model of a brain tumor detection and classification algorithm into several types. This work consists of two key components: (i) brain tumor detection and (ii) classification of the tumor. The tumor classifications are conducted in both three classes (Meningioma, Pituitary, and glioma) and two classes (malignant, benign). The model has been reported to detect brain tumors with 99.85% accuracy, to distinguish benign and malignant tumors with 99.87% accuracy, and to type meningioma, pituitary, and glioma tumors with 99.38% accuracy. The results of this study indicate that the described technique is useful in the detection and classification of brain tumors. [ABSTRACT FROM AUTHOR]

Background: High-dimensional omics data integration has emerged as a prominent avenue within the healthcare industry, presenting substantial potential to improve predictive models. However, the data integration process faces several challenges, including data heterogeneity, priority sequence in which data blocks are prioritized for rendering predictive information contained in multiple blocks, assessing the flow of information from one omics level to the other and multicollinearity. Methods: We propose the Priority-Elastic net algorithm, a hierarchical regression method extending Priority-Lasso for the binary logistic regression model by incorporating a priority order for blocks of variables while fitting Elastic-net models sequentially for each block. The fitted values from each step are then used as an offset in the subsequent step. Additionally, we considered the adaptive elastic-net penalty within our priority framework to compare the results. Results: The Priority-Elastic net and Priority-Adaptive Elastic net algorithms were evaluated on a brain tumor dataset available from The Cancer Genome Atlas (TCGA), accounting for transcriptomics, proteomics, and clinical information measured over two glioma types: Lower-grade glioma (LGG) and glioblastoma (GBM). Conclusion: Our findings suggest that the Priority-Elastic net is a highly advantageous choice for a wide range of applications. It offers moderate computational complexity, flexibility in integrating prior knowledge while introducing a hierarchical modeling perspective, and, importantly, improved stability and accuracy in predictions, making it superior to the other methods discussed. This evolution marks a significant step forward in predictive modeling, offering a sophisticated tool for navigating the complexities of multi-omics datasets in pursuit of precision medicine's ultimate goal: personalized treatment optimization based on a comprehensive array of patient-specific data. This framework can be generalized to time-to-event, Cox proportional hazards regression and multicategorical outcomes. A practical implementation of this method is available upon request in R script, complete with an example to facilitate its application. [ABSTRACT FROM AUTHOR]

Gliomas are one of the most frequent primary brain tumors and pose a serious threat to people's lives and health. Platelets, a crucial component of blood, have been applied as drug delivery carriers for disease diagnosis and treatment. In this study, we designed engineered nanoplatelets for targeted delivery of therapeutic miR-375 and temozolomide (TMZ, a first-line glioma treatment agent) to enhance glioma therapy. Nanoplatelets were prepared through mild ultrasound, TMZ and miR-375 were co-loaded through ultrasound and electrostatic interactions, respectively, to combine chemotherapy with gene therapy against glioma. To improve the blood brain barrier (BBB) crossing efficiency and glioma targeting ability, the nanoplatelets were modified with central nervous system-specific rabies viral glycoprotein peptide (RVG) through thiol-maleimide click reaction. The RVG modified nanoplatelets co-loaded TMZ and miR-375 (NR/TMZ/miR-375) not only inherited the good stability and remarkable biocompatibility of platelets, but also promoted the cellular uptake and penetration of glioma tissues, and effectively induced cell apoptosis to enhance the therapeutic effect of drugs. In vivo studies showed that NR/TMZ/miR-375 significantly increased the circulation time of TMZ, and exhibited superior combined antitumor effects. In summary, this multifunctional 'natural' nanodrug delivery system provides a potent, scalable, and safety approach for platelet-based combined cancer chemotherapy and gene therapy. [ABSTRACT FROM AUTHOR]

Brain cancer is regarded as one of the most life-threatening forms of cancer worldwide. Oxidative stress acts to derange normal brain homeostasis, thus is involved in carcinogenesis in brain. The Nrf2/Keap1/ARE pathway is an important signaling cascade responsible for the maintenance of redox homeostasis, and regulation of anti-inflammatory and anticancer activities by multiple downstream pathways. Interestingly, Nrf2 plays a somewhat, contradictory role in cancers, including brain cancer. Nrf2 has traditionally been regarded as a tumor suppressor since its cytoprotective functions are considered to be the principle cellular defense mechanism against exogenous and endogenous insults, such as xenobiotics and oxidative stress. However, hyperactivation of the Nrf2 pathway supports the survival of normal as well as malignant cells, protecting them against oxidative stress, and therapeutic agents. Plants possess a pool of secondary metabolites with potential chemotherapeutic/chemopreventive actions. Modulation of Nrf2/ARE and downstream activities in a Keap1-dependant manner, with the aid of plant-derived secondary metabolites exhibits promise in the management of brain tumors. Current article highlights the effects of Nrf2/Keap1/ARE cascade on brain tumors, and the potential role of secondary metabolites regarding the management of the same. [ABSTRACT FROM AUTHOR]

Background: Pediatric central nervous system tumors are the most common solid tumors in children and leading cause of cancer-related morbidity and mortality. Various factors may influence the practice of blood transfusion during this tumor diagnosis. The primary aim of this study was to determine the factors that may influence intraoperative blood transfusion in pediatric patients undergoing surgery for intracranial tumors and to predict patients who may require blood transfusion. Methods: A retrospective study was performed in all pediatric patients younger than 15 years who underwent craniotomy for brain tumor removal from January 2018 to December 2023 in our institution. Preoperative, intraoperative and postoperative data were collected from medical and store anesthesia records. The predictors of intraoperative blood transfusion were determined using multivariate logistic regression. Results: A total of 138 patients were enrolled in the study, of whom 62 (44.9%) required intraoperative blood transfusion. In multivariate regression analysis age < 4 years and operating time > 490 min were determined as independent variables in terms of need for intraoperative blood transfusion. It was determined that the need for transfusion was higher in patient who were operated on urgently and patients with comorbidities (p = 0.023, p = 0.005). Conclusion: In conclusion, the findings obtained in this study suggest that age and surgical duration are independent risk factors for intraoperative blood transfusion in pediatric patients undergoing surgery for intracranial tumors. Particularly, in younger patients and prolonged surgeries, closer monitoring and awareness may enhance early detection, leading to the prevention of complications. [ABSTRACT FROM AUTHOR]

Introduction: The authors conducted meta-analyses regarding the association between cellular and mobile phone use and brain tumor development by applying various radiofrequency-electromagnetic radiation (RF-EMR) exposure subcategories. With changing patterns of mobile phone use and rapidly developing Wireless Personal Area Network (WPAN) technology (such as Bluetooth), this study will provide insight into the importance of more precise exposure subcategories for RF-EMR. Methods: The medical librarian searched MEDLINE (PubMed), EMBASE, and the Cochrane Library until 16 December 2020. Results: In these meta-analyses, 19 case-control studies and five cohort studies were included. Ipsilateral users reported a pooled odds ratio (OR) of 1.40 (95% CI 1.21–1.62) compared to non-regular users. Users with years of use over 10 years reported a pooled OR of 1.27 (95% CI 1.08–1.48). When stratified by each type of brain tumor, only meningioma (OR 1.20 (95% CI 1.04–1.39)), glioma (OR 1.45 (95% CI 1.16–1.82)), and malignant brain tumors (OR 1.93 (95% CI 1.55–2.39)) showed an increased OR with statistical significance for ipsilateral users. For users with years of use over 10 years, only glioma (OR 1.32 (95% CI 1.01–1.71)) showed an increased OR with statistical significance. When 11 studies with an OR with cumulative hours of use over 896 h were synthesized, the pooled OR was 1.59 (95% CI 1.25–2.02). When stratified by each type of brain tumor, glioma, meningioma, and acoustic neuroma reported the pooled OR of 1.66 (95% CI 1.13–2.44), 1.29 (95% CI 1.08–1.54), and 1.84 (95% CI 0.78–4.37), respectively. For each individual study that considered cumulative hours of use, the highest OR for glioma, meningioma, and acoustic neuroma was 2.89 (1.41–5.93) (both side use, > 896 h), 2.57 (1.02–6.44) (both side use, > 896 h), and 3.53 (1.59–7.82) (ipsilateral use, > 1640 h), respectively. For five cohort studies, the pooled risk ratios (RRs) for all CNS tumors, glioma, meningioma, and acoustic neuroma, were statistically equivocal, respectively. However, the point estimates for acoustic neuroma showed a rather increased pooled RR for ever-use (1.26) and over 10 years of use (1.61) compared to never-use, respectively. Discussion: In this meta-analysis, as the exposure subcategory used became more concrete, the pooled ORs demonstrated higher values with statistical significance. Although the meta-analysis of cohort studies yielded statistically inconclusive pooled effect estimates, (i) as the number of studies included grows and (ii) as the applied exposure subcategories become more concrete, the pooled RRs could show a different aspect in future research. Additionally, future studies should thoroughly account for changing patterns in mobile phone use and the growing use of earphones or headphones with WPAN technology. [ABSTRACT FROM AUTHOR]

Glioblastomas (GBMs) are the most common and aggressive malignant brain tumors, presenting significant challenges for treatment due to their invasive nature and localization in critical brain regions. Standard treatment includes surgical resection followed by radiation and adjuvant chemotherapy with temozolomide (TMZ). Recent advances in immunotherapy, including the use of mRNA vaccines, offer promising alternatives. This review focuses on the emerging use of mRNA vaccines for GBM treatment. We summarize recent advancements, evaluate current obstacles, and discuss notable successes in this field. Our analysis highlights that while mRNA vaccines have shown potential, their use in GBM treatment is still experimental. Ongoing research and clinical trials are essential to fully understand their therapeutic potential. Future developments in mRNA vaccine technology and insights into GBM-specific immune responses may lead to more targeted and effective treatments. Despite the promise, further research is crucial to validate and optimize the effectiveness of mRNA vaccines in combating GBM. [ABSTRACT FROM AUTHOR]

Significant progress has been made recently with the contribution of technological advances in studies on brain cancer. Regarding this, identifying and correctly classifying tumors is a crucial task in the field of medical imaging. The disease-related tumor classification problem, on which deep learning technologies have also become a focus, is very important in the diagnosis and treatment of the disease. The use of deep learning models has shown promising results in recent years. However, the sparsity of ground truth data in medical imaging or inconsistent data sources poses a significant challenge for training these models. The utilization of StyleGANv2-ADA is proposed in this paper for augmenting brain MRI slices to enhance the performance of deep learning models. Specifically, augmentation is applied solely to the training data to prevent any potential leakage. The StyleGanv2-ADA model is trained with the Gazi Brains 2020, BRaTS 2021, and Br35h datasets using the researchers' default settings. The effectiveness of the proposed method is demonstrated on datasets for brain tumor classification, resulting in a notable improvement in the overall accuracy of the model for brain tumor classification on all the Gazi Brains 2020, BraTS 2021, and Br35h datasets. Importantly, the utilization of StyleGANv2-ADA on the Gazi Brains 2020 Dataset represents a novel experiment in the literature. The results show that the augmentation with StyleGAN can help overcome the challenges of working with medical data and the sparsity of ground truth data. Data augmentation employing the StyleGANv2-ADA GAN model yielded the highest overall accuracy for brain tumor classification on the BraTS 2021 and Gazi Brains 2020 datasets, together with the BR35H dataset, achieving 75.18%, 99.36%, and 98.99% on the EfficientNetV2S models, respectively. This study emphasizes the potency of GANs for augmenting medical imaging datasets, particularly in brain tumor classification, showcasing a notable increase in overall accuracy through the integration of synthetic GAN data on the used datasets. [ABSTRACT FROM AUTHOR]

Background: Glioma is the most common primary malignant tumor in the brain, and even with standard treatments including surgical resection, radiotherapy, and chemotherapy, the long-term survival rate of patients remains unsatisfactory. Recurrence is one of the leading causes of death in glioma patients. The molecular mechanisms underlying glioma recurrence remain unclear. Methods: Our study utilized single-cell sequencing, spatial transcriptomics, and RNA-seq data to identify a subtype of FN1 + tumor-associated macrophages (FN1 + TAMs) associated with glioma recurrence. Results: This study revealed an increased abundance of FN1 + TAMs in recurrent gliomas, indicating their potential involvement as a critical factor in glioma recurrence. A negative correlation was observed between the abundance of FN1 + TAMs in primary gliomas and the interval time to recurrence, suggesting poor prognosis for glioma patients with high levels of FN1 + TAMs. Further investigation showed that FN1 + TAMs were enriched in hypoxic tumor regions, implying that metabolic changes in tumors drive the production and recruitment of FN1 + TAMs. Additionally, FN1 + TAMs were found to contribute to the regulation of an immunosuppressive microenvironment in gliomas, and their abundance might serve as an indicator of patients' sensitivity to immunotherapy. Finally, we developed a user-friendly website, PRIMEG (http://www.szflab.site/PRIMEG/), for exploring the immune microenvironment of primary and recurrent gliomas. Conclusion: Our findings highlight a subtype of FN1 + TAMs associated with glioma recurrence, providing new insights into potential therapeutic targets. Moreover, the abundance of FN1 + TAMs hold promise for predicting immune therapy response and aiding in more precise risk stratification of recurrent glioma patients. [ABSTRACT FROM AUTHOR]

Recently, the anti-tumor effects of cannabis extract on various cancers have attracted the attention of researchers. Here, we report a nanoemulsion (NE) composition designed to enhance the delivery of two active components in cannabis extracts (∆9-Tetrahydrocannabinol (THC) and Cannabidiol (CBD)) in an animal model of glioblastoma. The efficacy of the NE containing the two drugs (NED) was compared with the bulk drugs and carrier (NE without the drugs) using the C6 tumor model in rats. Hemocompatibility factors (RBC, MCV, MCH, MCHC, RDW, PPP, PT and PTT) were studied to determine the potential in vivo toxicity of NED. The optimized NED with mean ± SD diameter 29 ± 6 nm was obtained. It was shown that by administering the drugs in the form of NED, the hemocompatibility increased. Cytotoxicity studies indicated that the NE without the active components (i.e. mixture of surfactants and oil) was the most cytotoxic group, while the bulk group had no toxicity. From the in vivo MRI and survival studies, the NED group had maximum efficacy (with ~4 times smaller tumor volume on day 7 of treatment, compared with the control. Also, survival time of the control, bulk drug, NE and NED were 9, 4, 12.5 and 51 days, respectively) with no important adverse effects. In conclusion, the NE containing cannabis extract could be introduced as an effective treatment in reducing brain glioblastoma tumor progression. [ABSTRACT FROM AUTHOR]

Background: Gliomas are aggressive brain tumors with poor prognosis. Understanding the tumor immune microenvironment (TIME) in gliomas is essential for developing effective immunotherapies. This study aimed to identify TIME-related biomarkers in glioma using bioinformatic analysis of RNA-seq data. Methods: In this study, we employed weighted gene co-expression network analysis (WGCNA) on bulk RNA-seq data to identify TIME-related genes. To identify prognostic genes, we performed univariate Cox regression and least absolute shrinkage and selection operator (LASSO) regression analyses. Based on these genes, we constructed a prognostic signature and delineated risk groups. To validate the prognostic signature, external validation was conducted. Results: CD8 + T cell infiltration was strongly correlated with glioma patient prognosis. We identified 115 CD8 + T cell-related genes through integrative analysis of bulk-seq data. CDCA5, KIF11, and KIF4A were found to be significant immune-related genes (IRGs) associated with overall survival in glioma patients and served as independent prognostic factors. We developed a prognostic nomogram that incorporated these genes, age, gender, and grade, providing a reliable tool for clinicians to predict patient survival probabilities. The nomogram's predictions were supported by calibration plots, further validating its accuracy. Conclusion: In conclusion, our study identifies CD8 + T cell infiltration as a strong predictor of glioma patient outcomes and highlights the prognostic value of genes. The developed prognostic nomogram, incorporating these genes along with clinical factors, provides a reliable tool for predicting patient survival probabilities and has important implications for personalized treatment decisions in glioma. [ABSTRACT FROM AUTHOR]

Background: Glioblastoma (GBM) is the most frequent and aggressive brain tumor in adults with the lowest survival rates five years post-diagnosis. Oncolytic viruses (OVs) selectively target and damage cancer cells, and for this reason they are being investigated as new therapeutic tools also against GBM. Methods: An oncolytic herpes simplex virus type 1 (oHSV-1) with deletions in the γ34.5 neurovirulence gene and the US12 gene, expressing enhanced green fluorescent protein (EGFP-oHSV-1) as reporter gene was generated and tested for its capacity to infect and kill the murine GL261 glioblastoma (GBM) cell line. Syngeneic mice were orthotopically injected with GL261cells. Seven days post-implantation, EGFP-oHSV-1 was administered intratumorally. Twenty-one days after parental tumor challenge in the opposite brain hemisphere, mice were sacrified and their brains were analysed by immunohistochemistry to assess tumor presence and cell infiltrate. Results: oHSV-1 replicates and induces cell death of GL261 cells in vitro. A single intracranial injection of EGFP-oHSV-1 in established GL261 tumors significantly prolongs survival in all treated mice compared to placebo treatment. Notably, 45% of treated mice became long-term survivors, and rejected GL261 cells upon rechallenge in the contralateral brain hemisphere, indicating an anamnestic antitumoral immune response. Post-mortem analysis revealed a profound modification of the tumor microenvironment with increased infiltration of CD4 + and CD8 + T lymphocytes, intertumoral vascular collapse and activation and redistribution of macrophage, microglia, and astroglia in the tumor area, with the formation of intense fibrotic tissue suggestive of complete rejection in long-term survivor mice. Conclusions: EGFP-oHSV1 demonstrates potent antitumoral activity in an immunocompetent GBM model as a monotherapy, resulting from direct cell killing combined with the stimulation of a protective adaptive immune response. These results open the way to possible application of our strategy in clinical setting. [ABSTRACT FROM AUTHOR]

Background: We sought to analyze, in well-defined clinical setting, the first 100 patients treated at the intraoperative MRI (iMRI) hybrid surgical theatre at our facility in a population-based setting to evaluate which pathologies are best approached with iMRI assisted surgeries, as this is not yet clearly defined. Methods: Patients undergoing surgery in the 3T iMRI hybrid surgical theatre at our neurosurgical department between December 2017 to May 2021 were included after informed consent. Demographic, clinical, surgical, histological, radiological and outcome parameters, as well as variables related to iMRI, were retrospectively collected and analyzed. Patients were subdivided into adult and pediatric cohorts. Results: Various neurosurgical procedures were performed; resection of tumors and epileptic foci, endoscopic skull base procedures including pituitary lesions, deep brain stimulation (DBS) and laser interstitial thermal therapy (LITT). In total, 41 patients were pediatric. An iMRI scan was carried out in 96% of cases and led to continuation of surgery in 50% of cases, mainly due to visualized remaining pathological tissue (95.2%). Median time to iMRI from intubation was 280 min and median total duration of surgery was 445 min. The majority of patients experienced no postoperative complications (70%), 13 patients suffered permanent postoperative deficits, predominantly visual. Conclusion: Herein, we demonstrate the first 100 patients undergoing neurosurgery aided by iMRI at our facility since introduction. Indications for surgery differed between pediatric and adult patients. The iMRI was utilized for tumor surgeries, particularly adult low-grade gliomas and pediatric tumors, as well as for epilepsy surgery and DBS. In this heterogenous population, iMRI led to continuation of surgery in 50%. To establish the benefit in maximizing the extent of resection in these brain pathologies future studies are recommended. Clinical trial number: Not applicable. [ABSTRACT FROM AUTHOR]

Background: This study assessed the cost-effectiveness of proton beam therapy (PBT) compared to conventional radiotherapy (CRT) for treating patients with brain tumors in Sweden. Methods: Data from a longitudinal non-randomized study performed between 2015 and 2020 was used, and included adult patients with brain tumors, followed during treatment and through a one-year follow-up. Clinical and demographic data were sourced from the longitudinal study and linked to Swedish national registers to get information on healthcare resource use. A cost-utility framework was used to evaluate the cost-effectiveness of PBT vs. CRT. Patients in PBT group (n = 310) were matched with patients in CRT group (n = 40) on relevant observables using propensity score matching with replacement. Costs were estimated from a healthcare perspective and included costs related to inpatient and specialized outpatient care, and prescribed medications. The health outcome was quality-adjusted life-years (QALYs), derived from the EORTC-QLQ-C30. Generalized linear models (GLM) and two-part models were used to estimate differences in costs and QALYs. Results: PBT yielded higher total costs, 14,639 US$, than CRT, 13,308 US$, with a difference of 1,372 US$ (95% CI, -4,914–7,659) over a 58 weeks' time horizon. Further, PBT resulted in non-significantly lower QALYs, 0.746 compared to CRT, 0.774, with a difference of -0.049 (95% CI, -0.195–0.097). The probability of PBT being cost-effective was < 30% at any willingness to pay. Conclusions: These results suggest that PBT cannot be considered a cost-effective treatment for brain tumours, compared to CRT. Trial registration: Not applicable. Novelty and Impact: This study informs on the cost-effectiveness of proton beam therapy (PBT) versus conventional radiotherapy (CRT) for patients with brain tumors in Sweden. Amidst global concerns regarding the cost-effectiveness of PBT, our investigation fills a notable gap by providing evidence from the Swedish healthcare landscape. Using real-world data, our study demonstrates the application of established health economic methodology to compare PBT and CRT for brain tumors, furthering research in this area. [ABSTRACT FROM AUTHOR]

Glioma is a primary brain tumor that grows quickly, has an unfavorable prognosis, and can spread intracerebrally. Glioma cells rely on glucose as the major energy source, and glycolysis plays a critical role in tumorigenesis and progression. Substrate utilization shifts throughout glioma progression to facilitate energy generation and biomass accumulation. This metabolic reprogramming promotes glioma cell proliferation and metastasis and ultimately decreases the efficacy of conventional treatments. Non-coding RNAs (ncRNAs) are involved in several glucose metabolism pathways during tumor initiation and progression. These RNAs influence cell viability and glucose metabolism by modulating the expression of key genes of the glycolytic pathway. They can directly or indirectly affect glycolysis in glioma cells by influencing the transcription and post-transcriptional regulation of oncogenes and suppressor genes. In this review, we discussed the role of ncRNAs in the metabolic reprogramming of glioma cells and tumor microenvironments and their abnormal expression in the glucometabolic pathway in glioma. In addition, we consolidated the existing theoretical knowledge to facilitate the use of this emerging class of biomarkers as biological indicators and potential therapeutic targets for glioma. [ABSTRACT FROM AUTHOR]

Background: Glioma is the most common brain tumor. IDH mutations occur frequently in glioma, indicating a more favorable prognosis. We aimed to explore energy metabolism-related genes in glioma to promote the research and treatment. Methods: Datasets were obtained from TCGA and GEO databases. Candidate genes were screened by differential gene expression analysis, then functional enrichment analysis was conducted on the candidate genes. PPI was also carried out to help determine the target gene. GSEA and DO analysis were conducted in the different expression level groups of the target gene. Survival analysis and immune cell infiltrating analysis were performed as well. Results: We screened 34 candidate genes and selected GLUD1 as the target gene. All candidate genes were significantly enriched in 10 KEGG pathways and 330 GO terms. GLUD1 expression was higher in IDH-mutant samples than IDH-wildtype samples, and higher in normal samples than tumor samples. Low GLUD1 expression was related to poor prognosis according to survival analysis. Most types of immune cells were negatively related to GLUD1 expression, but monocytes and activated mast cells exhibited significantly positive correlation with GLUD1 expression. GLUD1 expression was significantly related to 119 drugs and 6 immune checkpoint genes. GLUD1 was able to serve as an independent prognostic indicator of IDH-mutant glioma. Conclusion: In this study, we identified an energy metabolism-related gene GLUD1 potentially contributing to favorable clinical outcomes of IDH-mutant glioma. In glioma, GLUD1 related clinical outcomes and immune landscape were clearer, and more valuable information was provided for immunotherapy. [ABSTRACT FROM AUTHOR]

Background: The Multidisciplinary Tumor Board (MTB) is a collaborative platform involving specialists in oncology, surgery, radiology, pathology, and radiotherapy, and aims to optimize diagnostics and treatments. Despite MTB's widespread benefits, limited literature addresses its application in pediatric neuro-oncology. After a literature revision on pediatric neuro-oncology MTB, our study describes our institute's pediatric neuro-oncology MTB, focuses on evaluating its impact and the neuroradiologist's role in patient-centric approaches, considering recent genetic insights into pediatric brain tumors. Materials and methods: Literature Review concerning pediatric neuro-oncology MTB from January 2002 to June 2024. Clinical Data: retrospective study of all patient files presented in the pediatric neuro-oncology MTB (pnMTB) between 2019 and 2022. Statistical analysis was mainly carried out by directly comparing the absolute or relative values of the respective parameters examined; qualitative variables compared mainly with the chi-square test, quantitative variables mainly with the t-test. Results: Literature Review: 7 papers encompass a multidisciplinary approach for the pediatric CNS tumors. Clinical data: A total of 236 discussions were analyzed representing 107 patients. Median age was 14,3 years (range: 6 months – 17 years). The requests for case evaluations primarily came from the pediatric oncologists (83%) and neurosurgeons (14.8%), and they were mainly addressed to the neuroradiologists (70.3%). Proposals during pnMTB mainly involved imaging follow-up (47.8%) and management with chemotherapy (34.7%). Changes in patient treatment (CPT) occurred in 115 cases, and pediatric neuroradiologist intervention contributed to 72.4% of these changes. Conclusion: Thanks to their multidisciplinarity, high number of cases discussed, and usual respect for their proposals, the pnMTB has made it possible to improve the coordination among specialties involved in patient management, to apply the recent protocols, and to exchange knowledge among teams managing pediatric CNS tumors. [ABSTRACT FROM AUTHOR]

Background: The Hippo pathway is a conserved tumour suppressor signalling pathway, and its dysregulation is often associated with abnormal cell growth and tumorigenesis. We previously revealed that the transcriptional coactivator Yes-associated protein (YAP), the key effector of the Hippo pathway, is a molecular target for glioblastoma (GBM), the most common malignant brain tumour. Inhibiting YAP with small interfering RNA (siYAP) or the specific inhibitor verteporfin (VP) can diminish GBM growth to a certain degree. Results: In this study, to enhance the anti-GBM effect of siYAP and VP, we designed stepwise-targeting and hypoxia-responsive liposomes (AMVY@NPs), which encapsulate hypoxia-responsive polymetronidazole-coated VP and DOTAP adsorbed siYAP, with angiopep-2 (A2) modification on the surface. AMVY@NPs exhibited excellent blood‒brain barrier crossing, GBM targeting, and hypoxia-responsive and efficient siYAP and VP release properties. By inhibiting the expression and function of YAP, AMVY@NPs synergistically inhibited both the growth and stemness of GBM in vitro. Moreover, AMVY@NPs strongly inhibited the growth of orthotopic U87 xenografts and improved the survival of tumour-bearing mice without adverse effects. Conclusion: Specific targeting of YAP with stepwise-targeting and hypoxia-responsive liposome AMVY@NPs carrying siYAP and VP efficiently inhibited GBM progression. This study provides a valuable drug delivery platform and creative insights for molecular targeted treatment of GBM in the future. [ABSTRACT FROM AUTHOR]

Background: Glioblastoma (GB) is recognized as one of the most aggressive brain tumors, with a median survival of 14.6 months. However, there are still some patients whose survival time was greater than 3 years, and the biological reasons behind this clinical phenomenon arouse our research interests. By conducting proteomic analysis on tumor tissues obtained from GB patients who survived over 3 years compared to those who survived less than 1 year, we identified a significant upregulation of SelK in patients with shorter survival times. Therefore, we hypothesized that SelK may be an important indicator related to the occurrence and progression of GBM. Methods: Proteomics and immunohistochemistry from GB patients were analyzed to investigate the correlation between SelK and clinical prognosis. Cellular phenotypes were evaluated by cell cycle analysis, cell viability assays, and xenograft models. Immunoblots and co-immunoprecipitation were conducted to verify SelK-mediated ubiquitin-dependent degradation of CDK4. Results: SelK was found to be significantly upregulated in GB samples from short-term survivors (≤ 1 year) compared to those from long-term survivors (≥ 3 years), and its expression levels were negatively correlated with clinical prognosis. Knocking down of SelK expression reduced GB cell viability, induced G0/G1 phase arrest, and impaired the growth of transplanted glioma cells in nude mice. Down-regulation of SelK-induced ER stress leads to a reduction in the expression of SKP2 and an up-regulation of β-TrCP1 expression. Up-regulation of β-TrCP1, thereby accelerating the ubiquitin-dependent degradation of CDK4 and ultimately inhibiting the malignant proliferation of the GB cells. Conclusion: This study discovered a significant increase in SelK expression in GB patients with poor prognosis, revealing a negative correlation between SelK expression and patient outcomes. Further mechanistic investigations revealed that SelK enhances the proliferation of GB cells by targeting the endoplasmic reticulum stress/SKP2/β-TrCP1/CDK4 axis. [ABSTRACT FROM AUTHOR]

Background: Glioblastoma (GBM) is the most common brain tumor with the worst prognosis. Temozolomide is the only first-line drug for GBM. Unfortunately, the resistance issue is a classic problem. Therefore, it is essential to develop new drugs to treat GBM. As an oncogene, Skp2 is involved in the pathogenesis of various cancers including GBM. In this study, we investigated the anticancer effect of AAA237 on human glioblastoma cells and its underlying mechanism. Methods: CCK-8 assay was conducted to evaluate IC50 values of AAA237 at 48, and 72 h, respectively. The Cellular Thermal Shift Assay (CETSA) was employed to ascertain the status of Skp2 as an intrinsic target of AAA237 inside the cellular milieu. The EdU-DNA synthesis test, Soft-Agar assay and Matrigel assay were performed to check the suppressive effects of AAA237 on cell growth. To identify the migration and invasion ability of GBM cells, transwell assay was conducted. RT-qPCR and Western Blot were employed to verify the level of BNIP3. The mRFP-GFP-LC3 indicator system was utilized to assess alterations in autophagy flux and investigate the impact of AAA237 on the dynamic fusion process between autophagosomes and lysosomes. To investigate the effect of compound AAA237 on tumor growth in vivo, LN229 cells were injected into the brains of mice in an orthotopic model. Results: AAA237 could inhibit the growth of GBM cells in vitro. AAA237 could bind to Skp2 and inhibit Skp2 expression and the degradation of p21 and p27. In a dose-dependent manner, AAA237 demonstrated the ability to inhibit colony formation, migration, and invasion of GBM cells. AAA237 treatment could upregulate BNIP3 as the hub gene and therefore induce BNIP3-dependent autophagy through the mTOR pathway whereas 3-MA can somewhat reverse this process. In vivo, the administration of AAA237 effectively suppressed the development of glioma tumors with no side effects. Conclusion: Compound AAA237, a novel Skp2 inhibitor, inhibited colony formation, migration and invasion of GBM cells in a dose-dependent manner and time-dependent manner through upregulating BNIP3 as the hub gene and induced BNIP3-dependent autophagy through the mTOR pathway therefore it might be a viable therapeutic drug for the management of GBM. [ABSTRACT FROM AUTHOR]

The two types of craniopharyngioma, adamantinomatous (ACP) and papillary (PCP), are clinically relevant tumours in children and adults. Although the biology of primary craniopharyngioma is starting to be unravelled, little is known about the biology of recurrence. To fill this gap in knowledge, we have analysed through methylation array, RNA sequencing and pERK1/2 immunohistochemistry a cohort of paired primary and recurrent samples (32 samples from 14 cases of ACP and 4 cases of PCP). We show the presence of copy number alterations and clonal evolution across recurrence in 6 cases of ACP, and analysis of additional whole genome sequencing data from the Children's Brain Tumour Network confirms chromosomal arm copy number changes in at least 7/67 ACP cases. The activation of the MAPK/ERK pathway, a feature previously shown in primary ACP, is observed in all but one recurrent cases of ACP. The only ACP without MAPK activation is an aggressive case of recurrent malignant human craniopharyngioma harbouring a CTNNB1 mutation and loss of TP53. Providing support for a functional role of this TP53 mutation, we show that Trp53 loss in a murine model of ACP results in aggressive tumours and reduced mouse survival. Finally, we characterise the tumour immune infiltrate showing differences in the cellular composition and spatial distribution between ACP and PCP. Together, these analyses have revealed novel insights into recurrent craniopharyngioma and provided preclinical evidence supporting the evaluation of MAPK pathway inhibitors and immunomodulatory approaches in clinical trials in against recurrent ACP. [ABSTRACT FROM AUTHOR]

Background: Remifentanil, an ultra-short-acting µ-opioid receptor agonist, is commonly used for anesthetic management due to excellent adjustability. Remifentanil is known to cause sinus bradycardia, however, because it has a direct negative chronotropic effect on the cardiac conduction system and there is an indirect negative chronotropic effect via the parasympathetic nervous system. Case presentation: An 8-year-old Japanese boy was diagnosed with acute hydrocephalus due to a brain tumor in the fourth ventricle and underwent emergency surgery. Imaging examination showed brainstem compression. Endoscopic third ventriculostomy and ventriculoperitoneal shunt surgery were scheduled. Remifentanil was started during induction of general anesthesia, but electrocardiogram showed sinus bradycardia, then Wenckebach-type atrioventricular block, and then complete atrioventricular block. Remifentanil was immediately discontinued, and we administered atropine sulfate. Complete atrioventricular block was restored to sinus rhythm. When remifentanil was restarted, however, the electrocardiogram again showed sinus bradycardia, Wenckebach-type atrioventricular block, and then complete atrioventricular block. Remifentanil was again immediately discontinued, we administered adrenaline, and then complete atrioventricular block was restored to sinus rhythm. Fentanyl was used instead of remifentanil with continuous infusion of dopamine. There has since been no further occurrence of complete atrioventricular block. Conclusions: This is the first known case of complete atrioventricular block in a pediatric patient with increased intracranial pressure seemingly caused by administration of remifentanil. [ABSTRACT FROM AUTHOR]

Background: We aimed to investigate whether the HALP score was a predictor of survival in patients with Glioblastoma (GBM). Methods: A total of 84 Glioblastoma (GBM) patients followed in our clinic were included in the study. HALP scores were calculated using the preoperative hemoglobin, albumin, lymphocyte and platelet results of the patients. For the HALP score, a cut-off value was found by examining the area below the receiver operating characteristic (ROC) curve. Patients were divided into two groups as low and high according to this cut-off value. The relationships among the clinical, dermographic and laboratory parameters of the patients were examined using these two groups. Results: Median OS, PFS, HALP score, NLR, PLR were 15 months (1.0–78.0), 8 months (1.0–66.0), 37.39 ± 23.84 (min 6.00-max 132.31), 4.14, 145.07 respectively. A statistically significant correlation was found between HALP score and OS, PFS, NLR, PLR, ECOG-PS status using Spearman's rho test (p = 0.001, p < 0.001, p < 0.001, p < 0.001, p = 0.026 respectively). For the HALP score, a cut-off value of = 37.39 (AUC = 0.698, 95% CI, p < 0.002) was found using ROC analysis. Median OS was 12 (6.99–17.01) months in the low HALP group and 21 (11.37–30.63) months in the high HALP group (p = 0.117). NLR and PLR were significantly lower in the HALP high group (p < 0.001, p < 0.001 respectively). The ratio of receiving treatment was significantly higher in the high HALP group (p < 0.05). In Multivariate analysis, significant results were found for treatment status and ECOG-PS status (p < 0.001, p = 0.038 respectively). Conclusions: The HALP score measured at the beginning of treatment seems to have predictive importance in the prognosis of GBM patients. A HALP score of > 37.39 was associated with prolonged survival in high-grade brain tumors. [ABSTRACT FROM AUTHOR]

Background: Cancer pathology shows disease development and associated molecular features. It provides extensive phenotypic information that is cancer-predictive and has potential implications for planning treatment. Based on the exceptional performance of computational approaches in the field of digital pathogenic, the use of rich phenotypic information in digital pathology images has enabled us to identify low-level gliomas (LGG) from high-grade gliomas (HGG). Because the differences between the textures are so slight, utilizing just one feature or a small number of features produces poor categorization results. Methods: In this work, multiple feature extraction methods that can extract distinct features from the texture of histopathology image data are used to compare the classification outcomes. The successful feature extraction algorithms GLCM, LBP, multi-LBGLCM, GLRLM, color moment features, and RSHD have been chosen in this paper. LBP and GLCM algorithms are combined to create LBGLCM. The LBGLCM feature extraction approach is extended in this study to multiple scales using an image pyramid, which is defined by sampling the image both in space and scale. The preprocessing stage is first used to enhance the contrast of the images and remove noise and illumination effects. The feature extraction stage is then carried out to extract several important features (texture and color) from histopathology images. Third, the feature fusion and reduction step is put into practice to decrease the number of features that are processed, reducing the computation time of the suggested system. The classification stage is created at the end to categorize various brain cancer grades. We performed our analysis on the 821 whole-slide pathology images from glioma patients in the Cancer Genome Atlas (TCGA) dataset. Two types of brain cancer are included in the dataset: GBM and LGG (grades II and III). 506 GBM images and 315 LGG images are included in our analysis, guaranteeing representation of various tumor grades and histopathological features. Results: The fusion of textural and color characteristics was validated in the glioma patients using the 10-fold cross-validation technique with an accuracy equals to 95.8%, sensitivity equals to 96.4%, DSC equals to 96.7%, and specificity equals to 97.1%. The combination of the color and texture characteristics produced significantly better accuracy, which supported their synergistic significance in the predictive model. The result indicates that the textural characteristics can be an objective, accurate, and comprehensive glioma prediction when paired with conventional imagery. Conclusion: The results outperform current approaches for identifying LGG from HGG and provide competitive performance in classifying four categories of glioma in the literature. The proposed model can help stratify patients in clinical studies, choose patients for targeted therapy, and customize specific treatment schedules. [ABSTRACT FROM AUTHOR]

The incidence of lung cancer brain metastasis combined with hemorrhagic cerebral venous sinus thrombosis (CVST) is very rare, and the understanding and treatment experience of this case is insufficient. We reported a case of lung cancer brain metastasis accompanied by venous sinus thrombosis, and describe the diagnosis and treatment plan for colleagues to learn from experience and lessons. Key points: 1. The incidence of lung cancer brain metastasis combined with hemorrhagic CVST is very rare, and the understanding and treatment experience of this case is insufficient. 2. The clinical manifestations of metastatic tumors and CVST in patients are not specific, and the evolution of the medical history should be inquired in detail, with careful physical examination and imaging review. 3. CVST should be considered in patients with risk factors. The incidence of hemorrhagic CVST is relatively rare, and due to atypical symptoms and imaging manifestations, it is easy to miss and misdiagnose. 4. The responsible lesion should be determined based on the evolution of the medical history, the treatment of the responsible lesion should be prioritized, symptoms should be relieved in a timely manner and the optimal treatment time should be strived for. [ABSTRACT FROM AUTHOR]

Glioblastoma (GBM) is the most common malignant brain tumor in adults, which remains incurable and often recurs rapidly after initial therapy. While large efforts have been dedicated to uncover genomic/transcriptomic alternations associated with the recurrence of GBMs, the evolutionary trajectories of matched pairs of primary and recurrent (P-R) GBMs remain largely elusive. It remains challenging to identify genes associated with time to relapse (TTR) and construct a stable and effective prognostic model for predicting TTR of primary GBM patients. By integrating RNA-sequencing and genomic data from multiple datasets of patient-matched longitudinal GBMs of isocitrate dehydrogenase wild-type (IDH-wt), here we examined the associations of TTR with heterogeneities between paired P-R GBMs in gene expression profiles, tumor mutation burden (TMB), and microenvironment. Our results revealed a positive correlation between TTR and transcriptomic/genomic differences between paired P-R GBMs, higher percentages of non-mesenchymal-to-mesenchymal transition and mesenchymal subtype for patients with a short TTR than for those with a long TTR, a high correlation between paired P-R GBMs in gene expression profiles and TMB, and a negative correlation between the fitting level of such a paired P-R GBM correlation and TTR. According to these observations, we identified 55 TTR-associated genes and thereby constructed a seven-gene (ZSCAN10, SIGLEC14, GHRHR, TBX15, TAS2R1, CDKL1, and CD101) prognostic model for predicting TTR of primary IDH-wt GBM patients using univariate/multivariate Cox regression analyses. The risk scores estimated by the model were significantly negatively correlated with TTR in the training set and two independent testing sets. The model also segregated IDH-wt GBM patients into two groups with significantly divergent progression-free survival outcomes and showed promising performance for predicting 1-, 2-, and 3-year progression-free survival rates in all training and testing sets. Our findings provide new insights into the molecular understanding of GBM progression at recurrence and potential targets for therapeutic treatments. [ABSTRACT FROM AUTHOR]

Purpose: Diffuse midline glioma (DMG), H3 K27M-mutant is a type of diffuse high-grade glioma that occurs in the brain midline carrying an extremely poor prognosis under the best efforts of surgery, radiation, and other therapies. For better therapy, we explored the efficacy and toxicity of a novel therapy that combines apatinib and temozolomide in DMG. Methods: A retrospective analysis of 32 patients with DMG who underwent apatinib plus temozolomide treatment was performed. Apatinib was given 500 mg in adults, 250 mg in pediatric patients once daily. Temozolomide was administered at 200 mg/m2/d according to the standard 5/28 days regimen. The main clinical data included basic information of patients, radiological and pathological characteristics of tumors, treatment, adverse reactions, prognosis. Results: The objective response rate was 24.1%, and the disease control rate was 79.3%. The median PFS of all patients was 5.8 months, and median OS was 10.3 months. A total of 236 cycles of treatment were available for safety assessment and the toxicity of the combination therapy was relatively well tolerated. The most common grade 3 toxicities were myelosuppression including leukopenia (5.08%), neutropenia (4.24%), lymphopenia (2.12%), thrombocytopenia (1.69%) and anemia (1.27%). Grade 4 toxicities included neutropenia (2.12%), thrombocytopenia (2.12%) and proteinuria (1.69%). All the adverse events were relieved after symptomatic treatment or dose reduction. Conclusions: Apatinib plus temozolomide could be an effective regimen with manageable toxicities and favorable efficacy and may outperform temozolomide monotherapy, particularly in newly diagnosed adults with tumors located outside the pons. The novel therapy deserves further investigation in adult DMG patients. Key points: 1. The novel therapy that combines apatinib and temozolomide could be an effective regimen with manageable toxicities in DMGs. 2. Apatinib plus temozolomide is a therapeutic improved with favorable efficacy and at least superior to temozolomide monotherapy. 3. It was confirmed that some adult and thalamic subgroup patients with DMG could benefit from the application of apatinib plus temozolomide. [ABSTRACT FROM AUTHOR]

Glioblastoma (GBM) is the most common malignant brain tumor and has a dismal prognosis even under the current first-line treatment, with a 5-year survival rate less than 7%. Therefore, it is important to understand the mechanism of treatment resistance and develop new anti-tumor strategies. Induction of programmed cell death (PCD) has become a promising anti-tumor strategy, but its effectiveness in treating GBM remains controversial. On the one hand, PCD triggers tumor cell death and then release mediators to draw in immune cells, creating a pro-inflammatory tumor microenvironment (TME). One the other hand, mounting evidence suggests that PCD and inflammatory TME will force tumor cells to evolve under survival stress, leading to tumor recurrence. The purpose of this review is to summarize the role of PCD and inflammatory TME in the tumor evolution of GBM and promising methods to overcome tumor evolution. [ABSTRACT FROM AUTHOR]

Background: Glioblastoma are highly malignant type of primary brain tumors. Treatment for glioblastoma multiforme (GBM) generally involves surgery combined with chemotherapy and radiotherapy. However, the development of tumoral chemo- and radioresistance induces complexities in clinical practice. Multiple signaling pathways are known to be involved in radiation-induced cell survival. However, the role of alpha-thalassemia X-linked mutant retardation syndrome (ATRX), a chromatin remodeling protein, in GBM radioresistance remains unclear. Methods: In the present study, the ATRX mutation rate in patients with glioma was obtained from The Cancer Genome Atlas, while its expression analyzed using bioinformatics. Datasets were also obtained from the Gene Expression Omnibus, and ATRX expression levels following irradiation of GBM were determined. The effects of ATRX on radiosensitivity were investigated using a knockdown assays. Results: The present study demonstrated that the ATRX mutation rate in patients with GBM was significantly lower than that in patients with low-grade glioma, and that patients harboring an ATRX mutation exhibited a prolonged survival, compared with to those harboring the wild-type gene. Single-cell RNA sequencing demonstrated that ATRX counts increased 2 days after irradiation, with ATRX expression levels also increasing in U-251MG radioresistant cells. Moreover, the results of in vitro irradiation assays revealed that ATRX expression was increased in U-251MG cells, while ATRX knockdown was associated with increased levels of radiosensitivity. Conclusions: High ATRX expression levels in primary GBM may contribute to high levels of radioresistance. Thus ATRX is a potential target for overcoming the radioresistance in GBM. [ABSTRACT FROM AUTHOR]

Diagnosing brain tumors is a complex and time-consuming process that relies heavily on radiologists' expertise and interpretive skills. However, the advent of deep learning methodologies has revolutionized the field, offering more accurate and efficient assessments. Attention-based models have emerged as promising tools, focusing on salient features within complex medical imaging data. However, the precise impact of different attention mechanisms, such as channel-wise, spatial, or combined attention within the Channel-wise Attention Mode (CWAM), for brain tumor classification remains relatively unexplored. This study aims to address this gap by leveraging the power of ResNet101 coupled with CWAM (ResNet101-CWAM) for brain tumor classification. The results show that ResNet101-CWAM surpassed conventional deep learning classification methods like ConvNet, achieving exceptional performance metrics of 99.83% accuracy, 99.21% recall, 99.01% precision, 99.27% F1-score and 99.16% AUC on the same dataset. This enhanced capability holds significant implications for clinical decision-making, as accurate and efficient brain tumor classification is crucial for guiding treatment strategies and improving patient outcomes. Integrating ResNet101-CWAM into existing brain classification software platforms is a crucial step towards enhancing diagnostic accuracy and streamlining clinical workflows for physicians. [ABSTRACT FROM AUTHOR]

Background: Glioblastoma (GBM) is the most common and aggressive primary brain cancer. The treatment of GBM consists of a combination of surgery and subsequent oncological therapy, i.e., radiotherapy, chemotherapy, or their combination. If postoperative oncological therapy involves irradiation, magnetic resonance imaging (MRI) is used for radiotherapy treatment planning. Unfortunately, in some cases, a very early worsening (progression) or return (recurrence) of the disease is observed several weeks after the surgery and is called rapid early progression (REP). Radiotherapy planning is currently based on MRI for target volumes definitions in many radiotherapy facilities. However, patients with REP may benefit from targeting radiotherapy with other imaging modalities. The purpose of the presented clinical trial is to evaluate the utility of 11C-methionine in optimizing radiotherapy for glioblastoma patients with REP. Methods: This study is a nonrandomized, open-label, parallel-setting, prospective, monocentric clinical trial. The main aim of this study was to refine the diagnosis in patients with GBM with REP and to optimize subsequent radiotherapy planning. Glioblastoma patients who develop REP within approximately 6 weeks after surgery will undergo 11C-methionine positron emission tomography (PET/CT) examinations. Target volumes for radiotherapy are defined using both standard planning T1-weighted contrast-enhanced MRI and PET/CT. The primary outcome is progression-free survival defined using RANO criteria and compared to a historical cohort with REP treated without PET/CT optimization of radiotherapy. Discussion: PET is one of the most modern methods of molecular imaging. 11C-Methionine is an example of a radiolabelled (carbon 11) amino acid commonly used in the diagnosis of brain tumors and in the evaluation of response to treatment. Optimized radiotherapy may also have the potential to cover those regions with a high risk of subsequent progression, which would not be identified using standard-of-care MRI for radiotherapy planning. This is one of the first study focused on radiotherapy optimization for subgroup of patinets with REP. Trial Registration: NCT05608395, registered on 8.11.2022 in clinicaltrials.gov; EudraCT Number: 2020–000640-64, registered on 26.5.2020 in clinicaltrialsregister.eu. Protocol ID: MOU-2020–01, version 3.2, date 18.09.2020. [ABSTRACT FROM AUTHOR]

Background: Gliomas are aggressive malignant tumors, with poor prognosis. There is an unmet need for the discovery of new, non-invasive biomarkers for differential diagnosis, prognosis, and management of brain tumors. Our objective is to validate four plasma biomarkers – glial fibrillary acidic protein (GFAP), neurofilament light (NEFL), matrix metalloprotease 3 (MMP3) and fatty acid binding protein 4 (FABP4) – and compare them with established brain tumor molecular markers and survival. Methods: Our cohort consisted of patients with benign and malignant brain tumors (GBM = 77, Astrocytomas = 26, Oligodendrogliomas = 23, Secondary tumors = 35, Meningiomas = 70, Schwannomas = 15, Pituitary adenomas = 15, Normal individuals = 30). For measurements, we used ultrasensitive electrochemiluminescence multiplexed immunoassays. Results: High plasma GFAP concentration was associated with GBM, low GFAP and high FABP4 were associated with meningiomas, and low GFAP and low FABP4 were associated with astrocytomas and oligodendrogliomas. NEFL was associated with progression of disease. Several prognostic genetic alterations were significantly associated with all plasma biomarker levels. We found no independent associations between plasma GFAP, NEFL, FABP4 and MMP3, and overall survival. The candidate biomarkers could not reliably discriminate GBM from primary or secondary CNS lymphomas. Conclusions: GFAP, NEFL, FABP4 and MMP3 are useful for differential diagnosis and prognosis, and are associated with molecular changes in gliomas. [ABSTRACT FROM AUTHOR]

Background: Glioblastoma (GBM) is a highly heterogeneous, recurrent and aggressively invasive primary malignant brain tumor. The heterogeneity of GBM results in poor targeted therapy. Therefore, the aim of this study is to depict the cellular landscape of GBM and its peritumor from a single-cell perspective. Discovering new cell subtypes and biomarkers, and providing a theoretical basis for precision therapy. Methods: We collected 8 tissue samples from 4 GBM patients to perform 10 × single-cell transcriptome sequencing. Quality control and filtering of data by Seurat package for clustering. Inferring copy number variations to identify malignant cells via the infercnv package. Functional enrichment analysis was performed by GSVA and clusterProfiler packages. STRING database and Cytoscape software were used to construct protein interaction networks. Inferring transcription factors by pySCENIC. Building cell differentiation trajectories via the monocle package. To infer intercellular communication networks by CellPhoneDB software. Results: We observed that the tumor microenvironment (TME) varies among different locations and different GBM patients. We identified a proliferative cluster of oligodendrocytes with high expression of mitochondrial genes. We also identified two clusters of myeloid cells, one primarily located in the peritumor exhibiting an M1 phenotype with elevated TNFAIP8L3 expression, and another in the tumor and peritumor showing a proliferative tendency towards an M2 phenotype with increased DTL expression. We identified XIST, KCNH7, SYT1 and DIAPH3 as potential factors associated with the proliferation of malignant cells in GBM. Conclusions: These biomarkers and cell clusters we discovered may serve as targets for treatment. Targeted drugs developed against these biomarkers and cell clusters may enhance treatment efficacy, optimize immune therapy strategies, and improve the response rates of GBM patients to immunotherapy. Our findings provide a theoretical basis for the development of individualized treatment and precision medicine for GBM, which may be used to improve the survival of GBM patients. [ABSTRACT FROM AUTHOR]

Background: To investigate whether the intraoperative superb microvascular imaging(SMI) technique helps evaluate lesion boundaries compared with conventional grayscale ultrasound in brain tumor surgery and to explore factors that may be associated with complete radiographic resection. Methods: This study enrolled 57 consecutive brain tumor patients undergoing surgery. During the operation, B-mode and SMI ultrasound evaluated the boundaries of brain tumors. MRI before and within 48h after surgery was used as the gold standard to evaluate gross-total resection(GTR). The ultrasound findings and GTR results were analyzed to determine the imaging factors related to GTR. Results: A total of 57 patients were enrolled in the study, including 32 males and 25 females, with an average age of 53.4 ± 14.1 years old(range 19 ~ 80). According to the assessment criteria of MRI, before and within 48 h after the operation, 37(63.9%) cases were classified as GTR, and 20(35.1%) cases were classified as GTR. In comparing tumor interface definition between B-mode and SMI mode, SMI improved HGG boundary recognition in 5 cases(P = 0.033). The results showed that the tumor size ≥ 5 cm and unclear ultrasonic boundary were independent risk factors for nGTR (OR>1, P<0.05). Conclusions: As an innovative intraoperative doppler technique in neurosurgery, SMI can effectively demarcate the tumor's boundary and help achieve GTR as much as possible. [ABSTRACT FROM AUTHOR]

Background: There are limited data available, particularly in low- and middle-income countries (LMICs), on the long-term quality of life (QoL) and family functioning of primary caregivers of children and young people (CYPs) affected by primary brain tumors (PBTs). This study aimed to assess the factors associated with the mean change in QoL and family functioning scores of primary caregivers of CYP patients with PBTs 12 months posttreatment. Methods: This prospective cohort study enrolled CYPs aged 5–21 years with newly diagnosed PBTs and their primary caregivers. The study was carried out between November 2020 and July 2023. The primary caregivers of CYPs were recruited from two major tertiary care centers in Karachi, Pakistan. The primary caregivers QoL were assessed by the Pediatric Quality of Life Inventory (PedsQL) Family Impact Module. The assessment was undertaken by a psychologist at the time of diagnosis and 12 months posttreatment. The data were analyzed with STATA version 12. Results: Forty-eight CYPs with newly diagnosed PBTs and their primary caregivers (46 mothers and 2 fathers) were enrolled. At 12 months posttreatment, 25 (52%) CYPs and their primary caregivers (mothers) were reassessed, and 23 (48%) were lost to follow-up. On multivariable analysis, a significant decrease in mothers' mean 12-month posttreatment QoL and family functioning scores was associated with CYP having posttreatment seizures (beta= -10.2; 95% CI: -18.4 to -2.0) and with the financial burden associated with the CYP's illness (beta= -0.3; 95% CI: -0.4 to -0.1). However, in those cases where CYP had higher posttreatment quality of life scores (beta = 0.4; 95% CI = 0.1, 0.6) and posttreatment higher verbal intelligence scores (beta = 0.1; 95% CI = 0.01, 0.3), the mothers' QoL and family functioning scores were significantly greater. Conclusion: We found a significant decrease in QoL of mothers who had a high financial burden and whose CYP had posttreatment seizures. However, those whose CYPs had higher posttreatment verbal intelligence scores and quality of life scores had significantly greater QoL scores. Identification of the factors that influence primary caregivers QoL has the potential to aid in the development of targeted strategies to alleviate stressors and improve the overall quality of life for primary caregivers and their children who are at high risk. [ABSTRACT FROM AUTHOR]

Background: Pediatric-type diffuse high-grade glioma (pHGG) is the most frequent malignant brain tumor in children and can be subclassified into multiple entities. Fusion genes activating the MET receptor tyrosine kinase often occur in infant-type hemispheric glioma (IHG) but also in other pHGG and are associated with devastating morbidity and mortality. Methods: To identify new treatment options, we established and characterized two novel orthotopic mouse models harboring distinct MET fusions. These included an immunocompetent, murine allograft model and patient-derived orthotopic xenografts (PDOX) from a MET-fusion IHG patient who failed conventional therapy and targeted therapy with cabozantinib. With these models, we analyzed the efficacy and pharmacokinetic properties of three MET inhibitors, capmatinib, crizotinib and cabozantinib, alone or combined with radiotherapy. Results: Capmatinib showed superior brain pharmacokinetic properties and greater in vitro and in vivo efficacy than cabozantinib or crizotinib in both models. The PDOX models recapitulated the poor efficacy of cabozantinib experienced by the patient. In contrast, capmatinib extended survival and induced long-term progression-free survival when combined with radiotherapy in two complementary mouse models. Capmatinib treatment increased radiation-induced DNA double-strand breaks and delayed their repair. Conclusions: We comprehensively investigated the combination of MET inhibition and radiotherapy as a novel treatment option for MET-driven pHGG. Our seminal preclinical data package includes pharmacokinetic characterization, recapitulation of clinical outcomes, coinciding results from multiple complementing in vivo studies, and insights into molecular mechanism underlying increased efficacy. Taken together, we demonstrate the groundbreaking efficacy of capmatinib and radiation as a highly promising concept for future clinical trials. [ABSTRACT FROM AUTHOR]

Background: Primary malignant brain tumours are more than one-third of all brain tumours and despite the molecular investigation to identify cancer driver mutations, the current therapeutic options available are challenging due to high intratumour heterogeneity. In addition, an immunosuppressive and inflammatory tumour microenvironment strengthens cancer progression. Therefore, we defined an immune and inflammatory profiling of meningioma and glial tumours to elucidate the role of the immune infiltration in these cancer types. Methods: Using tissue microarrays of 158 brain tumour samples, we assessed CD3, CD4, CD8, CD20, CD138, Granzyme B (GzmB), 5-Lipoxygenase (5-LOX), Programmed Death-Ligand 1 (PD-L1), O-6-Methylguanine-DNA Methyltransferase (MGMT) and Transglutaminase 2 (TG2) expression by immunohistochemistry (IHC). IHC results were correlated using a Spearman correlation matrix. Transcript expression, correlation, and overall survival (OS) analyses were evaluated using public datasets available on GEPIA2 in Glioblastoma (GBM) and Lower Grade Glioma (LGG) cohorts. Results: Seven out of ten markers showed a significantly different IHC expression in at least one of the evaluated cohorts whereas CD3, CD4 and 5-LOX were differentially expressed between GBMs and astrocytomas. Correlation matrix analysis revealed that 5-LOX and GzmB expression were associated in both meningiomas and GBMs, whereas 5-LOX expression was significantly and positively correlated to TG2 in both meningioma and astrocytoma cohorts. These findings were confirmed with the correlation analysis of TCGA-GBM and LGG datasets. Profiling of mRNA levels indicated a significant increase in CD3 (CD3D, CD3E), and CD138 (SDC1) expression in GBM compared to control tissues. CD4 and 5-LOX (ALOX5) mRNA levels were significantly more expressed in tumour samples than in normal tissues in both GBM and LGG. In GBM cohort, GzmB (GZMB), SDC1 and MGMT gene expression predicted a poor overall survival (OS). Moreover, in LGG cohort, an increased expression of CD3 (CD3D, CD3E, CD3G), CD8 (CD8A), GZMB, CD20 (MS4A1), SDC1, PD-L1, ALOX5, and TG2 (TGM2) genes was associated with worse OS. Conclusions: Our data have revealed that there is a positive and significant correlation between the expression of 5-LOX and GzmB, both at RNA and protein level. Further evaluation is needed to understand the interplay of 5-LOX and immune infiltration in glioma progression. [ABSTRACT FROM AUTHOR]

Backgrounds: Primary brain tumors (PBTs) are uncommon, but they significantly increase the risk of disability and death. There is a deficiency of data concerning the epidemiology and anatomical distribution of PBTs among adults in Palestine. Methods: A retrospective descriptive study in which data were collected from the clinical reports of Palestinian patients diagnosed with PBTs at Al-Makassed Hospital during the period (2018–2023). Results: In Palestinian adolescents and adults, the incidence rate of PBTs was 3.92 per 100,000 person-years. Glioblastoma (18.8%) was the most common type identified, and it was more common in males. Non-malignant tumors were more common than malignant tumors (2.41 vs. 1.52 per 100,000). The mortality rate from PBTs was 4.8%. The most common initial symptom was headaches, and it occurred more with non-malignant tumors (57.28% vs. 42.72%, p-value < 0.001). Cerebral meninges (26.3%) were the most common location for primary brain tumors (p-value < 0.001). Conclusion: This is the first study of primary brain tumor epidemiology in Palestine. The overall incidence of PBTs in Palestinian adolescents and adults was 3.96 per 100,000, which was lower than the incidence rate of primary brain tumors worldwide. More studies on the epidemiology and distribution of PBTs in Palestine are recommended. [ABSTRACT FROM AUTHOR]

Brain tumor diagnosis using MRI scans poses significant challenges due to the complex nature of tumor appearances and variations. Traditional methods often require extensive manual intervention and are prone to human error, leading to misdiagnosis and delayed treatment. Current approaches primarily include manual examination by radiologists and conventional machine learning techniques. These methods rely heavily on feature extraction and classification algorithms, which may not capture the intricate patterns present in brain MRI images. Conventional techniques often suffer from limited accuracy and generalizability, mainly due to the high variability in tumor appearance and the subjective nature of manual interpretation. Additionally, traditional machine learning models may struggle with the high-dimensional data inherent in MRI images. To address these limitations, our research introduces a deep learning-based model utilizing convolutional neural networks (CNNs).Our model employs a sequential CNN architecture with multiple convolutional, max-pooling, and dropout layers, followed by dense layers for classification. The proposed model demonstrates a significant improvement in diagnostic accuracy, achieving an overall accuracy of 98% on the test dataset. The proposed model demonstrates a significant improvement in diagnostic accuracy, achieving an overall accuracy of 98% on the test dataset. The precision, recall, and F1-scores ranging from 97 to 98% with a roc-auc ranging from 99 to 100% for each tumor category further substantiate the model's effectiveness. Additionally, the utilization of Grad-CAM visualizations provides insights into the model's decision-making process, enhancing interpretability. This research addresses the pressing need for enhanced diagnostic accuracy in identifying brain tumors through MRI imaging, tackling challenges such as variability in tumor appearance and the need for rapid, reliable diagnostic tools. [ABSTRACT FROM AUTHOR]

Glioblastoma (GBM) is the most aggressive primary brain tumor with low survival rate. Currently, temozolomide (TMZ) is the first-line drug for GBM treatment of which efficacy is unfortunately hindered by short circulation time and drug resistance associated to hypoxia and redox tumor microenvironment. Herein, a dual-targeted and multi-responsive nanoplatform is developed by loading TMZ in hollow manganese dioxide nanoparticles functionalized by polydopamine and targeting ligands RAP12 for photothermal and receptor-mediated dual-targeted delivery, respectively. After accumulated in GBM tumor site, the nanoplatform could respond to tumor microenvironment and simultaneously release manganese ion (Mn2+), oxygen (O2) and TMZ. The hypoxia alleviation via O2 production, the redox balance disruption via glutathione consumption and the reactive oxygen species generation, together would down-regulate the expression of O6-methylguanine-DNA methyltransferase under TMZ medication, which is considered as the key to drug resistance. These strategies could synergistically alleviate hypoxia microenvironment and overcome TMZ resistance, further enhancing the anti-tumor effect of chemotherapy/chemodynamic therapy against GBM. Additionally, the released Mn2+ could also be utilized as a magnetic resonance imaging contrast agent for monitoring treatment efficiency. Our study demonstrated that this nanoplatform provides an alternative approach to the challenges including low delivery efficiency and drug resistance of chemotherapeutics, which eventually appears to be a potential avenue in GBM treatment. [ABSTRACT FROM AUTHOR]

Brain tumor classification using MRI images is a crucial yet challenging task in medical imaging. Accurate diagnosis is vital for effective treatment planning but is often hindered by the complex nature of tumor morphology and variations in imaging. Traditional methodologies primarily rely on manual interpretation of MRI images, supplemented by conventional machine learning techniques. These approaches often lack the robustness and scalability needed for precise and automated tumor classification. The major limitations include a high degree of manual intervention, potential for human error, limited ability to handle large datasets, and lack of generalizability to diverse tumor types and imaging conditions.To address these challenges, we propose a federated learning-based deep learning model that leverages the power of Convolutional Neural Networks (CNN) for automated and accurate brain tumor classification. This innovative approach not only emphasizes the use of a modified VGG16 architecture optimized for brain MRI images but also highlights the significance of federated learning and transfer learning in the medical imaging domain. Federated learning enables decentralized model training across multiple clients without compromising data privacy, addressing the critical need for confidentiality in medical data handling. This model architecture benefits from the transfer learning technique by utilizing a pre-trained CNN, which significantly enhances its ability to classify brain tumors accurately by leveraging knowledge gained from vast and diverse datasets.Our model is trained on a diverse dataset combining figshare, SARTAJ, and Br35H datasets, employing a federated learning approach for decentralized, privacy-preserving model training. The adoption of transfer learning further bolsters the model's performance, making it adept at handling the intricate variations in MRI images associated with different types of brain tumors. The model demonstrates high precision (0.99 for glioma, 0.95 for meningioma, 1.00 for no tumor, and 0.98 for pituitary), recall, and F1-scores in classification, outperforming existing methods. The overall accuracy stands at 98%, showcasing the model's efficacy in classifying various tumor types accurately, thus highlighting the transformative potential of federated learning and transfer learning in enhancing brain tumor classification using MRI images. [ABSTRACT FROM AUTHOR]

Background: LncRNAs regulate tumorigenesis and development in a variety of cancers. We substantiate for the first time that LINC00606 is considerably expressed in glioblastoma (GBM) patient specimens and is linked with adverse prognosis. This suggests that LINC00606 may have the potential to regulate glioma genesis and progression, and that the biological functions and molecular mechanisms of LINC00606 in GBM remain largely unknown. Methods: The expression of LINC00606 and ATP11B in glioma and normal brain tissues was evaluated by qPCR, and the biological functions of the LINC00606/miR-486-3p/TCF12/ATP11B axis in GBM were verified through a series of in vitro and in vivo experiments. The molecular mechanism of LINC00606 was elucidated by immunoblotting, FISH, RNA pulldown, CHIP-qPCR, and a dual-luciferase reporter assay. Results: We demonstrated that LINC00606 promotes glioma cell proliferation, clonal expansion and migration, while reducing apoptosis levels. Mechanistically, on the one hand, LINC00606 can sponge miR-486-3p; the target gene TCF12 of miR-486-3p affects the transcriptional initiation of LINC00606, PTEN and KLLN. On the other hand, it can also regulate the PI3K/AKT signaling pathway to mediate glioma cell proliferation, migration and apoptosis by binding to ATP11B protein. Conclusions: Overall, the LINC00606/miR-486-3p/TCF12/ATP11B axis is involved in the regulation of GBM progression and plays a role in tumor regulation at transcriptional and post-transcriptional levels primarily through LINC00606 sponging miR-486-3p and targeted binding to ATP11B. Therefore, our research on the regulatory network LINC00606 could be a novel therapeutic strategy for the treatment of GBM. LINC00606 is highly expressed in GBM patients with carcinogenic function and correlated with poor prognosis. LINC00606 regulates glioblastoma progression by sponging miR-486-3p and interacting with ATP11B. [ABSTRACT FROM AUTHOR]

This study addresses the critical challenge of detecting brain tumors using MRI images, a pivotal task in medical diagnostics that demands high accuracy and interpretability. While deep learning has shown remarkable success in medical image analysis, there remains a substantial need for models that are not only accurate but also interpretable to healthcare professionals. The existing methodologies, predominantly deep learning-based, often act as black boxes, providing little insight into their decision-making process. This research introduces an integrated approach using ResNet50, a deep learning model, combined with Gradient-weighted Class Activation Mapping (Grad-CAM) to offer a transparent and explainable framework for brain tumor detection. We employed a dataset of MRI images, enhanced through data augmentation, to train and validate our model. The results demonstrate a significant improvement in model performance, with a testing accuracy of 98.52% and precision-recall metrics exceeding 98%, showcasing the model's effectiveness in distinguishing tumor presence. The application of Grad-CAM provides insightful visual explanations, illustrating the model's focus areas in making predictions. This fusion of high accuracy and explainability holds profound implications for medical diagnostics, offering a pathway towards more reliable and interpretable brain tumor detection tools. [ABSTRACT FROM AUTHOR]

Brain tumors pose a significant medical challenge necessitating precise detection and diagnosis, especially in Magnetic resonance imaging(MRI). Current methodologies reliant on traditional image processing and conventional machine learning encounter hurdles in accurately discerning tumor regions within intricate MRI scans, often susceptible to noise and varying image quality. The advent of artificial intelligence (AI) has revolutionized various aspects of healthcare, providing innovative solutions for diagnostics and treatment strategies. This paper introduces a novel AI-driven methodology for brain tumor detection from MRI images, leveraging the EfficientNetB2 deep learning architecture. Our approach incorporates advanced image preprocessing techniques, including image cropping, equalization, and the application of homomorphic filters, to enhance the quality of MRI data for more accurate tumor detection. The proposed model exhibits substantial performance enhancement by demonstrating validation accuracies of 99.83%, 99.75%, and 99.2% on BD-BrainTumor, Brain-tumor-detection, and Brain-MRI-images-for-brain-tumor-detection datasets respectively, this research holds promise for refined clinical diagnostics and patient care, fostering more accurate and reliable brain tumor identification from MRI images. All data is available on Github: https://github.com/muskan258/Brain-Tumor-Detection-from-MRI-Images-Utilizing-EfficientNetB2). [ABSTRACT FROM AUTHOR]

Background: Glioblastoma is a malignant and aggressive type of central nevous system malignancy characterized by many distinct biological features including extensive hypoxia. Hypoxia in glioblatoma associates with complex signaling patterns including activation of several pathways such as MAPK, PI3K-AKT/mTOR and IL-6/JAK/STAT3 with the master regulator HIF-1, which in turn drive particular tumor behaviors determining, in the end, treatment outcomes and patients fate. Thus, the present study was designed to investigate the expression of selected hypoxia related factors including STAT3 in a small set of long-term surviving glioma patients. Methods: The expression of selected hypoxia related factors including STAT3 was evaluated in a time series of formalin fixed paraffin embedded and cryopreserved glioma samples from repeatedly resected patients. In addition, comparative studies were also conducted on primary glioma cells derived from original patient samples, stabilized glioma cell lines and tumor-xenograft mice model. Obtained data were correlated with clinical findings too. Results: Glioblastoma samples of the analyzed patients displayed heterogeneity in the expression of hypoxia- related and EMT markers with most interesting trend being observed in pSTAT3. This heterogeneity was subsequently confirmed in other employed models (primocultures derived from glioblastoma tissue resections, cryopreserved tumor specimens, stabilized glioblastoma cell line in vitro and in vivo) and concerned, in particular, STAT3 expression which remained stable. In addition, subsequent studies on the role of STAT3 in the context of glioblastoma hypoxia demonstrated opposing effects of its deletion on cell viability as well as the expression of hypoxia and EMT markers. Conclusions: Our results suport the importance of STAT3 expression and activity in the context of hypoxia in malignant glioblastoma long-term surviving glioma patients while emphasizing heterogeneity of biological outcomes in varying employed tumor models. [ABSTRACT FROM AUTHOR]

Background: Protein arginine methyltransferase 6 (PRMT6) plays a crucial role in various pathophysiological processes and diseases. Glioblastoma (GBM; WHO Grade 4 glioma) is the most common and lethal primary brain tumor in adults, with a prognosis that is extremely poor, despite being less common than other systemic malignancies. Our current research finds PRMT6 upregulated in GBM, enhancing tumor malignancy. Yet, the specifics of PRMT6's regulatory processes and potential molecular mechanisms in GBM remain largely unexplored. Methods: PRMT6's expression and prognostic significance in GBM were assessed using glioma public databases, immunohistochemistry (IHC), and immunoblotting. Scratch and Transwell assays examined GBM cell migration and invasion. Immunoblotting evaluated the expression of epithelial-mesenchymal transition (EMT) and Wnt-β-catenin pathway-related proteins. Dual-luciferase reporter assays and ChIP-qPCR assessed the regulatory relationship between PRMT6 and YTHDF2. An in situ tumor model in nude mice evaluated in vivo conditions. Results: Bioinformatics analysis indicates high expression of PRMT6 and YTHDF2 in GBM, correlating with poor prognosis. Functional experiments show PRMT6 and YTHDF2 promote GBM migration, invasion, and EMT. Mechanistic experiments reveal PRMT6 and CDK9 co-regulate YTHDF2 expression. YTHDF2 binds and promotes the degradation of negative regulators APC and GSK3β mRNA of the Wnt-β-catenin pathway, activating it and consequently enhancing GBM malignancy. Conclusions: Our results demonstrate the PRMT6-YTHDF2-Wnt-β-Catenin axis promotes GBM migration, invasion, and EMT in vitro and in vivo, potentially serving as a therapeutic target for GBM. [ABSTRACT FROM AUTHOR]

Methylation class "CNS tumor with BCOR/BCOR(L1)-fusion" was recently defined based on methylation profiling and tSNE analysis of a series of 21 neuroepithelial tumors with predominant presence of a BCOR fusion and/or characteristic CNV breakpoints at chromosome 22q12.31 and chromosome Xp11.4. Clear diagnostic criteria are still missing for this tumor type, specially that BCOR/BCOR(L1)-fusion is not a consistent finding in these tumors despite being frequent and that none of the Heidelberger classifier versions is able to clearly identify these cases, in particular tumors with alternative fusions other than those involving BCOR, BCORL1, EP300 and CREBBP. In this study, we introduce a BCOR::CREBBP fusion in an adult patient with a right temporomediobasal tumor, for the first time in association with methylation class "CNS tumor with BCOR/BCOR(L1)-fusion" in addition to 35 cases of CNS neuroepithelial tumors with molecular and histopathological characteristics compatible with "CNS tumor with BCOR/BCOR(L1)-fusion" based on a comprehensive literature review and data mining in the repository of 23 published studies on neuroepithelial brain Tumors including 7207 samples of 6761 patients. Based on our index case and the 35 cases found in the literature, we suggest the archetypical histological and molecular features of "CNS tumor with BCOR/BCOR(L1)-fusion". We also present four adult diffuse glioma cases including GBM, IDH-Wildtype and Astrocytoma, IDH-Mutant with CREBBP fusions and describe the necessity of complementary molecular analysis in "CNS tumor with BCOR/BCOR(L1)-alterations for securing a final diagnosis. [ABSTRACT FROM AUTHOR]

Glioblastoma (GBM) is a fatal brain tumour that is traditionally diagnosed based on histological features. Recent molecular profiling studies have reshaped the World Health Organization approach in the classification of central nervous system tumours to include more pathogenetic hallmarks. These studies have revealed that multiple oncogenic pathways are dysregulated, which contributes to the aggressiveness and resistance of GBM. Such findings have shed light on the molecular vulnerability of GBM and have shifted the disease management paradigm from chemotherapy to targeted therapies. Targeted drugs have been developed to inhibit oncogenic targets in GBM, including receptors involved in the angiogenic axis, the signal transducer and activator of transcription 3 (STAT3), the PI3K/AKT/mTOR signalling pathway, the ubiquitination-proteasome pathway, as well as IDH1/2 pathway. While certain targeted drugs showed promising results in vivo, the translatability of such preclinical achievements in GBM remains a barrier. We also discuss the recent developments and clinical assessments of targeted drugs, as well as the prospects of cell-based therapies and combinatorial therapy as novel ways to target GBM. Targeted treatments have demonstrated preclinical efficacy over chemotherapy as an alternative or adjuvant to the current standard of care for GBM, but their clinical efficacy remains hindered by challenges such as blood-brain barrier penetrance of the drugs. The development of combinatorial targeted therapies is expected to improve therapeutic efficacy and overcome drug resistance. [ABSTRACT FROM AUTHOR]