Glioblastoma Brain Research Articles

Prognostic Prediction Model for Glioblastoma: A Ferroptosis-Related Gene Prediction Model and Independent External Validation.

Glioblastoma (GBM) is the most common primary malignant intracranial tumor with a poor prognosis. Ferroptosis is a newly discovered, iron-dependent, regulated cell death, and recent studies suggest its close correlation to GBM. The transcriptome and clinical data were obtained for patients diagnosed with GBM from TCGA, GEO, and CGGA. Ferroptosis-related genes were identified, and a risk score model was constructed using Lasso regression analyses. Survival was evaluated by univariate or multivariate Cox regressions and Kaplan-Meier analyses, and further analyses were performed between the high- and low-risk groups. There were 45 ferroptosis-related different expressed genes between GBM and normal brain tissues. The prognostic risk score model was based on four favorable genes, CRYAB, ZEB1, ATP5MC3, and NCOA4, and four unfavorable genes, ALOX5, CHAC1, STEAP3, and MT1G. A significant difference in OS between high- and low-risk groups was observed in both the training cohort (p < 0.001) and the validation cohorts (p = 0.029 and 0.037). Enrichment analysis of pathways and immune cells and functioning was conducted between the two risk groups. A novel prognostic model for GBM patients was developed based on eight ferroptosis-related genes, suggesting a potential prediction effect of the risk score model in GBM.

Low-dose pleiotropic radiosensitive nanoformulations for three-pronged radiochemotherapy of hypoxic brain glioblastoma under BOLD/DWI monitoring

BackgroundHypoxia-mediated radioresistance is the main obstacle to the successful treatment of glioblastoma (GBM). Enhancing hypoxic radiosensitivity and alleviating tumor hypoxia are both effective means to improve therapeutic efficacy, and the combination of the two is highly desirable and meaningful.ResultsHerein, we construct a low-dose pleiotropic radiosensitive nanoformulation consisting of a high-Z atomic nanocrystal core and mesoporous silica shell, surface-modified with angiopep-2 (ANG) peptide and loaded with nitric oxide (NO) donor and hypoxia-activated prodrug (AQ4N). Benefiting from ANG-mediated transcytosis, this nanoformulation can efficiently cross the BBB and accumulate preferentially in the brain. Low-dose radiation triggers this nanoformulation to exert a three-pronged synergistic therapeutic effect through high-Z-atom-dependent dose deposition enhancement, NO-mediated hypoxia relief, and AQ4N-induced hypoxia-selective killing, thereby significantly inhibiting GBM in situ growth while prolonging survival and maintaining stable body weight in the glioma-bearing mice. Meanwhile, the proposed in vivo 9.4 T BOLD/DWI can realize real-time dynamic assessment of local oxygen supply and radiosensitivity to monitor the therapeutic response of GBM.ConclusionsThis work provides a promising alternative for hypoxia-specific GBM-targeted comprehensive therapy, noninvasive monitoring, and precise prognosis.Graphical

Integrated regulatory and metabolic networks of the tumor microenvironment for therapeutic target prioritization.

Translation of genomic discovery, such as single-cell sequencing data, to clinical decisions remains a longstanding bottleneck in the field. Meanwhile, computational systems biological models, such as cellular metabolism models and cell signaling pathways, have emerged as powerful approaches to provide efficient predictions in metabolites and gene expression levels, respectively. However, there has been limited research on the integration between these two models. This work develops a methodology for integrating computational models of probabilistic gene regulatory networks with a constraint-based metabolism model. By using probabilistic reasoning with Bayesian Networks, we aim to predict cell-specific changes under different interventions, which are embedded into the constraint-based models of metabolism. Applications to single-cell sequencing data of glioblastoma brain tumors generate predictions about the effects of pharmaceutical interventions on the regulatory network and downstream metabolisms in different cell types from the tumor microenvironment. The model presents possible insights into treatments that could potentially suppress anaerobic metabolism in malignant cells with minimal impact on other cell types' metabolism. The proposed integrated model can guide therapeutic target prioritization, the formulation of combination therapies, and future drug discovery. This model integration framework is also generalizable to other applications, such as different cell types, organisms, and diseases.

ResNet-SVM: Fusion based glioblastoma tumor segmentation and classification.

Computerized segmentation of brain tumor based on magnetic resonance imaging (MRI) data presents an important challenging act in computer vision. In image segmentation, numerous studies have explored the feasibility and advantages of employing deep neural network methods to automatically detect and segment brain tumors depicting on MRI. For training the deeper neural network, the procedure usually requires extensive computational power and it is also very time-consuming due to the complexity and the gradient diffusion difficulty. In order to address and help solve this challenge, we in this study present an automatic approach for Glioblastoma brain tumor segmentation based on deep Residual Learning Network (ResNet) to get over the gradient problem of deep Convolutional Neural Networks (CNNs). Using the extra layers added to a deep neural network, ResNet algorithm can effectively improve the accuracy and the performance, which is useful in solving complex problems with a much rapid training process. An additional method is then proposed to fully automatically classify different brain tumor categories (necrosis, edema, and enhancing regions). Results confirm that the proposed fusion method (ResNet-SVM) has an increased classification results of accuracy (AC = 89.36%), specificity (SP = 92.52%) and precision (PR = 90.12%) using 260 MRI data for the training and 112 data used for testing and validation of Glioblastoma tumor cases. Compared to the state-of-the art methods, the proposed scheme provides a higher performance by identifying Glioblastoma tumor type.

MGMT Gene rs1625649 Polymorphism in Iranian Patients with Brain Glioblastoma: A Case Control Study.

Glioblastoma (GBM) is the most common and aggressive primary malignant brain tumor with poor prognosis and high potential of dispersion to other brain tissues in adult. Effective and modern choices of treatment including chemotherapy with alkylating agents marginally extend survival of GBM. However, alkylating agents can lead to highly harmful mismatch during DNA replication causing apoptosis and cell death. Accordingly, O6-Methylguanine-DNA methyltransferase (MGMT) removes alkyl adducts, thereby causing resistance to alkylating drugs. Single-Nucleotide Polymorphisms (SNPs) in MGMT promoter region may play a role in the regulation of MGMT expression and prediction of glioma development risk. In order to evaluate the clinical significance of rs1625649 SNP in the MGMT promoter region of glioblastoma, genomic DNA from a series of 54 patients with GBM and 50 healthy individuals in Iranian population were collected for tetra ARMS PCR amplification. None of the "A" or "C" alleles were associated with tumor occurrence, the "AA" genotype was more frequent in healthy subjects, and the "AC" genotype was 4.6 times more common in patients with GBM. The longest survival time was observed in the "CC" genotype; however, this difference was not statistically significant. On the other hand, homozygous rs1625649 (AA genotype) was significantly associated with a better survival than the cases with heterozygous rs1625649 (CA genotype) or wild type rs1625649 (CC genotype), predicting better response to temozolomide-based chemotherapy.

Co-doped ZnO nanowires: Synthesis, photocatalytic performance, and cytotoxic activity against human brain glioblastoma cells

In this paper, we introduced a green method for the preparation of pure and 1, 3, 7, and 10 % cobalt doped zinc oxide (Co-doped ZnO) Nanowires through the application of Salvadora persica aqueous extract. The synthesis of nanorods was confirmed by the results of PXRD, UV–vis, FESEM, EDX, and Raman analyses. The existence of well doped cobalt within the structure of zinc oxide was affirmed by the results of PXRD, EDX, and UV–vis. The FESEM analysis displayed the length and diameter of pure ZnO Nanowires to be 500 ± 0.2 nm and 25 ± 5 nm, while an enlargement was observed in the length and diameter of doped Nanowires due to the doping of cobalt into ZnO. The photocatalytic activity of synthesized samples indicated the culmination of doped cobalt into ZnO in increasing the rate of photocatalyst performance. The optimum sample was 1 % Co-doped ZnO Nanowires that eliminated 89 % of MB with a concentration of 20 mg/Lit in pH = 7. The toxicity effect of synthesized Nanowires on brain glioblastoma cells (U87) was investigated by the means of WST-1 test. Accordingly, the doped ZnO Nanowires induced a more toxic impact on U87 cells when compared to the case of pure zinc oxide Nanowires. Thus, performing a doping process on the nanostructure of ZnO led to increasing its inhibitory effect against U87 cells and intensifying its photocatalytic activity on MB dye.

BI-907828, a novel potent MDM2 inhibitor, inhibits glioblastoma brain tumor stem cells in vitro and prolongs survival in orthotopic xenograft mouse models.

The tumor suppressor TP53 (p53) is frequently mutated, and its downstream effectors inactivated in many cancers, including glioblastoma (GBM). In tumors with wild-type status, p53 function is frequently attenuated by alternate mechanisms including amplification and overexpression of its key negative regulator, MDM2. We investigated the efficacy of the MDM2 inhibitor, BI-907828, in GBM patient-derived brain tumor stem cells (BTSCs) with different amplification statuses of MDM2, in vitro and in orthotopic xenograft models. In vitro growth inhibition and on-target efficacy of BI-907828 were assessed by cell viability, co-immunoprecipitation assays, and western blotting. In vivo efficacy of BI-907828 treatments was assessed with qPCR, immunohistochemistry, and in intracranial xenograft models. BI-907828 decreases viability and induces cell death at picomolar concentrations in both MDM2 amplified and normal copy number TP53 wild-type BTSC lines. Restoration of p53 activity, including robust p21 expression and apoptosis induction, was observed in TP53 wild-type but not in TP53 mutant BTSCs. shRNA-mediated knock-down of TP53 in wild-type BTSCs abrogated the effect of BI-907828, confirming the specificity of the inhibitor. Pharmacokinetic-pharmacodynamic studies in orthotopic tumor-bearing severe combined immunodeficiency (SCID) mice demonstrated that a single 50mg/kg p.o. dose of BI-907828 resulted in strong activation of p53 target genes p21 and MIC1. Long-term weekly or bi-weekly treatment with BI-907828 in orthotopic BTSC xenograft models was well-tolerated and improved survival both as a single-agent and in combination with temozolomide, with dose-dependent efficacy observed in the MDM2 amplified model. BI-907828 provides a promising new therapeutic option for patients with TP53 wild-type primary brain tumors.

Menadione Potentiates Auranofin-Induced Glioblastoma Cell Death

Glioblastoma (GBM) is the most aggressive primary brain tumor. Recently, agents increasing the level of oxidative stress have been proposed as anticancer drugs. However, their efficacy may be lowered by the cytoprotective activity of antioxidant enzymes, often upregulated in neoplastic cells. Here, we assessed the mRNA and protein expression of thioredoxin reductase 1 (TrxR1), a master regulator of cellular redox homeostasis, in GBM and non-tumor brain tissues. Next, we examined the influence of an inhibitor of TrxR1, auranofin (AF), alone or in combination with a prooxidant menadione (MEN), on growth of GBM cell lines, patient-derived GBM cells and normal human astrocytes. We detected considerable amount of TrxR1 in the majority of GBM tissues. Treatment with AF decreased viability of GBM cells and their potential to form colonies and neurospheres. Moreover, it increased the intracellular level of reactive oxygen species (ROS). Pre-treatment with ROS scavenger prevented the AF-induced cell death, pointing to the important role of ROS in the reduction of cell viability. The cytotoxic effect of AF was potentiated by treatment with MEN. In conclusion, our results identify TrxR1 as an attractive drug target and highlights AF as an off-patent drug candidate in GBM therapy.

Cerebrospinal fluid methylome-based liquid biopsies for accurate malignant brain neoplasm classification.

Resolving the differential diagnosis between brain metastases (BM), glioblastomas (GBM), and central nervous system lymphomas (CNSL) is an important dilemma for the clinical management of the main three intra-axial brain tumor types. Currently, treatment decisions require invasive diagnostic surgical biopsies that carry risks and morbidity. This study aimed to utilize methylomes from cerebrospinal fluid (CSF), a biofluid proximal to brain tumors, for reliable non-invasive classification that addresses limitations associated with low target abundance in existing approaches. Binomial GLMnet classifiers of tumor type were built, in fifty iterations of 80% discovery sets, using CSF methylomes obtained from 57 BM, GBM, CNSL, and non-neoplastic control patients. Publicly-available tissue methylation profiles (N = 197) on these entities and normal brain parenchyma were used for validation and model optimization. Models reliably distinguished between BM (area under receiver operating characteristic curve [AUROC] = 0.93, 95% confidence interval [CI]: 0.71-1.0), GBM (AUROC = 0.83, 95% CI: 0.63-1.0), and CNSL (AUROC = 0.91, 95% CI: 0.66-1.0) in independent 20% validation sets. For validation, CSF-based methylome signatures reliably distinguished between tumor types within external tissue samples and tumors from non-neoplastic controls in CSF and tissue. CSF methylome signals were observed to align closely with tissue signatures for each entity. An additional set of optimized CSF-based models, built using tumor-specific features present in tissue data, showed enhanced classification accuracy. CSF methylomes are reliable for liquid biopsy-based classification of the major three malignant brain tumor types. We discuss how liquid biopsies may impact brain cancer management in the future by avoiding surgical risks, classifying unbiopsiable tumors, and guiding surgical planning when resection is indicated.

Abstract B009: Sub-oxidant ascorbate sensitizes glioblastoma stem cells to DNA damaging agents by inducing DNA de-methylation and altering the chromatin landscape

Abstract Gliomas are the most common primary brain cancer and glioblastoma (GBM) represents the most aggressive and lethal subtype. The high GBM morbidity and mortality are due to profound genetic, epigenetic, and phenotypic heterogeneity within the tumor that contributes to an almost guaranteed development of therapeutic resistance and tumor recurrence. GBM stem-like cells (GSCs) are tumor cell sub-populations that disproportionately drive tumor propagation, therapy resistance, and tumor recurrence. Epigenetic deregulation is thought to play a fundamental role in the formation and expansion of GSCs and their contributions to oncogenesis. However, the epigenetic reprogramming events that contribute to GSC tumor propagation and therapy resistance remain elusive. DNA methylation (5mC) can be modified to generate hydroxymethylation (5hmC) or reversed through enzymatic reactions coordinated by the Ten-Eleven Translocation (TET) enzymes. Multiple studies show a decrease in both 5hmC levels and TET expression in gliomas and a correlation between loss of 5hmC and increasing glioma grade and poor prognosis. These observations suggest that re-establishing TET activity and 5hmC levels in tumor cells will have tumor suppressive effects. Ascorbate (vitamin C) has gained pre-clinical interest as an attractive antitumor agent for GBM due to its ability to regulate redox status, cross the blood-brain barrier, and enhance TET enzymatic activity; therefore, providing an innovative potential epigenetic therapeutic for GBM. We show that low-dose ascorbate (i.e. sub-pro-oxidant concentrations) enhances TET activity and elevates global 5hmC levels concurrent with decreases in 5mC in GSCs. These changes in DNA methylation correlate with a decrease in self-renewal capacity and increased tumor cell death induced by temozolomide (TMZ) and ionizing radiation (IR). We also present a novel mechanism whereby ascorbate increases the histone mark H3K36me3 by upregulating GSC expression of the histone methyltransferase NSD1 via demethylation of the gene promoter region. We propose that the enhanced susceptibility of ascorbate-treated tumor cells to DNA damage therapies results from increased H3K36me3-induced euchromatin states that increases genomic susceptibility to DNA damage. These findings support the need to better understand the epigenetic, transcriptomic, and chromatin architectural effects of ascorbate in cancer. These results also strongly indicate that ascorbate could serve as an adjunct to standard-of-care GBM treatment by restoring TET tumor-suppressor activity and re-sensitizing GSCs to conventional radio- and chemotherapeutics. Citation Format: Maureen Malloy, Sweta Sudhir, Harmon Khela, Maria Fagundo-Lugo, Bachchu Lal, Joseph Bressler, John Laterra, Hernando Lopez Bertoni. Sub-oxidant ascorbate sensitizes glioblastoma stem cells to DNA damaging agents by inducing DNA de-methylation and altering the chromatin landscape. [abstract]. In: Proceedings of the AACR Special Conference: Cancer Epigenomics; 2022 Oct 6-8; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2022;82(23 Suppl_2):Abstract nr B009.

An integrative non-invasive malignant brain tumors classification and Ki-67 labeling index prediction pipeline with radiomics approach

BackgroundThe histological sub-classes of brain tumors and the Ki-67 labeling index (LI) of tumor cells are major factors in the diagnosis, prognosis, and treatment management of patients. Many existing studies primarily focused on the classification of two classes of brain tumors and the Ki-67LI of gliomas. This study aimed to develop a preoperative non-invasive radiomics pipeline based on multiparametric-MRI to classify-three types of brain tumors, glioblastoma (GBM), metastasis (MET) and primary central nervous system lymphoma (PCNSL), and to predict their corresponding Ki-67LI. MethodsIn this retrospective study, 153 patients with malignant brain tumors were involved. The radiomics features were extracted from three types of MRI (T1-weighted imaging (T1WI), fluid-attenuated inversion recovery (FLAIR), and contrast-enhanced T1-weighted imaging (CE-T1WI)) with three masks (tumor core, edema, and whole tumor masks) and selected by a combination of Pearson correlation coefficient (CORR), LASSO, and Max-Relevance and Min-Redundancy (mRMR) filters. The performance of six classifiers was compared and the top three performing classifiers were used to construct the ensemble learning model (ELM). The proposed ELM was evaluated in the training dataset (108 patients) by 5-fold cross-validation and in the test dataset (45 patients) by hold-out. The accuracy (ACC), sensitivity (SEN), specificity (SPE), F1-Score, and the area under the receiver operating characteristic curve (AUC) indicators evaluated the performance of the models. ResultsThe best feature sets and ELM with the optimal performance were selected to construct the tri-categorized brain tumor aided diagnosis model (training dataset AUC: 0.96 (95% CI: 0.93, 0.99); test dataset AUC: 0.93) and Ki-67LI prediction model (training dataset AUC: 0.96 (95% CI: 0.94, 0.98); test dataset AUC: 0.91). The CE-T1WI was the best single modality for all classifiers. Meanwhile, the whole tumor was the most vital mask for the tumor classification and the tumor core was the most vital mask for the Ki-67LI prediction. ConclusionThe developed radiomics models led to the precise preoperative classification of GBM, MET, and PCNSL and the prediction of Ki-67LI, which could be utilized in clinical practice for the treatment planning for brain tumors.

NIMG-53. DELTA RELAXOMETRY WITH CONTRAST ENHANCED MAGNETIC RESONANCE FINGERPRINTING: INITIAL APPLICATION TO DIFFERENTIATE BRAIN METASTASES AND GLIOBLASTOMA

Abstract PURPOSE The utility of post-contrast magnetic resonance fingerprinting (MRF) for brain tumor assessment has not been assessed. The aim of this work is to report differences in MRF-derived delta-relaxometry metrics between brain metastases and glioblastoma, offering a new strategy for tumor differentiation. METHODS Post- and pre-contrast MRF (T1, T2 and proton density maps) were acquired from 29 patients (14 brain metastases (Met), 15 glioblastoma (GB)) along with conventional MRI (T1w, T1w-Gd, T2w, T2w-FLAIR, and ADC). Post-contrast MRF was skull-stripped and non-linearly co-registered with pre-contrast MRF. Delta relaxometry metrics (ΔR1/ΔR2 ratio, ΔR1, ΔR2, and normalized ΔR1/ΔR2) were calculated in the native image space (1.2x1.2x3.0 mm3). Tumor regions were segmented using DeepMedic into necrotic core (NC), enhancing tumor (ET), and peritumoral edema (ED). ROI-averaged means of delta relaxometry metrics were compared using paired t-test for ET and ED regions in Met vs GB. Voxel-wise ΔR1/ΔR2 ratios (log transformed) were compared using unpaired two-tailed t-test with Bonferroni correction to quantify distribution differences in delta relaxometry between Met and GB. RESULTS Across all voxels of all patients in each group, ΔR1/ΔR2 ratios between Met and GB were different (0.272±0.61 vs 0.247±0.66 in ED, p &amp;lt; .001; 0.278±0.69 vs 0.264±0.67 in ET, p &amp;lt; .001). On a per-patient basis, the median, 75th percentile, and 90th percentile of ΔR1/ΔR2 ratios were different between ET and ED regions (p &amp;lt; .001). Within-ROI (NC, ET, and ED) averaged mean ΔR1/R2 ratios were not significantly different between Met and GB. CONCLUSION Voxel-wise distribution of ΔR1/ΔR2 ratios was different between tumor types (Met and GB) in both ET and ED regions. ROI-averaged ΔR1/ΔR2 ratios were different between ET and ED regions, but not specific for tumor type. Delta relaxometry provides a unique tumor-specific contrast and shows potential for solid tumor as well as peritumoral edema differentiation between Met and GB.

Optimization of Tumor Targeting Gold Nanoparticles for Glioblastoma Applications.

Glioblastoma brain tumors represent an aggressive form of gliomas that is hallmarked by being extremely invasive and aggressive due to intra and inter-tumoral heterogeneity. This complex tumor microenvironment makes even the newer advancements in glioblastoma treatment less effective long term. In developing newer treatment technologies against glioblastoma, one should tailor the treatment to the tumor microenvironment, thus allowing for a more robust and sustained anti-glioblastoma effect. Here, we present a novel gold nanoparticle therapy explicitly designed for bioactivity against glioblastoma representing U87MG cell lines. We employ standard conjugation techniques to create oligonucleotide-coated gold nanoparticles exhibiting strong anti-glioblastoma behavior and optimize their design to maximize bioactivity against glioblastoma. Resulting nanotherapies are therapy specific and show upwards of 75% inhibition in metabolic and proliferative activity with stark effects on cellular morphology. Ultimately, these gold nanotherapies are a good base for designing more multi-targeted approaches to fighting against glioblastoma.

Development of Plant Protein Derived Tri Angular Shaped Nano Zinc Oxide Particles with Inherent Antibacterial and Neurotoxicity Properties.

The synthesis of nanometer-sized metallic nanoparticles utilizing bio-sources is one of the most cost-effective and ecologically friendly approaches. Nano-zinc oxide particles (N-ZnO Ps) were made using a simple green synthesis method using an aqueous zinc nitrate salt and Perilla frutescens crude protein as a protecting and reducing agent in the current work. UV-visible (UV-vis) spectrophotometry, X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), Fourier transform infrared spectroscopy (FT-IR), scanning electron microscopy (SEM), (energy dispersive x-ray spectroscopy) EDX and high-resolution transmission electron microscopy (HR-TEM) were used to characterize the synthesized N-ZnO Ps. A distinctive UV-vis absorption peak was observed at 370 nm due to N-ZnO Ps. The SEM and HR-TEM pictures revealed N-ZnO Ps with a triangular form. The XRD pattern indicated the wurtzite structure of N-ZnO Ps. Nanoparticles exhibited a zeta potential of −11.3 mV. The antibacterial activity of N-ZnO Ps was tested against Escherichia coli (E. coli) and Klebsiella pneumoniae (K. pneumonia) microorganisms. The N-ZnO Ps were non-toxic to HMC-3 human normal brain microglia cells; however, they exhibited a potential cytotoxic effect on the LN-18 human brain glioblastoma cell line. These results indicate that N-ZnOPs can act as promising antibacterial and anticancer treatments in the prevention of Glioblastoma.

Differentiation of glioblastoma tissues using spontaneous Raman scattering with dimensionality reduction and data classification.

The neurosurgery of intracranial tumors is often complicated by the difficulty of distinguishing tumor center, infiltration area, and normal tissue. The current standard for intraoperative navigation is fluorescent diagnostics with a fluorescent agent. This approach can be further enhanced by measuring the Raman spectrum of the tissue, which would provide additional information on its composition even in the absence of fluorescence. However, for the Raman spectra to be immediately helpful for a neurosurgeon, they must be additionally processed. In this work, we analyzed the Raman spectra of human brain glioblastoma multiforme tissue samples obtained during the surgery and investigated several approaches to dimensionality reduction and data classificatin to distinguish different types of tissues. In our study two approaches to Raman spectra dimensionality reduction were approbated and as a result we formulated new technique combining both of them: feature filtering based on the selection of those shifts which correspond to the biochemical components providing the statistically significant differences between groups of examined tissues (center of glioblastoma multiforme, tissues from infiltration area and normally appeared white matter) and principal component analysis. We applied the support vector machine to classify tissues after dimensionality reduction of registered Raman spectra. The accuracy of the classification of malignant tissues (tumor edge and center) and normal ones using the principal component analysis alone was 83% with sensitivity of 96% and specificity of 44%. With a combined technique of dimensionality reduction we obtained 83% accuracy with 77% sensitivity and 92% specificity of tumor tissues classification.

Central Nervous System Diseases That Are Difficult to Distinguish from Infection

It is often difficult to distinguish infectious disease of the central nervous system from a wide variety of non-infectious diseases, as neurosurgeons have few opportunities to treat them. Differentiation of infectious diseases from neoplastic diseases is often challenging. Since it often takes time to eliminate infectious diseases, it is necessary to utilize all the obtained medical information to make a proper diagnosis to avoid missing the appropriate chance of surgical treatment. In this paper, we describe tips for and pitfalls of accurately distinguishing such diseases, including brain abscess versus glioblastoma, meningitis versus dural lesions, and infection versus lymphoproliferative disorders in immunocompromised patients. In these cases, it is difficult to make a decision based only on the examination and imaging findings on admission, and it is important to make a diagnosis based on medical history and patient background.

Tumor spheroids accelerate persistently invading cancer cells

Glioblastoma brain tumors form in the brain’s white matter and remain one of the most lethal cancers despite intensive therapy and surgery. The complex morphology of these tumors includes infiltrative growth and gain of cell motility. Therefore, various brain-mimetic model systems have been developed to investigate invasion dynamics. Despite this, exactly how gradients of cell density, chemical signals and metabolites influence individual cells’ migratory behavior remains elusive. Here we show that the gradient field induced by the spheroid—accelerates cells’ invasion of the extracellular matrix. We show that cells are pushed away from the spheroid along a radial gradient, as predicted by a biased persistent random walk. Thus, our results grasp in a simple model the complex behavior of metastasizing cells. We anticipate that this well-defined and quantitative assay could be instrumental in the development of new anti-cancer strategies.

Target-based virtual screening and molecular interaction studies for lead identification of natural olive compounds against glioblastoma multiforme.

Glioblastoma multiforme, a rare traumatic brain disorder, is at the research climax for its uncontrolled growth leading to a catastrophic outcome. Throwing light on the target-based virtual screening of drugs using natural phytocompounds is a striking cornerstone in glioblastoma-based drug discovery, accelerating with leaps and bounds. This project aims to develop promising lead compounds against glioblastoma brain cancer using OliveNet™, an open-source database. In this pursuit, our rationale for selecting molecules was based on their capability to pass through the blood-brain barrier. Out of 51 derivative molecules from flavonoids and polyphenols, 17 molecules were screened out bearing the best ADMET (absorption, distribution, metabolism, excretion, and toxicity) properties, alongside fulfilling our rationale of lead selection. Two polyphenols, 3,4,5-trimethoxybenzoic acid and 4-ethyl guaiacol, have binding affinity for the antioxidant flavonoid luteolin of -5.1 and -4.3 kcal/mol, respectively. According to docking studies, the residues ASN1960, ASN1966, ASN1960, PHE1984, TYR1896, VAL1911, and LYS1966 make both polar and nonpolar interactions with 3,4,5-trimethoxybenzoic acid and 4-ethylguanidine, respectively. LD50 values of toxicity screening using TOX Pro brought to limelight the excellent safety profile of polyphenols and flavonoids. Furthermore, studies using in silico cytotoxicity prediction and molecular modelling have decisively shown that these polyphenols are likely to be effective brain cancer inhibitors and promising future lead candidates against glioblastoma multiforme.

Late Effects of Radiation in Children Treated for Cerebral Glioblastoma: A Case Report of Glioblastoma Patient who’s Survival Exceeds 10 Years

Glioblastoma is one of the most aggressive cancers. The prognosis is generally poor despite surgery and adjuvant treatment. Improved management has resulted in a significant increase in overall survival. However, oncological treatments can lead to long-term complications. We report the case of a 13 year old girl with a case of glioblastoma in the brain surviving more than 10 years, during regular follow up we found a delay in school with concentration and memory problems which pushed us to make a neuropsychological evaluation that showed Intelligence below average with IQ between. The aim of this study is to share a case of glioblastoma brain surviving more than 10 years and to evaluate neurocognitive sequelae in children treated for brain tumor and management modalities through our case and literature reviews.

Screening seven hub genes associated with prognosis and immune infiltration in glioblastoma.

Glioblastoma (GBM) is the most common and deadly primary brain tumor in adults. Diagnostic and therapeutic challenges have been raised because of poor prognosis. Gene expression profiles of GBM and normal brain tissue samples from GSE68848, GSE16011, GSE7696, and The Cancer Genome Atlas (TCGA) were downloaded. We identified differentially expressed genes (DEGs) by differential expression analysis and obtained 3,800 intersected DEGs from all datasets. Enrichment analysis revealed that the intersected DEGs were involved in the MAPK and cAMP signaling pathways. We identified seven different modules and 2,856 module genes based on the co-expression analysis. Module genes were used to perform Cox and Kaplan-Meier analysis in TCGA to obtain 91 prognosis-related genes. Subsequently, we constructed a random survival forest model and a multivariate Cox model to identify seven hub genes (KDELR2, DLEU1, PTPRN, SRBD1, CRNDE, HPCAL1, and POLR1E). The seven hub genes were subjected to the risk score and survival analyses. Among these, CRNDE may be a key gene in GBM. A network of prognosis-related genes and the top three differentially expressed microRNAs with the largest fold-change was constructed. Moreover, we found a high infiltration of plasmacytoid dendritic cells and T helper 17 cells in GBM. In conclusion, the seven hub genes were speculated to be potential prognostic biomarkers for guiding immunotherapy and may have significant implications for the diagnosis and treatment of GBM.