Brain Glioma Research Articles

Surgery in recurrent brain glioma, does it improve clinical outcome?

IntroductionGliomas account for 24% of all primary brain and CNS tumors. Histologically, gliomas were categorized into subtypes and grades (I through IV). Low-grade gliomas are grade I and grade II, while high-grade gliomas are grade III and grade IV. The gold standard for treatment of high-grade glioma is the most extensive safe surgical resection followed by radiotherapy combined with chemotherapy. However, recurrence of glioma is inevitable. Management of recurrent cases is still controversy.Aim of the workThe purpose of this research was to examine the efficacy of reoperation for cases of recurrent glioma in improving patients’ outcome.MethodsThis study included 25 patients with recurrent glioma admitted and operated upon in neurosurgical department in Assiut University hospitals through 1 year from 1/11/2020 to 30/10/2021.ResultsThere were 17 male (68%) and 8 female (32%), their age incidence ranged from 25 to 74 years with a mean age of 49.09, gross total resection of recurrent cases could be done in 14 (56%) patients, subtotal resection in 6 (24%) and partial in 5 (20%), all of recurrence were found at the same site almost the same of primary tumor except that two cases were radionecrosis and one patient revealed aggressive transformation from anaplastic astrocytoma to GBM by the end of the study period, and 17 (68%) patients alive and 8 (32%) died.ConclusionSurgical management of recurrent gliomas in selected patients is generally associated with improved functional performance and prolonged survival. Patients’ Karnofsky score at recurrence is an important prognostic factor for both low-grade glioma and high-grade glioma.

Clinical prognostic factors in patients with diffuse gliomas of the brain

BACKGROUND: Diffuse gliomas of the brain represent a heterogeneous group, which differs in age of the disease onset, histologic characteristics, tumor grade, molecular genetic markers and prognosis. AIM: of the present study is to identify the factors, which affect the duration of the relapse-free period and life expectancy in patients with diffuse brain gliomas, based on a comprehensive analysis of clinical syndromes and symptoms. MATERIALS AND METHODS: The study was carried out at the Departments of Neurosurgery and Nerve Diseases of the S.M. Kirov Military Medical Academy (Saint Petersburg, Russia). It included 390 in-patients with diffuse brain gliomas who underwent treatment over the periods of 2014–2022 and 2004–2006. Age at time of diagnosis was 45.26 ± 15.72 years old. The study included 218 (55.9%) male and 172 (44.1%) female patients. It was a single-centre prospective study. RESULTS: of the author’s research. Favourable demographic factors include the age range of 18–39 (relapse-free period duration p 0.01, life expectancy p 0.001) and female sex (relapse-free period p = 0.02, life expectancy p = 0.03). Prognostically favourable clinical manifestations include epileptic seizures before surgery (relapse-free period p 0.01, life expectancy p 0.02); unfavourable clinical manifestations are pyramidal syndrome (relapse-free period p 0.01, life expectancy p 0.001), sensitive disorders (relapse-free period p = 0.045–0.12, life expectancy p = 0.09–0.17), speech pathology (relapse-free period p 0.02, life expectancy p = 0.02–0.12) and cerebellar syndrome (relapse-free period p 0.02, life expectancy p 0.001). Clinical signs such as headache, nausea and vomiting, asthenia, diffuse neurological symptoms, craniocerebral nerve involvement and visual impairment do not affect (p 0.05) the outcomes of the patients with diffuse brain gliomas. CONCLUSION: Thus, favourable demographic prognostic factors include female sex and age range of 18–39, favourable clinical prognostic factors include epileptic seizures before surgery. Unfavourable clinical prognostic factors include presence of motor, sensory disorders, speech impairment and cerebellar syndrome.

Eco-friendly Synthesis and Molecular Modelling of 2-Phenylimidazo[1,2-b]pyridazine Derivatives: In Vitro and In Vivo Studies for Lead Optimization.

7-methyl-2-phenylimidazo[1,2-b]pyridazin-3-carboxylic acid (DM1) and 6-methoxy-2-phenylimidazo[1,2-b]pyridazin-3-carboxylic acid (DM2) have been shown to act as human (h) Cav3.1 voltage-gated calcium channel blockers with promising in vivo anti-absence activity, positioning them as potential antiepileptic drugs. The primary aim of this work was to develop cost-effective and environmentally friendly synthetic procedures for preparing 2-phenylimidazo[1,2-b]pyridazine derivatives. After optimizing the synthesis of this compound class using efficient and green techniques such as microwaves and ultrasound irradiation, we further evaluated the antiepileptic effects of DM1 and DM2 in two animal models: CD-1 ICR mice after pentylenetetrazol administration and DBA/2 mice with seizures induced by audiogenic stimuli. Their neuroprotective effect against oxidative stress were assessed using C6 rat brain glioma cells. DM1 and DM2 exhibited potent anti-seizure effects in both animal models and demonstrated significant in vitro neuroprotective activity by reducing reactive oxygen species release. To lay the groundwork for the future rational optimization of this promising class of compounds, the molecular bases of DM1 and DM2 activity were investigated by modelling their interaction with hCav3.1 channels. The calculated binding modes of DM1 and DM2 to hCav3.1 channels partially mirrored that of the selective Cav3.1 blocker Z944, paving the way for future lead optimization.

Grandpaternal Aging and the Risk of Primary Pediatric Brain Tumors in Grandchildren

Background and Aims: Previous research shows inconclusive evidence of a relationship between grandpaternal and parental aging and the risk of childhood brain tumors. This study aims to estimate the impact of advanced paternal and grandpaternal aging on the incidence of childhood brain tumors in Jordan. Materials and Methods: This case-control study included pediatric primary brain tumor patients and controls, matched by age and gender, ascertained from the Jordanian Cancer Registry (JCR). Collected data included patients’ diagnoses and birthdate, along with the ages of parents and paternal grandparents. Results: The study included 183 pediatric brain tumor patients and 127 controls, matched by age and gender (p>0.05). Advanced grandpaternal age, defined as age at fathers' birth greater than 40 years, was present in 31.7% and 17.3% of cases and controls, respectively. Advanced grandpaternal age was associated with a 1.956-fold higher risk of developing all brain tumors (p=.012 (OR=1.956)). In participants with a grandpaternal age older than 30, advanced paternal age had a 6.56-fold increased risk of developing brain tumors (p=0.000, (OR=6.56)), an 8.4-fold increased risk of developing gliomas (p=.000, (OR=8.40)), a 4.1-fold increased risk of developing medulloblastomas (p=.045, (OR=4.1)). Grandpaternal age and advanced grandpaternal age were independent predictors for the incidence of all brain tumors, gliomas and medulloblastomas. Conclusions : Advanced grandpaternal age or a combination of advanced grandpaternal and paternal age, when combined with other risk factors, may help prevent, screen, and aid in early detection of brain tumors in the pediatric population.

MiR-200 family as new potential prognostic factor of overall survival of patients with WHO G2 and WHO G3 brain gliomas

Gliomas are the predominant cause of cancer-related deaths among the young population. Even after incorporation of IDH1/2 mutations and 1p19q codeletion there are doubts regarding adjuvant treatment in WHO G2/G3 gliomas. miRNA molecules control about 30% of all genes, also many oncogenes, tumor suppressor genes and genes responsible for the response to ionizing radiation and systemic treatment. Patients with brain gliomas exhibit miRNA disorders. We aimed to evaluate the expression of miR-200 family members in relation to selected clinico-pathological factors and their prognostic value. We enrolled 53 patients diagnosed with WHO G2/G3 brain gliomas treated between 2012–2016. RT-qPCR based expression of miR-200 family was assessed in tumor and surrounding non-cancerous tissue. An analysis of selected clinico-pathological features was carried out. A logistic regression model was prepared for the miRNA signature. The predictive potential of the signature was assessed using the ROC curve. A stepwise backward regression model was used to select variables with a significant predictive potential related to OS. It was shown that miR-200a-3p, miR-200a-5p, miR-200c-5p, miR-141-3p and miR-429 can be independent predictors of survival. Better 2- and 5-year OS was associated with higher expression of miR-200a-3p, miR141-3p and lower expression of miR-200a-5p, miR-200c-5p, miR-429. The strongest predictors of survival were miR-200a-5p, miR-200b-3p, miR-200c-5p, miR-141-3p, miR-429, tumor volume and CTV. Members of the miR-200 family exhibit prognostic value for 2- and 5-year OS. Presented predictive models of survival may be clinically useful for treatment optimization.

18F-FET PET/CT can aid in diagnosing patients with indeterminate MRI findings for brain tumors: a prospective study.

This prospective study aimed to evaluate the diagnostic value of fluorine-18-labeled fluoroethyltyrosine (18F-FET) positron emission tomography (PET)/computed tomography (CT) in diagnosing brain tumors within an Asian patient population. Patients suspected of having primary or recurrent brain tumors were prospectively recruited. Each patient underwent 18F-FET and fluorine-18 fluorodeoxyglucose (18F-FDG) PET/CT on separate days within 1week. We calculated the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy to compare the diagnostic performance of the two PET scans. The standardized uptake value (SUV) and tumor-to-background ratio (TBR) of the lesions were determined using static images. Additionally, time-activity curves (TACs) and time-to-peak (TTP) were generated from the dynamic PET images. From September 2019 to December 2023, 33 subjects were enrolled for reasons including suspected brain tumors (n = 20) or suspicious glioma recurrence (n = 8) on magnetic resonance imaging (MRI) and restaging for glioma (n = 5). Among the patients with suspected brain tumors or glioma recurrence on MRI, 25% had false-positive results. 18F-FET PET/CT accurately identified 86% of these false positives. The sensitivity, specificity, PPV, NPV, and accuracy of visual interpretation of 18F-FET PET/CT were 96.2%, 85.7%, 96.2%, 85.7%, and 93.9%, respectively. The corresponding 18F-FDG PET/CT values were 73.1%, 71.4%, 90.5%, 41.7%, and 72.7%. 18F-FET PET/CT demonstrated significantly higher sensitivity and accuracy than 18F-FDG PET (p = 0.031 and p = 0.030, respectively). Using TBRmean as an adjunct reference index enhanced the diagnostic accuracy of 18F-FET PET/CT, achieving a sensitivity and NPV of 100%. Wash-out TAC or TTP < 20min was associated with a PPV of 100% for brain tumors. 18F-FET PET/CT appears to be a valuable tool for assessing brain tumors with indeterminate MRI findings in this Asian cohort. 18F-FET PET/CT offers benefits over 18F-FDG PET in differentiating brain tumors from nontumor brain lesions, particularly when using semiquantitative analysis with TBR. This study was registered on CinicalTrial.gov (NCT06563024).

RETRACTION: Efficacy of Three-Dimensional Arterial Spin Labeling and How it Compares Against That of Contrast Enhanced Magnetic Resonance Imaging in Preoperative Grading of Brain Gliomas.

K. Wang, H. Guo, X. Tian, Y. Miao, P. Han, and F. Jin, "Efficacy of Three-Dimensional Arterial Spin Labeling and How it Compares Against That of Contrast Enhanced Magnetic Resonance Imaging in Preoperative Grading of Brain Gliomas," Environmental Toxicology 38, no. 7 (2023): 1723-1731, https://doi.org/10.1002/tox.23800. The above article, published online on 11 April 2023, in Wiley Online Library (http://onlinelibrary.wiley.com/), has been retracted by agreement between the journal Editor-in-Chief, Paul B. Tchounwou; and Wiley Periodicals LLC. Following an investigation by the publisher, the parties have concluded that this article was accepted solely on the basis of a compromised peer review process. Therefore, the article must be retracted. The author Feng Jin disagrees with this decision. The other authors did not respond.

An eXplainable deep learning model for multi-modal MRI grading of IDH-mutant astrocytomas

IDH-Mutant-astrocytomas are malignant brain glioma tumors. They are graded as lower grade (grades 2 and 3) or higher grade (grade 4) according to their rate of growth and molecular features. Conventional IDH grading requires pathology examination, which is invasive, costly, and time-consuming. Advanced MRI modalities are widely used to overcome some of these limitations but require a high level of interpretation for tumor grading. Recent advances in deep learning have shown potential to aid and improve the accuracy and efficiency of tumor classification. This study proposes a novel non-invasive approach for grading IDH-Mutant-astrocytomas using a Light-weight Attention Network (LAN) that integrates multi-modal MRI data, including both conventional and advanced MRI modalities. The study addresses inter-modality heterogeneity using Principal Component Analysis (PCA) while minimizing computational complexity. LAN uses a 3D Convolutional Neural Network (CNN) and a volumetric attention mechanism to extract tumor patterns and classify grades 2 to 4. This is followed by an eXplainable AI approach that uses SHapley Additive exPlanations (SHAP) to interpret model decisions and identify key contributing features. The proposed LAN model outperforms other pre-trained state-of-the-art models, achieving an overall accuracy of 0.84. The SHAP attribution scores demonstrate that advanced MRI modalities such as Arterial Spin Labeling (ASL) and Diffusion-weighted Imaging (DWI), along with conventional MRI sequences such as FLAIR, T1-c and T2, contribute significantly to improving tumor heterogeneity visualization for aggressive grade 2 gliomas. This work demonstrates the feasibility of integrating explainable deep learning with multi-modal MRI data for precise and comprehensible early glioma grading, potentially leading to better clinical decision-making.

TMOD-23. LENTIVIRAL-INDUCED BRAIN GLIOMAS IN RAT MODEL

Abstract High-grade gliomas are the most common type of malignant intracranial tumor with dismal five-year prognostic outcomes despite developments in chemoradiotherapy and surgical intervention. Distinct oncogenes associated with tumorigenesis and progression have been identified, providing targets for potential therapeutic interventions and modeling efforts. In this study, we evaluated the efficacy of vectors targeting EGFR and PTEN, individually and in combination to produce high-grade brain gliomas. Four groups of rats underwent intracranial stereotaxic injection of lentiviral vector into the forceps minor of the corpus callosum. All animals were weighed and monitored for behavioral deficits three times per week for 90 days. Upon signs of clinical morbidity or at 90 days post-inoculation, tissues were harvested and analyzed using hematoxylin and eosin and immunohistochemical staining. Rats injected with the combination of the EGFR and the sh-PTEN vectors exhibited declines in weight, survival, and motor function compared to the control. The combination of the EGFR and sh-PTEN vectors was the only condition to induce tumorigenesis with histopathological confirmation of high-grade identity as confirmed by immunohistochemistry showing the most positivity for GFAP and SOX2 compared to the control. Significant increases in Ki-67 characterized combination tumors as highly proliferative. Increased oxidative stress and angiogenesis were supported by significant increases in 4HNE, SMA, and HIF-1α in the tumors compared to other treatment groups. Thus, the present model of high-grade rat brain glioma provides a potential model to assess pathogenic pathways and evaluate therapeutic strategies.

SURG-57. FLUORESCENCE LIFETIME IMAGING (FLIM) PROVIDES HIGHLY ACCURATE, DYE-FREE TUMOR MARGIN DETECTION DURING GLIOMA RESECTIONS

Abstract INTRODUCTION Fluorescence guided tumor margin detection is an important tool to enhance the extent of resection for gliomas. Existing dyes, such as 5-ALA, require planned administration, surgical resection in dark conditions, and are only effective for high-grade tumors. We have developed a novel laser scanning probe to assess tissue autofluorescence decay (lifetime) which allows tumor margin detection without exogenous dyes and under normal room light conditions. This technology has been optimized for margin detection in low-grade and high-grade gliomas. METHODS Patients undergoing craniotomy for resection of gliomas were enrolled. During resection of the final tumor margin, multiple 1x1 mm regions were scanned with FLIm and biopsied for evaluation by a neuropathologist. FLIm data and ground truth pathology were processed by a machine-learning algorithm to develop classifiers for the FLIm data. Classifiers were then applied to the full data set to determine the accuracy of FLIm predictions. RESULTS 12 patients with non-enhancing (LGG) and 25 patients with enhancing (HGG) tumors were enrolled, with a total of 228 biopsies obtained. For LGG, sensitivity of FLIm for detection of residual tumor was 0.89 (95% CI 0.8-0.98) with a specificity of 0.92 (95% CI 0.86-0.97), resulting in a positive predictive value of 0.76 and negative predictive value of 0.96 with an overall accuracy of 0.91. For HGG, sensitivity of FLIm for detection of residual tumor was 0.92 (95% CI 0.84-1.0) with a specificity of 0.95 (95% CI 0.91-0.99), resulting in a positive predictive value of 0.90 and negative predictive value of 0.96 with an overall accuracy of 0.94, exceeding reported values for 5-ALA. CONCLUSION FLIm can distinguish between residual low and high-grade glioma and normal brain at resection margins with very high sensitivity and specificity. This quantitative technology does not require administration of exogenous dyes and can be used under normal room light illumination.

Glioma subtype prediction based on radiomics of tumor and peritumoral edema under automatic segmentation

Comprehensive and non-invasive preoperative molecular diagnosis is important for prognostic and therapy decision-making in adult-type diffuse gliomas. We employed a deep learning method for automatic segmentation of brain gliomas directly from conventional magnetic resonance imaging (MRI) scans of the tumor core and peritumoral edema regions based on available glioma MRI data provided in the BraTS2021. Three-dimensional volumes of interest were segmented from 424 cases of glioma imaging data retrospectively obtained from two medical centers using the segmentation method and radiomic features were extracted. We developed a subtype prediction model based on extracted radiomic features and analyzed significance and correlations between glioma morphological characteristics and pathological features using data from patients with adult-type diffuse glioma. The automated segmentation achieved mean Dice scores of 0.884 and 0.889 for the tumor core and whole tumor, respectively. The area under the receiver operating characteristic curve for the prediction of adult-type diffuse gliomas subtypes was 0.945. “Glioblastoma, IDH-wildtype”, “Astrocytoma, IDH-mutant”, and “Oligodendroglioma, IDH-mutant, 1p/19q-coded” showed AUCs of 0.96, 0.914, and 0.961, respectively, for subtype prediction. Glioma morphological characteristics, molecular and pathological levels, and clinical data showed significant differences and correlations. An automatic segmentation model for gliomas based on 3D U-Nets was developed, and the prediction model for gliomas built using the parameters obtained from the automatic segmentation model showed high overall performance.

The Segment Anything foundation model achieves favorable brain tumor auto-segmentation accuracy in MRI to support radiotherapy treatment planning.

Promptable foundation auto-segmentation models like Segment Anything (SA, Meta AI, New York, USA) represent anovel class of universal deep learning auto-segmentation models that could be employed for interactive tumor auto-contouring in RT treatment planning. Segment Anything was evaluated in an interactive point-to-mask auto-segmentation task for glioma brain tumor auto-contouring in 16,744 transverse slices from 369 MRI datasets (BraTS 2020 dataset). Up to nine interactive point prompts were automatically placed per slice. Tumor boundaries were auto-segmented on contrast-enhanced T1w sequences. Out of the three auto-contours predicted by SA, accuracy was evaluated for the contour with the highest calculated IoU (Intersection over Union, "oracle mask," simulating interactive model use with selection of the best tumor contour) and for the tumor contour with the highest model confidence ("suggested mask"). Mean best IoU (mbIoU) using the best predicted tumor contour (oracle mask) in full MRI slices was 0.762 (IQR 0.713-0.917). The best 2D mask was achieved after amean of 6.6 interactive point prompts (IQR 5-9). Segmentation accuracy was significantly better for high- compared to low-grade glioma cases (mbIoU 0.789 vs. 0.668). Accuracy was worse using the suggested mask (0.572). Stacking best tumor segmentations from transverse MRI slices, mean 3D Dice score for tumor auto-contouring was 0.872, which was improved to 0.919 by combining axial, sagittal, and coronal contours. The Segment Anything foundation segmentation model can achieve high accuracy for glioma brain tumor segmentation in MRI datasets. The results suggest that foundation segmentation models could facilitate RT treatment planning when properly integrated in aclinical application.

Topological embedding and directional feature importance in ensemble classifiers for multi-class classification

Cancer is the second leading cause of disease-related death worldwide, and machine learning-based identification of novel biomarkers is crucial for improving early detection and treatment of various cancers. A key challenge in applying machine learning to high-dimensional data is deriving important features in an interpretable manner to provide meaningful insights into the underlying biological mechanismsWe developed a class-based directional feature importance (CLIFI) metric for decision tree methods and demonstrated its use for the The Cancer Genome Atlas proteomics data. The CLIFI metric was incorporated into four algorithms, Random Forest (RF), LAtent VAriable Stochastic Ensemble of Trees (LAVASET), and Gradient Boosted Decision Trees (GBDTs), and a new extension incorporating the LAVA step into GBDTs (LAVABOOST). Both LAVA methods incorporate topological information from protein interactions into the decision function.The different models' performance in classifying 28 cancers resulted in F1-scores of 92.6% (RF), 92.0% (LAVASET), 89.3% (LAVABOOST) and 85.7% (GBDT), with no method outperforming all others for individual cancer type prediction. The CLIFI metric enables visualisation of the model's decision-making functions. The resulting CLIFI value distributions indicated heterogeneity in the expression of several proteins (MYH11, ERα, BCL2) across different cancer types (including brain glioma, breast, kidney, thyroid and prostate cancer) aligning with the original raw expression data.In conclusion, we have developed an integrated, directional feature importance metric for multi-class decision tree-based classification models that facilitates interpretable feature importance assessment. The CLIFI metric can be combined with incorporating topological information into the decision functions of models to introduce inductive bias, enhancing interpretability.

Distinguishing clinical and imaging characteristics of primary central nervous system lymphoma from high-grade glioma and metastatic brain tumors.

The purpose of this retrospective analysis was to evaluate the clinical presentations, radiological characteristics, patient outcomes, and therapeutic approaches among individuals diagnosed with primary central nervous system lymphoma (PCNSL), high-grade glioma (HGG), and metastatic brain tumors (METS). We assembled a cohort of brain tumor patients from two medical centers, with two oncologists independently reviewing their clinical profiles. A retrospective examination of 87 PCNSL, 87 HGG, and 71 METS cases was performed to assess the aforementioned parameters. Notable variations were identified in the incidence of epileptic seizures and cognitive impairments between PCNSL and METS patients. Cerebral hemisphere involvement was predominantly observed in HGG and METS cases. PCNSL cases exhibited a higher likelihood of multiple lesions, whereas HGG showed a greater tendency for recurrence. The median survival times were established at 24.3months for PCNSL, 44.5months for HGG, and 27.1months for METS patients. In PCNSL cases, the number of lesions was identified as a significant predictor of mortality (P = 0.008). Our findings highlight the importance of clinical and imaging features in diagnosing PCNSL, which may present distinct features compared to HGG and METS.

Compression force promotes glioblastoma progression through the Piezo1‑GDF15‑CTLA4 axis.

Glioma, a type of brain tumor, is influenced by mechanical forces in its microenvironment that affect cancer progression. However, our understanding of the contribution of compression and its associated mechanisms remains limited. The objective of the present study was to create an environment in which human brain glioma H4 cells experience pressure and thereby investigate the compressive mechanosensors and signaling pathways. Subsequent time‑lapse imaging and wound healing assays confirmed that 12 h of compression significantly increased cell migration, thereby linking compression with enhanced cell motility. Compression upregulated the expression of Piezo1, a mechanosensitive ion channel, and growth differentiation factor 15 (GDF15), a TGF‑β superfamily member. Knockdown experiments targeting PIEZO1 or GDF15 using small interfering RNA resulted in reduced cell motility, with Piezo1 regulating GDF15 expression. Compression also upregulated CTLA4, a critical immune checkpoint protein. The findings of the present study therefore suggest that compression enhances glioma progression by stimulating Piezo1, promoting GDF15 expression and increasing CTLA4 expression. Thus, these findings provide important insights into the influence of mechanical compression on glioma progression and highlight the involvement of the Piezo1‑GDF15 signaling pathway. Understanding tumor responses to mechanical forces in the brain microenvironment may guide the development of targeted therapeutic strategies to mitigate tumor progression and improve patient outcomes.

Diagnostic and prognostic significance of tetraspanin 6 and its role in facilitating glioma progression

BackgroundSeveral tetraspanin (TSPAN) proteins have been implicated in tumorigenesis and disease progression. However, the precise function of tetraspanin 6 (TSPAN6) in glioma remains unclear.MethodsIntegrated raw data from the Tumor Genome Atlas (TCGA) and Gene Expression Profiling (GEO) databases were processed using R4.2.1. Bioinformatic methods were used to analyze the gene expression levels of TSPAN6 in both glioma and normal brain tissues, correlating them with clinical characteristics. Additionally, the predictive value of TSPAN6 in relation to the immune checkpoint blockade (ICB) therapy response was assessed. In vitro experiments were conducted to investigate the effects of TSPAN6 knockdown on glioma cell proliferation, migration, cell cycle regulation, and macrophage recruitment.ResultsTSPAN6 expression was significantly upregulated in glioma tissues compared to normal tissues. Elevated TSPAN6 expression is strongly correlated with unfavorable clinical characteristics in gliomas. Bioinformatic analyses revealed a significant correlation between elevated TSPAN6 expression and reduced overall survival. Additionally, TSPAN6 was co-expressed with several immune checkpoint genes and revealed a prognostic value in the context of ICB therapy. Functional enrichment analysis revealed the involvement of TSPAN6 in cell-cycle regulation. Furthermore, TSPAN6 expression positively correlated with macrophage and neutrophil infiltration. In vitro experiments confirmed that the downregulation of TSPAN6 inhibited U251 cell proliferation, disrupted the cell cycle, diminished migratory capabilities, and reduced the recruitment of macrophages.ConclusionOur findings emphasize its potential as both a diagnostic and therapeutic target as well as a predictor of immune therapy response in gliomas.

Intraoperative Ultrasound in Glioma Tumors

Introduction: Glioma tumor is the most common primary brain tumor and extent of resection of this tumor plays a significant role in the survival rate of the patients. Intraoperative ultrasound (IOUS) has become a convenient technique in brain tumor surgery due to its non-invasiveness, affordability, and real-time imaging capabilities, making it appealing to neurosurgeons, and assists neurosurgeons in identifying the tumor's location and adjacent structures during surgery. Methods: This study was done prospectively, in Rasul Akram and Shahada-ye-Haftam Tir hospitals, Tehran, Iran, and evaluated IOUS's use in 20 patients with brain glioma between 2019 and 2022. Simple random sampling was used to select patients. The resection was performed using IOUS, and the extent of resection was assessed through imaging. We used SonoSite convex ultrasound probe with bandwidth of 10-3 MHz and scan depth of 3 to 18 cm. Results: The study found that with ultrasound guidance, the average mass resection rate was 89.3%. The use of ultrasound during surgery improved the resection rate compared to previous studies. Motor complications observed in 15% of patients after surgery included paralysis and verbal deficits, while the rate of meningitis was low. The average length of hospitalization for patients was 10 days, and the average intraoperative bleeding was 344 cubic centimeters. Conclusion: Ultrasound can be a valuable tool in resection of brain glioma as it provides real-time imaging and assists in tumor resection, and detect adjacent structures. However, it should be used in conjunction with other monitoring modalities. Further studies are necessary to explore the full potential and limitations of IOUS in neurosurgery. J Bangladesh Coll Phys Surg 2024; 42: 387-392

Reduced T and NK Cell Activity in Glioblastoma Patients Correlates with TIM-3 and BAT3 Dysregulation.

Inhibitory receptors are critical for regulating immune cell function. In cancer, these receptors are often over-expressed on the cell surface of T and NK cells, leading to reduced anti-tumor activity. Here, through the analysis of 11 commonly studied checkpoint and inhibitory receptors, we discern that only HAVCR2 (TIM3) and ENTPD1 (CD39) display significantly greater gene expression in glioblastoma compared to normal brain and lower grade glioma. Cell surface TIM-3, but not ENTPD1, was also elevated on activated CD4+ and CD8+ T cells, as well as on NK cells from glioblastoma patients compared to healthy donor T and NK cells. A subsequent analysis of molecules known to co-ordinate TIM-3 function and regulation was performed, which revealed that BAT3 expression was significantly reduced in CD4+ and CD8+ T cells, as well as NK cells from glioblastoma patients compared to counterparts from healthy donors. These pro-inhibitory changes are also correlated with reduced levels of the activation marker CD69 and the pro-inflammatory cytokine IFNγ in CD4+ and CD8+ T cells, as well as NK cells from glioblastoma patients. Collectively, these data reveal that glioblastoma-mediated CD4+ and CD8+ T cell and NK cell suppression is due, at least in part, to dysregulated TIM-3 and BAT3 expression and the associated downstream immunoregulatory and dysfunctional effects.

Environmental Enrichment and Health Outcomes Among Low-Grade Glioma Brain Tumor Survivors.

BACKGROUND: Brain tumor survivors who received radiotherapy (RT) are at a disproportionately increased risk of accelerated aging and symptom burden. We aim to examine the association between environmental enrichment (EE) and health outcomes among low-grade glioma survivors who received brain RT. METHODS: The study used a cross-sectional cohort design, enrolling participants approximately 5 years from diagnosis. The construct of EE consisted of social network, physical activity, employment status, and financial stability. Berkman-Syme Social Network Index, International Physical Activity Questionnaire, Vocational Index Scale, and a Socioeconomic Questionnaire were used to measure the construct of EE. Health outcome measures included the Montreal Cognitive Assessment, Symbol Digit Modality Test, clinical brain magnetic resonance images (pre-RT, approximately 2-3 years after RT, and approximately 5 years after RT), Karnofsky Performance Status Scale, and the MD Anderson Symptom Inventory Brain Tumor Module. Ordinal logistic regression estimated the association between levels of EE and health outcomes. RESULTS: Thirty-nine participants completed the study and experienced varying levels of EE. The median age was 44 years old, ranging from 26 to 78 years old. Nineteen individuals were diagnosed with oligodendroglioma, and 18 were diagnosed with astrocytoma. Thirteen participants had low EE, 17 had moderate EE, and 9 had high EE. Although not statistically significant, we observed patterns of increasing health outcomes (Montreal Cognitive Assessment, Symbol Digit Modality Test, and Karnofsky Performance Status Scale) related to increasing levels of EE. CONCLUSION: This study is an initial exploration into the role of EE in health outcomes and survivorship programs for persons with glioma. Future research should assess EE before treatment or at the time of diagnosis and be longitudinal to accurately ascertain the association between EE and health outcomes. Comprehensive neuro-oncology survivorship programs structured to facilitate EE may reduce symptom burden, promote neuroplasticity, and improve cognitive and functional outcomes after brain radiation.

Multimodal Connectivity‐Guided Glioma Segmentation From Magnetic Resonance Images via Cascaded 3D Residual U‐Net

ABSTRACTGlioma is a type of brain tumor with a high mortality rate. Magnetic resonance imaging (MRI) is commonly used for examination, and the accurate segmentation of tumor regions from MR images is essential to computer‐aided diagnosis. However, due to the intrinsic heterogeneity of brain glioma, precise segmentation is very challenging, especially for tumor subregions. This article proposed a two‐stage cascaded method for brain tumor segmentation that considers the hierarchical structure of the target tumor subregions. The first stage aims to identify the whole tumor (WT) from the background area; and the second stage aims to achieve fine‐grained segmentation of the subregions, including enhanced tumor (ET) region and tumor core (TC) region. Both stages apply a deep neural network structure combining modified 3D U‐Net with a residual connection scheme to tumor region and subregion segmentation. Moreover, in the training phase, the 3D masks generation of subregions with potential incomplete connectivity are guided by the completely connected regions. Experiments were performed to evaluate the performance of the methods on both area and boundary accuracy. The average dice score of the WT, TC, and ET regions on BraTS 2020 dataset is 0.9168, 0.0.8992, 0.8489, and the Hausdorff distance is 6.021, 9.203, 12.171, respectively. The proposed method outperforms current works, especially in segmenting fine‐grained tumor subregions.