Type Of Brain Tumor Research Articles

Brain Tumor Recurrence vs. Radiation Necrosis Classification and Patient Survivability Prediction.

GB (Glioblastoma WHO Grade 4) is the most aggressive type of brain tumor in adults that has a short survival rate even after aggressive treatment with surgery and radiation therapy. The changes in magnetic resonance imaging (MRI) for patients with GB after radiotherapy are indicative of either radiation-induced necrosis (RN) or recurrent brain tumor (rBT). Screening for rBT and RN at an early stage is crucial for facilitating faster treatment and better outcomes for the patients. Differentiating rBT from RN is challenging as both may present with similar radiological and clinical characteristics on MRI. Moreover, learning-based rBT versus RN classification using MRI may suffer from class imbalance due to a lack of patient data. While synthetic data generation using generative models has shown promise to address class imbalances, the underlying data representation may be different in synthetic or augmented data. This study proposes computational modeling with statistically rigorous repeated random sub-sampling to balance the subset sample size for rBT and RN classification. The proposed pipeline includes multiresolution radiomic feature (MRF) extraction followed by feature selection with statistical significance testing (p<0.05). The five-fold cross validation results show the proposed model with MRF features classifies rBT from RN with an area under the curve (AUC) of 0.892±0.055. Moreover, considering the dependence between survival time and censoring time (where patients are not followed up until death), the feasibility of using MRF radiomic features as a non-invasive biomarker to identify patients who are at higher risk of recurrence or radiation necrosis is demonstrated. The cross-validated results show that the MRF model provides the best overall survival prediction with an AUC of 0.77±0.032. Comparison with state-of-the-art methods suggest the proposed method is effective in RN versus rBT classification and patient survivability prediction.

Clinical Presentation, Approach and Outcome of Gliosarcoma: A Series of 12 Cases

Gliosarcoma (GSM) is a rare and aggressive type of brain tumour with limited treatment options and a poor prognosis. The present case series aimed to provide further insights into the clinical features, treatment outcomes, and prognosis of GSM. Medical records of 12 histologically confirmed cases of GSM were analysed from 2018 to 2022, revealing a male predominance and a median age of 54 years. The most common symptoms were headache and vomiting due to raised intracranial pressure. All patients underwent maximal safe resection followed by concurrent chemoradiation and adjuvant chemotherapy with Temozolomide (TMZ). KaplanMeier analysis showed a median Progression-free Survival (PFS) and overall survival of 8 and 12 months, respectively. The study revealed that the optimal treatment for primary GSM remains a therapeutic dilemma due to the rarity of the disease and the heterogeneity of the patient population and treatment regimens employed. The present study provides valuable insights into the clinical presentation and management of primary GSM in India and highlights the need for further research to improve outcomes for these patients.

LSTM-NAS-Net: Enhanced Brain Tumor Segmentation in MRI and CT Images using LSTM-Autoencoder-based Neural Architecture Search

Brain Tumour (BT) a mass or a lump or a growth which occurs due to abnormal cell division or unusual growth of cells in the brain tissue. Initially, the two major types of BT are Primary BT and Secondary BT, the tumour that originate from brain is known as Primary BT and it may be cancerous or non-cancerous. The tumour the initiates from other part of the body and spreads to the brain is stated as secondary BT. Diagnosing BT generally involves a multiple investigation method, such as MRI, CT, PET, SPECT as well as the neurological examinations and blood investigations, whereas some of the patients may need biopsies to evaluate the tumour size and stage. Here we use MRI and CT images for BT segmentation whereas these modalities play a major role in diagnosing, treating, planning and monitoring the BT patients. Moreover, the multimodal data can provide a quantitative information’s about the tumour size, shape, volume and texture. While segmenting the BT the lack of segmentation methods and the interpretability of the segmented regions are limited. To overcome this, we propose a novel LSTM autoencoder bas NAS method which is used for the extracting the BT features and these features can be fused using Contextual Integration Module (CIM) and segmented using the Segmentation Guided Regulizer (SGR) which helps to overcome the stated issues. Finally, the performance metrices are calculated by comparing with the state-of -the -art methods and our method achieves a best segmenting metrices.

Pivotal Role of FBXW4 in Glioma Progression and Prognosis.

Glioma stands as one of the most formidable brain tumor types, with patient outcomes remaining bleak even in the face of advancements in treatment modalities. FBXW4, a constituent of the F-box and WD repeat domain-containing protein family, is recognized for its participation in diverse cellular activities, including those related to tumor dynamics. Yet, the therapeutic relevance and specific role of FBXW4 in the context of glioma are not well defined. This study aims to elucidate the functional dynamics and significance of FBXW4 in glioma cases. This research undertook a comprehensive analysis of FBXW4's expression patterns and clinical relevance in glioma by harnessing data from the TCGA and GTEx databases. The investigation revealed a distinct downregulation of FBXW4 in glioma tissues compared to normal brain counterparts, with a pronounced correlation between FBXW4 levels and disease severity. Intriguingly, FBXW4 expression inversely related to WHO tumor grades, with the most advanced grade IV gliomas exhibiting the lowest FBXW4 levels, whereas grade II tumors demonstrated the highest. Cases presenting with IDH1/2 mutations or 1p/19q codeletions were also associated with elevated FBXW4 levels. Furthermore, diminished FBXW4 expression aligned with an increased risk of mortality. The findings suggest that FBXW4 holds promise as a prognostic marker and a potential therapeutic avenue in glioma management. Nonetheless, future research is imperative to decode the intricate signaling pathways involving FBXW4 and to understand its broader clinical ramifications in glioma treatment paradigms.

Enhancing Medical Diagnostics: Integrating AI for precise Brain Tumour Detection

Recent strides in artificial intelligence (AI) and deep learning techniques have propelled the development of an AI-powered brain tumour detection model. This study blends multilevel thresholding, neural network optimisation, and image preprocessing to craft a robust AI model capable of accurately categorising diverse brain tumour types and normal cases. Through rigorous testing with a comprehensive dataset of 1747 images, the model achieves an accuracy of 92%. Its integration into a user-friendly smartphone app, MediScan, enhances accessibility and practicality. The app provides heatmap visualisations and generates diagnostic reports, supporting medical professionals in making swift decisions. The model prioritises interpretability enhancement and has the potential to cultivate collaboration between AI experts and medical practitioners, thus advancing the field of brain tumour detection and diagnosis. While promising, the model demands computational resources and diverse datasets. This research also highlights AI's potential to transform healthcare diagnostics, ensuring precise and efficient brain tumour identification.

StemnesScoRe: an R package to estimate the stemness of glioma cancer cells at single-cell resolution.

Glioblastoma is the most heterogeneous and the most difficult-to-treat type of brain tumor and one of the deadliest among all cancers. The high plasticity of glioma cancer stem cells and the resistance they develop against multiple modalities of therapy, along with their high heterogeneity, are the main challenges faced during treatment of glioblastoma. Therefore, a better understanding of the stemness characteristics of glioblastoma cells is needed. With the development of various single-cell technologies and increasing applications of machine learning, indices based on transcriptomic and/or epigenomic data have been developed to quantitatively measure cellular states and stemness. In this study, we aimed to develop a glioma-specific stemness score model using scATAC-seq data for the first time. We first applied three powerful machine-learning algorithms, i.e. random forest, gradient boosting, and extreme gradient boosting, to glioblastoma scRNA-seq data to discover the most important genes associated with cellular states. We then identified promoter and enhancer regions associated with these genes. After downloading the scATAC-seq peaks and their read counts for each patient, we identified the overlapping regions between the single-cell peaks and the peaks of genes obtained through machine-learning algorithms. Then we calculated read counts that were mapped to these overlapping regions. We finally developed a model capable of estimating the stemness score for each glioma cell using overlapping regions and the importance of genes predictive of glioblastoma cellular states. We also created an R package, accessible to all researchers regardless of their coding proficiency. Our results showed that mesenchymal-like stem cells display higher stemness scores compared to neural-progenitor-, oligodendrocyte-progenitor-, and astrocyte-like cells. scATAC-seq can be used to assess heterogeneity in glioblastoma and identify cells with high stemness characteristics. The package is publicly available at https://github.com/Necla/StemnesScoRe and includes documentation with implementation of a real-data experiment.

Comparison Between Diagnostic Accuracy of MR Cerebral Perfusion and Histopathology for Diagnosing Low and HighGrade Gliomas

Background: Gliomas represent the most prevalent type of primary brain tumor and are associated with a significant mortality rate. The utilization of MR cerebral perfusion proves effective in distinguishing between low-grade and high-grade gliomas, surpassing the capabilities of routine MRI. This distinction allows for the timely initiation of surgical intervention. We aimed to document the diagnostic accuracy of MR cerebral perfusion against the gold standard of lab-based histopathology. Methods: We conducted this cross-sectional validation study over a duration of six months in the outpatient radiology department of a major public sector hospital of Rawalpindi, Pakistan. All patients had MR cerebral perfusion Scan of brain in order to differentiate low grade and high grade glioma. Quantitative rCBV analysis was carried out adopting a threshold value of 3, above this value the lesion was considered at a high grade. The results were written on specially designed proforma. Results: A total 230 patients were included in the study with mean age of 50.68+16.36 and there were 135 (58.7%) male and 95 (41.3%) were female. There were 122 (53.0%) patients presented with low grade glioma and 108 (47.0%) patients presented with high grade glioma. The sensitivity, specificity, PPV, NPV and diagnostic accuracy of MR cerebral perfusion to differentiate between low grade and high grade gliomas was 85.71%, 70.97%, 88.89%, 64.71% and 81.74% respectively. Conclusion: This study concludes that the MR perfusion reliably identifies higher-grade features in a tumor that otherwise may not appear on standard MRI scan.

An automated detection and classification of brain tumor from MRIs using Water Chaotic Fruitfly Optimization (WChFO) based Deep Recurrent Neural Network (DRNN)

AbstractDue to the complexity of the images and dearth of anatomical models, it is highly difficult to accurately represent the various deformations in each component of the medical images. In recent years, a significant number of children and adults have affected from brain tumors, which is one of the most terrible types of disease affects the people around the world. Moreover, the Magnetic Resonance Imaging (MRI) based brain tumor detection is one of a significant study area in the field of medical imaging. Since, the use of computerized methods aids in the detection and treatment of disease by the medical professionals. The development of an automated method for the accurate detection and classification of tumors from brain MRIs. In this framework, a tanh normalization process is used to smooth out the input brain MRIs with less noise artefacts and improved quality. Then, a group feature extraction model is used to extract the relevant features from the normalized image, which includes both Speeded Up Robust Features (SURF) and Grey Level Co-occurrence Matrix (GLCM) features. The Water Chaotic Fruitfly Optimization (WChFO) method is used to identify the best features for increasing the speed of classifier training and testing processes with less time. Moreover, a Deep Recurrent Neural Network (DRNN) model is used to classify the type of brain tumor for accurate early diagnosis and treatment. The most well-known benchmarking datasets, like BRATS and Kaggle, employed for analysis in order to assess the effectiveness and results of the proposed brain tumor diagnosis system. By using the proposed WChFO-DRNN technique, the accuracy of the tumor detection system is increased to 99.2% with the sensitivity, specificity of 99% and time consumption of 0.2s.

Detection and classification of brain tumor using hybrid deep learning models

Accurately classifying brain tumor types is critical for timely diagnosis and potentially saving lives. Magnetic Resonance Imaging (MRI) is a widely used non-invasive method for obtaining high-contrast grayscale brain images, primarily for tumor diagnosis. The application of Convolutional Neural Networks (CNNs) in deep learning has revolutionized diagnostic systems, leading to significant advancements in medical imaging interpretation. In this study, we employ a transfer learning-based fine-tuning approach using EfficientNets to classify brain tumors into three categories: glioma, meningioma, and pituitary tumors. We utilize the publicly accessible CE-MRI Figshare dataset to fine-tune five pre-trained models from the EfficientNets family, ranging from EfficientNetB0 to EfficientNetB4. Our approach involves a two-step process to refine the pre-trained EfficientNet model. First, we initialize the model with weights from the ImageNet dataset. Then, we add additional layers, including top layers and a fully connected layer, to enable tumor classification. We conduct various tests to assess the robustness of our fine-tuned EfficientNets in comparison to other pre-trained models. Additionally, we analyze the impact of data augmentation on the model's test accuracy. To gain insights into the model's decision-making, we employ Grad-CAM visualization to examine the attention maps generated by the most optimal model, effectively highlighting tumor locations within brain images. Our results reveal that using EfficientNetB2 as the underlying framework yields significant performance improvements. Specifically, the overall test accuracy, precision, recall, and F1-score were found to be 99.06%, 98.73%, 99.13%, and 98.79%, respectively.

Metabolism and signaling crosstalk in glioblastoma progression and therapy resistance.

Glioblastoma is the most common form of primary malignant brain tumor in adults and one of the most lethal human cancers, with high recurrence and therapy resistance. Glioblastoma cells display extensive genetic and cellular heterogeneity, which precludes a unique and common therapeutic approach. The standard of care in glioblastoma patients includes surgery followed by radiotherapy plus concomitant temozolomide. As in many other cancers, cell signaling is deeply affected due to mutations or alterations in the so-called molecular drivers. Moreover, glioblastoma cells undergo metabolic adaptations to meet the new demands in terms of energy and building blocks, with an increasing amount of evidence connecting metabolic transformation and cell signaling deregulation in this type of aggressive brain tumor. In this review, we summarize some of the most common alterations both in cell signaling and metabolism in glioblastoma, presenting an integrative discussion about how they contribute to therapy resistance. Furthermore, this review aims at providing a comprehensive overview of the state-of-the-art of therapeutic approaches and clinical trials exploiting signaling and metabolism in glioblastoma.

From Genes to Therapy: Pituitary Adenomas in the Era of Precision Medicine.

This review presents a comprehensive analysis of pituitary adenomas, a type of brain tumor with diverse behaviors and complexities. We cover various treatment approaches, including surgery, radiotherapy, chemotherapy, and their integration with newer treatments. Key to the discussion is the role of biomarkers in oncology for risk assessment, diagnosis, prognosis, and the monitoring of pituitary adenomas. We highlight advances in genomic, epigenomic, and transcriptomic analyses and their contributions to understanding the pathogenesis and molecular pathology of these tumors. Special attention is given to the molecular mechanisms, including the impact of epigenetic factors like histone modifications, DNA methylation, and transcriptomic changes on different subtypes of pituitary adenomas. The importance of the tumor immune microenvironment in tumor behavior and treatment response is thoroughly analyzed. We highlight potential breakthroughs and innovations for a more effective management and treatment of pituitary adenomas, while shedding light on the ongoing need for research and development in this field to translate scientific knowledge into clinical advancements, aiming to improve patient outcomes.

Resilience of Patients With Brain Tumor While Awaiting Surgery.

BACKGROUND: Resilience is the ability of patients to adapt effectively when given a diagnosis of an illness. While awaiting brain tumor surgery, patients often experience uncertainty from brain tumor-related symptoms resulting in inducing depressive symptoms, having physical disability, and reducing quality of life. Resilience studies have been widely conducted in the postoperative phase with a limited knowledge on the preoperative phase. This study aimed to identify predictors of resilience while awaiting brain tumor surgery. METHODS: This cross-sectional predictive study includes 100 participants 18 years and older, with diagnosis of brain tumors, and waiting for brain tumor surgery at the outpatient department of 1 tertiary hospital in Bangkok between August 2022 and February 2023. Multiple linear regression was used to examine the predictors of resilience. RESULTS: Most of the sample (77%) were female with a mean age of 52.71 (13.17) years. The most common type of brain tumor was meningioma (38%). The median waiting time since brain tumor diagnosis until the date of preadmission for operation was 18 (3-1464) days. Symptom severity, social support, and treatment plan were able to explain 37.3% of the variance of resilience in patients awaiting brain tumor surgery ( F = 19.077, P < .01, R2 = 0.373, adjusted R2 = 0.354). CONCLUSION: Resilience is an important skill for patients with brain tumor to manage uncertainty events that occur in their lives. The preoperation phase needs to assess both physical and mental tumor-related symptoms, and include caregivers as part of the care, to promote resilience skill for patients awaiting brain tumor surgery.

FAM83D: Negative regulator for glioma cell proliferation and inadequate indicator for the prognosis of gliomas

Glioma represents the most frequently occurring type of primary brain tumor. The FAM83D gene was identified as a mitosis-related gene that plays a critical role in the development and progression of various types of cancer. However, its precise involvement in glioma remains largely unknown. This study aims to investigate the expression and clinicopathological significance of FAM83D and to analyze its potential prognostic value in brain glioma. First, we examined the influence of FAM83D expression on the overall survival (OS) of glioblastoma patients through dataset analysis. The results showed that FAM83D expression levels negatively correlated with OS in Brain Low Grade Glioma (BLGG) patients, but not in glioblastoma multiforme (GBM) patients. Subsequently, we analyzed the levels of FAM83D in the brains of developing mice after birth. We observed that FAM83D transcription was upregulated in mouse glioma cells compared to primary astrocytes, but not microglia cells. Finally, we found that FAM83D levels negatively regulated the growth rate of glioma cells in vitro. Our findings indicate that FAM83D may not promote glioma cell growth in vitro and is not an effective biomarker in glioblastoma diagnosis. Therefore, it is unlikely to be useful as a clinical tool for the detection or monitoring of glioma.

Significant role of PPP3CB in malignant gliomas development, prognosis and potential therapeutic application—a study based on comprehensive bioinformatics, cell experiments and immunohistochemistry analyses

IntroductionMalignant gliomas are the most prevalent and fatal types of primary malignant brain tumors with poor prognosis. Protein phosphatase 3 catalytic subunit beta (PPP3CB) is a pivotal constituent of the Ca2+/calmodulin-dependent serine/threonine protein phosphatases and widely expressed in brain. We aimed at identifying whether PPP3CB has potential in being a novel biomarker of malignant gliomas, bringing new insights to clinical management and therapy. MethodsTranscriptomes and clinical data of Glioblastoma (GBM) and low-grade glioma (LGG) samples were downloaded from TCGA and CGGA. We first explored the expressional and survival features of tumor tissues. Then, PPP3CB-associated genes were identified and their functional pathways were explored through GSEA analyses. Western blotting was conducted to modify PPP3CB expression. Samples of glioma patients and healthy controls were collected and immunohistochemistry (IHC) staining was performed to detect protein level. Further, we carried out an immune infiltration analysis, explored the correlation between PPP3CB and immune checkpoint genes, as well as to assessed the tumor mutation burden (TMB) and tumor microenvironment score (TMEscore) of PPP3CB. ResultsPPP3CB expression in malignant glioma tissues was significantly downregulated and was considered an independent prognostic factor. Several functional pathways were observed through functional pathway analyses. PPP3CB's expression was strongly related to the infiltration of various immune cells and expression of key immune checkpoint genes. PPP3CB expression in high-grade gliomas was significantly lower, affecting glioma cells' proliferation and apoptosis in vitro. ConclusionPPP3CB was a potential biomarker for the diagnosis and prognosis of malignant gliomas.

10035-GMC-1 PTPN11 VARIANT MAY BE A PROGNOSTIC INDICATOR OF IDH-WILDTYPE GLIOBLASTOMA IN A COMPREHENSIVE GENOMIC PROFILING COHORT

Abstract Glioblastoma (GBM) is the most common type of primary malignant brain tumor and has a poor prognosis. Identifying novel targets and stratification strategies is urgently needed to improve patient survival. The present study aimed to identify clinically relevant genomic alterations in IDH-wildtype GBM using data from comprehensive genomic profiling (CGP) assays performed nationwide in Japan. The CGP assay results of 392 IDH-wildtype GBM cases performed between October 2019 and February 2023 obtained from the Center for Cancer Genomics and Advanced Therapeutics (C-CAT) were retrospectively analyzed. The median patient age was 52.5 years, and 207 patients (53%) were male. In the 286 patients for whom survival information was available, a protein-tyrosine phosphatase non-receptor type 11 (PTPN11) variant detected in 20 patients (6.8%) was extracted as the gene associated with significantly shorter overall survival (OS) (p = 0.002). Multivariate analysis demonstrated that the PTPN11 variant and poor PS were independent prognostic indicators. In contrast, no prognostic impact was observed in the cohort in The Cancer Genome Atlas data. The discrepancy in the prognostic impact of the PTPN11 variant from these two pools might have resulted from differences in the biases affecting the survival of patients who underwent a CGP assay, including left-truncation and right-censored bias. However, survival simulation done to adjust for these biases showed that the prognostic impact of the PTPN11 variant was also significant, suggesting that the PTPN11 variant was a negative prognostic indicator of IDH-wildtype GBM in the patient cohort with the CGP assay.

MedFusionGAN: multimodal medical image fusion using an unsupervised deep generative adversarial network

PurposeThis study proposed an end-to-end unsupervised medical fusion generative adversarial network, MedFusionGAN, to fuse computed tomography (CT) and high-resolution isotropic 3D T1-Gd Magnetic resonance imaging (MRI) image sequences to generate an image with CT bone structure and MRI soft tissue contrast to improve target delineation and to reduce the radiotherapy planning time.MethodsWe used a publicly available multicenter medical dataset (GLIS-RT, 230 patients) from the Cancer Imaging Archive. To improve the models generalization, we consider different imaging protocols and patients with various brain tumor types, including metastases. The proposed MedFusionGAN consisted of one generator network and one discriminator network trained in an adversarial scenario. Content, style, and L1 losses were used for training the generator to preserve the texture and structure information of the MRI and CT images.ResultsThe MedFusionGAN successfully generates fused images with MRI soft-tissue and CT bone contrast. The results of the MedFusionGAN were quantitatively and qualitatively compared with seven traditional and eight deep learning (DL) state-of-the-art methods. Qualitatively, our method fused the source images with the highest spatial resolution without adding the image artifacts. We reported nine quantitative metrics to quantify the preservation of structural similarity, contrast, distortion level, and image edges in fused images. Our method outperformed both traditional and DL methods on six out of nine metrics. And it got the second performance rank for three and two quantitative metrics when compared with traditional and DL methods, respectively. To compare soft-tissue contrast, intensity profile along tumor and tumor contours of the fusion methods were evaluated. MedFusionGAN provides a more consistent, better intensity profile, and a better segmentation performance.ConclusionsThe proposed end-to-end unsupervised method successfully fused MRI and CT images. The fused image could improve targets and OARs delineation, which is an important aspect of radiotherapy treatment planning.

Evaluation of the effects of simulated hypoxia by CoCl2 on radioresistance and change of hypoxia-inducible factors in human glioblastoma U87 tumor cell line

PurposeGlioblastoma (GBM) is considered the most common and lethal type of brain tumor with a poor prognosis. GBM treatment has challenges due to its aggressive nature, which often causes treatment failure and recurrence. Hypoxia is one of the characteristics of glioblastoma tumors that contribute to radioresistance and malignant phenotypes of GBM. In this study, we aimed to determine the effects of hypoxia on the radiosensitivity of U87 GBM cells by the hypoxia-mimicking model. MethodsFollowing the treatment of cells with different concentrations of CoCl2, an MTT assay was used to evaluate the cytotoxicity of CoCl2. To understand the effects of Ionizing radiation on CoCl2-treated groups, cells were exposed to irradiation after pretreating with 100 μM CoCl2, and a clonogenic survival assay was performed to determine the radiosensitivity of U87 cells. Also, the intracellular Reactive oxygen level was measured by 2′,7′–dichlorofluorescein diacetate (DCFDA) probe staining. Additionally, the expression of hypoxia-associated genes, including HIF-1α, HIF-2α, and their target genes (GLUT-1), was monitored by reverse transcription polymerase chain reaction (RT-PCR). ResultsOur study revealed that the cell viability of CoCl2-treated cells was decreased in a concentration-dependent manner. Also, CoCl2 did not cause any cytotoxicity on U87 cells at a concentration of 100 μM after treatment for 24 h. Colony formation assay showed that CoCl2 pretreatment induced radioresistance of tumor cells compared to non-treated cells. Also, CoCl2 can protect cells against irradiation by the clearance of ROS. Moreover, Real-time results showed that the mRNA expression of HIF-1α and GLUT-1 were significantly upregulated following hypoxia induction and/or irradiation condition. However, the level of HIF-2α mRNA did not change significantly in hypoxia or irradiation alone conditions, but it increased significantly only in hypoxia + irradiation conditions. ConclusionTaken together, our results indicated that simulating hypoxia by CoCl2 can effectively increase hypoxia-associated genes, specially HIF-1α and GLUT-1, but did not affect HIF-2α gene expression. Also, it can increase the clearance of ROS, respectively, and it leads to inducing radioresistance of U87 cells.

Association between viral infections and glioma risk: a two-sample bidirectional Mendelian randomization analysis

BackgroundGlioma is one of the leading types of brain tumor, but few etiologic factors of primary glioma have been identified. Previous observational research has shown an association between viral infection and glioma risk. In this study, we used Mendelian randomization (MR) analysis to explore the direction and magnitude of the causal relationship between viral infection and glioma.MethodsWe conducted a two-sample bidirectional MR analysis using genome-wide association study (GWAS) data. Summary statistics data of glioma were collected from the largest meta-analysis GWAS, involving 12,488 cases and 18,169 controls. Single-nucleotide polymorphisms (SNPs) associated with exposures were used as instrumental variables to estimate the causal relationship between glioma and twelve types of viral infections from corresponding GWAS data. In addition, sensitivity analyses were performed.ResultsAfter correcting for multiple tests and sensitivity analysis, we detected that genetically predicted herpes zoster (caused by Varicella zoster virus (VZV) infection) significantly decreased risk of low-grade glioma (LGG) development (OR = 0.85, 95% CI: 0.76–0.96, P = 0.01, FDR = 0.04). No causal effects of the other eleven viral infections on glioma and reverse causality were detected.ConclusionsThis is one of the first and largest studies in this field. We show robust evidence supporting that genetically predicted herpes zoster caused by VZV infection reduces risk of LGG. The findings of our research advance understanding of the etiology of glioma.

Klasifikasi Tumor Otak Menggunakan Local Binary Pattern dan SVM Classifier

Brain tumors are abnormal cell growths in brain tissue that can be life-threatening. This study aims to classify brain tumors to help early diagnosis. The method used is to extract features from brain MRI images using Local Binary Pattern (LBP) and then classified with Support Vector Machine (SVM). The data used were 2044 brain MRI images consisting of 3 classes namely meningioma, no tumor, and pituitary. The best results were obtained using LBP with a radius of 1 and the number of neighbors 8, while the best SVM model used the RBF kernel with a C value of 50, resulting in 88% accuracy, 86% precision, and 87% recall. It can be concluded that the combination of LBP and SVM methods is effective enough to classify brain tumor types to support early diagnosis.

Hypoxia, Ion Channels and Glioblastoma Malignancy.

The malignancy of glioblastoma (GBM), the most aggressive type of human brain tumor, strongly correlates with the presence of hypoxic areas within the tumor mass. Oxygen levels have been shown to control several critical aspects of tumor aggressiveness, such as migration/invasion and cell death resistance, but the underlying mechanisms are still unclear. GBM cells express abundant K+ and Cl- channels, whose activity supports cell volume and membrane potential changes, critical for cell proliferation, migration and death. Volume-regulated anion channels (VRAC), which mediate the swelling-activated Cl- current, and the large-conductance Ca2+-activated K+ channels (BK) are both functionally upregulated in GBM cells, where they control different aspects underlying GBM malignancy/aggressiveness. The functional expression/activity of both VRAC and BK channels are under the control of the oxygen levels, and these regulations are involved in the hypoxia-induced GBM cell aggressiveness. The present review will provide a comprehensive overview of the literature supporting the role of these two channels in the hypoxia-mediated GBM malignancy, suggesting them as potential therapeutic targets in the treatment of GBM.