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The clinical relevance of liquid biopsy for glioblastoma (GBM) remains undetermined due to practical and biological limitations such as absence of a reliable GBM‐specific biomarker, trace levels in circulation due to the blood–brain–barrier, and lack of a sensitive method to detect the trace levels of biomarkers. It is hypothesized that GBM stem cell (GSC)‐associated cell free DNA can function as reliable biomarker for GBM because it accounts for tumor heterogeneity and provide accurate molecular information about the cancer. An integrative methodology is used for GBM diagnosis by utilizing the sub‐single molecular sensitivity of nanoengineered plasmonic metasensors for real‐time genomic profiling of GSC DNA. The nanoengineered metasensors can detect the rare circulating GSC‐DNA accurately from just 5 µL of blood and the test can be performed in under 10 min. Analysis of clinical serum samples from GBM patients and healthy volunteers demonstrates that the technology yielded an accurate classification of GBM in an independent validation cohort (accuracy 98.3%, specificity 100%). The methodology detects GBM‐signatures from the patient blood rapidly within the half‐life period of cfDNA in circulation, non‐invasively and amplification‐free with a high diagnostic accuracy. With clinical validation, this methodology can evolve as a clinically viable diagnostic tool for fatal and hard‐to‐detect cancer like GBM. [ABSTRACT FROM AUTHOR]

Background/aim: Biomarkers for patients suffering from glioblastoma (GBM) are scarce. Extracellular vesicles (EV) are a promising candidate for a potential biomarker. Therefore, EV concentration could be a potential biomarker of tumor burden, volume, and prognosis., Patients and Methods: Large EV (lEV) and small EV (sEV) were isolated from 36 GBM patients' blood plasma by differential centrifugation. Nanoparticle tracking was used to measure EV concentration. Quantitative analysis of tumor volume was performed by evaluating T2/FLAIR relaxation times., Results: The mean size of lEV was 173.3 nm ± 18.2 nm, while sEV measured 148.3 ± 9.0 nm. Patients with higher lEV concentrations showed a trend towards longer overall survival (36.1 vs. 16.5 months, p=0.08). Regarding inflammatory markers, higher leukocyte count was positively correlated with higher sEV concentration (r 2 =0.3887, DF 21, p=0.0015). No significant relationship was found between lEV or sEV concentration and tumor volume., Conclusion: Overall EV concentration in the peripheral blood is not a predictor of tumor volume. sEV concentration is associated with a potential pro-inflammatory metabolism., (Copyright © 2024, International Institute of Anticancer Research (Dr. George J. Delinasios), All rights reserved.)

Epithelioid glioblastoma (eGB) is a rare GB subtype exhibiting characteristic morphology and genetic alterations. The efficacy of BRAF and MEK-1/2 inhibitors is demonstrated in eGB treatment, and therefore, considering eGB is important to enhance patient care and prognosis., (© 2024 The Author(s). Cytopathology published by John Wiley & Sons Ltd.)

Glioblastomas (GBMs) are the most aggressive types of central nervous system tumors. Although certain genomic alterations have been identified as prognostic biomarkers of GBMs, the histomorphological features that predict their prognosis remain elusive. In this study, following an integrative diagnosis of 227 GBMs based on the 2021 World Health Organization classification system, the cases were histologically fractionated by cellular variations and abundance to evaluate the relationship between cellular heterogeneity and prognosis in combination with O-6-methylguanine-DNA methyltransferase gene promoter methylation (mMGMTp) status. GBMs comprised four major cell types: astrocytic, pleomorphic, gemistocytic, and rhabdoid cells. t-distributed stochastic neighbor embedding analysis using the histological abundance of heterogeneous cell types identified two distinct groups with significantly different prognoses. In individual cell component analysis, the abundance of gemistocytes showed a significantly favorable prognosis but confounding to mMGMTp status. Conversely, the abundance of epithelioid cells was correlated with the unfavorable prognosis. Linear model analysis showed the favorable prognostic utility of quantifying gemistocytic and epithelioid cells, independent of mMGMTp. The evaluation of GBM cell histomorphological heterogeneity is more effective for prognosis prediction in combination with mMGMTp analysis, indicating that histomorphological analysis is a practical and useful prognostication tool in an integrative diagnosis of GBMs., (© 2024. The Author(s).)

Glioblastoma (GBM) is the most prevalent primary malignant tumor of the nervous system. In this study, we utilized pathomics analysis to explore the expression of CD40LG and its predictive value for the prognosis of GBM patients. We analyzed the expression differences of CD40LG in GBM tissue and normal brain tissue, along with performing survival prognosis analysis. Additionally, histopathological sections of GBM were used to screen for pathological features. Subsequently, SVM and LR pathomics models were constructed, and the models' performance was evaluated. The pathomics model was employed to predict CD40LG expression and patient prognosis. Furthermore, we investigated the potential molecular mechanisms through enrichment analysis, WGCNA analysis, immune correlation analysis, and immune checkpoint analysis. The expression level of CD40LG was significantly increased in GBM. Multivariate analysis demonstrated that high expression of CD40LG is a risk factor for overall survival (OS) in GBM patients. Five pathological features were identified, and SVM and LR pathomics models were constructed. Model evaluation showed promising predictive effects, with an AUC value of 0.779 for the SVM model, and the Hosmer-Lemeshow test confirmed the model's prediction probability consistency (P > 0.05). The LR model achieved an AUC value of 0.785, and the Hosmer-Lemeshow test indicated good agreement between the LR model's predicted probabilities and the true value (P > 0.05). Immune infiltration analysis revealed a significant correlation between the pathomics score (PS) and the degree of infiltration of activated DC cells, T cells CD4 naïve, Macrophages M2, Macrophages M1, and T cells CD4 memory resting. Our results demonstrate that the pathomics model exhibits predictability for CD40LG expression and GBM patient survival. These findings can be utilized to assist neurosurgeons in selecting optimal treatment strategies in clinical practice., (© 2024. The Author(s).)

Purpose: Current therapy strategies still provide only limited success in the treatment of glioblastoma, the most frequent primary brain tumor in adults. In addition to the characterization of the tumor microenvironment, global changes in the brain of patients with glioblastoma have been described. However, the impact and molecular signature of neuroinflammation distant of the primary tumor site have not yet been thoroughly elucidated., Experimental Design: We performed translocator protein (TSPO)-PET in patients with newly diagnosed glioblastoma (n = 41), astrocytoma WHO grade 2 (n = 7), and healthy controls (n = 20) and compared TSPO-PET signals of the non-lesion (i.e., contralateral) hemisphere. Back-translation into syngeneic SB28 glioblastoma mice was used to characterize Pet alterations on a cellular level. Ultimately, multiplex gene expression analyses served to profile immune cells in remote brain., Results: Our study revealed elevated TSPO-PET signals in contralateral hemispheres of patients with newly diagnosed glioblastoma compared to healthy controls. Contralateral TSPO was associated with persisting epileptic seizures and shorter overall survival independent of the tumor phenotype. Back-translation into syngeneic glioblastoma mice pinpointed myeloid cells as the predominant source of contralateral TSPO-PET signal increases and identified a complex immune signature characterized by myeloid cell activation and immunosuppression in distant brain regions., Conclusions: Neuroinflammation within the contralateral hemisphere can be detected with TSPO-PET imaging and associates with poor outcome in patients with newly diagnosed glioblastoma. The molecular signature of remote neuroinflammation promotes the evaluation of immunomodulatory strategies in patients with detrimental whole brain inflammation as reflected by high TSPO expression., (©2024 The Authors; Published by the American Association for Cancer Research.)

Introduction: Primary spinal glioblastoma (GBM) are very rare tumors of the spinal cord, with dismal prognosis and their exact management is controversial. We attempt to formulate treatment guidelines for these extremely rare tumors based on our institutional experience and a comprehensive review of the literature., Materials and Methods: In this retrospective study from 2008 to 2020, all the patients diagnosed with primary spinal GBM who underwent surgery at our institution were included. Clinical data were retrieved from case files, outpatient records and telephonic follow-up. Data on postoperative chemoradiation was noted in all the patients. The final diagnosis of spinal GBM was confirmed as per the histopathology reports. Patients who could not be followed up and those with prior history of cranial GBM were excluded from the study., Results: Nine patients were followed up and a median survival of 11 months was noted. Chemotherapy with TMZ and radiotherapy to the whole craniospinal axis significantly improved survival in these patients. The extent of surgical resection was not shown to be significant. Intracranial metastasis was the leading cause of mortality in such patients. Three patients developed documented intracranial metastasis during the course of the disease., Conclusions: Low threshold must be kept in mind in diagnosing patients with high-grade spinal cord intramedullary tumors in view of the rapidly progressing nature of the disease. In case of positive histopathological diagnosis of spinal GBM, the whole craniospinal axis should be imaged and any cranial metastasis which was originally missed during initial workup could be given appropriate radiotherapy.

In context of cancer diagnosis-based mass spectrometry (MS), the classification model created is crucial. Moreover, exploration of immune cell infiltration in tissues can offer insights within the tumor microenvironment. Here, we present a protocol to analyze 1D and 2D MS data from glioblastoma tissues for cancer diagnosis and immune cells identification. We describe steps for training the most optimal model and cross-validating it, for discovering robust biomarkers and obtaining their corresponding boxplots as well as creating an immunoscore based on MS-imaging data. For complete details on the use and execution of this protocol, please refer to Zirem et al. 1 ., Competing Interests: Declaration of interests M.S. and I.F. are inventors on a patent (priority number WO2015IB57301 20150922) related to the mass spectrometry technology used to develop this AI pipeline., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Background: Glioblastoma (GBM) is a clinically challenging primary brain tumor with poor survival outcome despite surgical resection and intensive chemoradiation. The metabolic heterogeneity of GBM can become biomarkers for treatment response, resistance, and outcome prediction. The aim of the study is to investigate metabolic distinctions between primary and recurrent GBM tissue and patient plasma to establish feasibility for metabolic profiling., Methods: A single-center cohort study analyzed tissue and blood samples from 15 patients with GBM using untargeted metabolomic/lipidomic assays. Metabolomic, lipidomic, and biogenic amine analyses were conducted on GBM tissue and patient plasma at diagnosis and recurrence using untargeted mass spectrometry. The study utilized a small but longitudinally collected cohort to evaluate alteration in metabolites, lipids, and biogenic amines between specimens at diagnosis and recurrence., Results: Exploratory analysis revealed significant alteration in metabolites, lipids, and biogenic amines between diagnostic and recurrent states in both tumor and plasma specimens. Notable metabolites differed at recurrence, including N-alpha-methylhistamine, glycerol-3-phosphate, phosphocholine, and succinic acid in tissue, and indole-3-acetate, and urea in plasma. Principal component analysis revealed distinct metabolomic profiles between tumor tissue and patient plasma. Distinct metabolic profiles were observed in GBM tissue and patient plasma at recurrence, demonstrating the feasibility of using metabolomic methodologies for longitudinal studies. One patient exhibited a unique tumor resistance signature at diagnosis, possibly indicating a high-risk metabolomic phenotype., Conclusions: In this small cohort, the findings suggest the potential of metabolomic signatures of GBM tissue and patient plasma for risk stratification, outcome prediction, and the development of novel adjuvant metabolic-targeting therapies. The findings suggest metabolic discrepancies at diagnosis and recurrence in tissue and plasma, highlighting potential implications for evaluation of clinical response. The identification of significant changes in metabolite abundance emphasizes the need for larger studies using targeted metabolomics to validate and further explore these profiles., Competing Interests: No competing interests were disclosed., (Copyright: © 2024 Liu YA et al.)

Glioblastoma, the most common malignant brain tumor in adults, is associated with a median overall survival duration of less than 2 years. Extraneural metastases occur in less than 1% of all patients with glioblastoma. The mechanism of extraneural metastasis is unclear. We present a case of extensive extraneural, extraosseous, epidural, and soft-tissue metastasis of glioblastoma. The diagnosis of metastatic glioblastoma was made only after next-generation sequencing (NGS) of the metastatic paraspinal lesions was completed. The CDK4, pTERT, PTEN, and TP53 molecular alterations seen in the initial intracranial glioblastoma were found in the paraspinal tumor, along with the addition of MYC, which is implicated in angiogenesis and epidermal-to-mesenchymal transition. Immunohistochemical stains showed that neoplastic cells were negative for GFAP. In conclusion, this case raises awareness about the role of NGS in the diagnosis of extraneural glioblastoma. This diagnosis was not possible with histology alone and only became evident after molecular profiling of the metastatic lesions and its comparison to the original tumor., (© The Author(s) 2024. Published by Oxford University Press.)

Background/aim: The prognostic value of scoring instruments was described for newly diagnosed glioblastoma. This study investigated five instruments in patients with recurrent tumors., Patients and Methods: Original Glasgow Prognostic Score (oGPS), modified GPS (mGPS), high-sensitivity mGPS (HS-mGPS), high-sensitivity oGPS (HS-oGPS), LabBM score, and 10 other factors were analyzed for progression-free survival (PFS) and overall survival (OS) in 51 patients., Results: On univariate analyses, oGPS 0-1, mGPS 0-1, and LabBM score 0-1.0 were significantly associated with improved OS and showed trends for improved PFS. On multivariate analysis, a trend was found for associations between LabBM score 0-1.0 and better OS. In addition, maximal cumulative diameter ≤40 mm and systemic therapy were independently associated with better PFS and OS, resection with PFS, and in-field recurrence with OS., Conclusion: Lower oGPS, mGPS, and LabBM scores were significantly associated with improved OS on univariate analyses. These instruments may be helpful when designing personalized treatment regimens for patients with recurrent glioblastoma., (Copyright © 2024 International Institute of Anticancer Research (Dr. George J. Delinasios), All rights reserved.)

Background/aim: In patients with recurrent glioblastoma, very little data are available regarding the prognostic value of platelet-to-lymphocyte (PLR) and neutrophil-to-lymphocyte (NLR) ratios. This study investigated potential associations between PLR or NLR and treatment outcomes., Patients and Methods: PLR and NLR at diagnosis of recurrence plus 10 additional characteristics were retrospectively analyzed for associations with progression-free survival (PFS) and overall survival (OS) in 75 patients with recurrent glioblastoma., Results: On multivariate analyses, maximal cumulative diameter of recurrent lesion(s) <40 mm (p=0.015) and systemic therapy (p<0.001) were associated with improved PFS. On multivariate analysis of OS, improved outcomes were significantly associated with PLR ≤150 (p=0.029), maximal cumulative diameter <40 mm (p=0.030), and systemic therapy (p=0.010)., Conclusion: In addition to other characteristics, PLR at the time of recurrence was identified as an independent predictor of OS in patients with recurrent glioblastoma. PLR may be useful when designing personalized treatment approaches or clinical trials., (Copyright © 2024, International Institute of Anticancer Research (Dr. George J. Delinasios), All rights reserved.)

The accurate diagnosis and classification of gliomas are essential for appropriate treatment planning and prognosis prediction. This study aimed to investigate the molecular diagnostics of IDH-wildtype diffuse astrocytic gliomas and identify potential genetic variants that could differentiate glioblastoma (GBM) from lower-grade gliomas when DNA methylation analysis is not feasible. In total, 479 H3-and IDH-wildtype diffuse astrocytic gliomas were included in this study. All the cases were diagnosed according to the 2021 World Health Organization (WHO) classification of central nervous system (CNS) tumors. Panel sequencing data were collected, and clinicopathological information was retrieved from medical records. Genetic alterations and histological findings were analyzed to determine their diagnostic utility and prognostic implications. Out of 479 cases, 439 (91.6%) were diagnosed with GBM, including 28 cases that were molecularly diagnosed as GBM. However, 40 (8.4%) cases could not be classified according to the 2021 WHO classification and were diagnosed as lower-grade diffuse astrocytic glioma, IDH-wildtype, not elsewhere classified (LGNEC). In addition to the three genetic alterations included in the diagnostic criteria of GBM, PTEN and EGFR mutations were found to be enriched in GBM. Patients harboring mTOR pathway mutations demonstrated a more favorable prognosis and often exhibited morphology resembling subependymal giant cell astrocytoma, along with a high tumor mutational burden. Among patients with mTOR pathway mutations, those lacking molecular diagnostic features of GBM exhibited outstanding survival outcomes, even in the presence of grade 4 histology. Integration of molecular features enhanced the diagnostic accuracy of IDH-wildtype gliomas. Some molecular alterations enriched in GBM offer valuable insights for molecular diagnosis and glioma classification. Furthermore, high-grade diffuse astrocytic gliomas featuring mTOR pathway mutations in the absence of molecular diagnostic features of GBM could represent more favorable tumor types distinct from GBM., (© 2024 The Authors. Brain Pathology published by John Wiley & Sons Ltd on behalf of International Society of Neuropathology.)

Background: Liquid biopsy represents a major development in cancer research, with significant translational potential. Similarly, it is increasingly recognized that multi-omic molecular approaches are a powerful avenue through which to understand complex and heterogeneous disease biology. We hypothesize that merging these two promising frontiers of cancer research will improve the discriminatory capacity of current models and allow for improved clinical utility., Methods: We have compiled a cohort of patients with glioblastoma, brain metastasis, and primary central nervous system lymphoma. Cell-free methylated DNA immunoprecipitation (cfMeDIP) and shotgun proteomic profiling was obtained from the cerebrospinal fluid (CSF) of each patient and used to build tumour-specific classifiers., Results: We show that the DNA methylation and protein profiles of cerebrospinal fluid can be integrated to fully discriminate lymphoma from its diagnostic counterparts with perfect AUC of 1 (95% confidence interval 1-1) and 100% specificity, significantly outperforming single-platform classifiers., Conclusions: We present the most specific and accurate CNS lymphoma classifier to date and demonstrates the synergistic capability of multi-platform liquid biopsies. This has far-reaching translational utility for patients with newly diagnosed intra-axial brain tumours., (© 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Objective: The detection of neoplastic cells in cerebral spinal fluid (CSF) is pivotal for the management of patients with central nervous system (CNS) tumours. This article delves into the CSF cytological characteristics of common CNS neoplasms, aligning with the 2021 World Health Organization (WHO) classification of CNS tumours., Methods: A retrospective review of CSF specimens positive for primary CNS neoplasms was performed at three tertiary medical centres. Only cases that had histopathologic confirmation and/or molecular workup were included., Results: Common primary CNS neoplasms seen in CSF cytology specimens include medulloblastoma, (non-WNT/non-SHH as well as SHH-activated and TP53 mutant), pineoblastoma, atypical teratoid/rhabdoid tumour (AT/RT), IDH-wildtype glioblastoma, and primary diffuse large B-cell lymphoma of the CNS. Ependymomas and germinomas can also have CSF involvement but are less common. Although the typical histologic architecture of these tumours may not be preserved in the CSF, unique cytomorphologic features such as nuclear moulding, nuclear pleomorphism, rhabdoid cells, prominent nucleoli and rosette formation can still be appreciated., Conclusion: Adopting the updated terminology and correlating cytologic observations with molecular findings will streamline the diagnostic process, reducing the complexities and ambiguities pathologists often encounter when analysing CSF specimens for potential primary CNS neoplasms., (© 2023 John Wiley & Sons Ltd.)

Purpose: Glioblastoma (GBM) is the most common primary brain cancer in adults with a very poor prognosis. Metabolic drivers of tumorigenesis are highly relevant within the central nervous system, where glucose is the critical source of energy. The impact of obesity on survival outcomes in patients with GBM is not well established. This study investigates the prognostic value of body mass index (BMI) in patients diagnosed with GBM., Methods: Adult patients with newly diagnosed GBM treated at Thomas Jefferson University Hospital between January 1, 2008, and December 31, 2022, were included in the study. BMI was calculated using the formula BMI = kg/m 2 . Patients BMI groups were underweight (BMI < 19.00), normal weight (BMI 19.00-24.99), overweight (BMI 25-29.99), and obese (BMI > 30.00). All patients received 60 Gy of radiation therapy with concurrent and adjuvant temozolomide following maximal safe resection. A difference in clinical outcomes of overall survival (OS) and progression-free survival (PFS) were evaluated between the groups using Kaplan-Meier and log-rank tests., Results: A total of 392 patients met inclusion criteria. The median age was 60.3 (range 18.9-86.7), with 144 females and 248 males. Median BMI was 27.0 (Range; 17.7-52.9). Non-overweight GBM patients (BMI < 25.00, OS 2.1 years, CI 1.7-2.4 years) had increased overall survival compared to overweight patients (BMI ≥ 25.00, OS 1.5 years, CI 1.4-1.6 years) (p < 0.001). Patients with MGMT-methylated GBM also had significantly greater OS and PFS compared to MGMT-unmethylated patients (p < 0.001). Non-overweight GBM patients (BMI < 25.00, median PFS 1.5 years, CI 1.3-2.0 years) also had increased progression-free survival compared to overweight patients (BMI ≥ 25.00, median PFS 1.1 years, CI 0.9-1.2 years) (p < 0.001)., Conclusions: Our study indicates normal BMI (19.00-24.99) at the time of GBM diagnosis is a favorable prognostic indicator for overall and progression-free survival. Additional studies are warranted for further analysis of BMI and survival outcomes in GBM patients., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. No grants or outside funding was acquired for the completion of this study as well., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

In this work, the dual-ligand lanthanide metal-organic framework (MOF)-based electrochemiluminescence (ECL) sensor was constructed for the detection of miRNA-128 in glioblastoma (GBM) diagnosis. The luminescent Eu-MOF (EuBBN) was synthesized with terephthalic acid (BDC) and 2-amino terephthalic acid (BDC-NH 2) as dual-ligand. Due to the antenna effect, EuBBN with conjugated-π structure exhibited strong luminescent signal and high quantum efficiency, which can be employed as ECL nanoprobe. Furthermore, the novel plasmonic CuS@Au heterostructure array has been prepared. The localized surface plasmon resonance coupling effect of the CuS@Au heterostructure array can amplify the ECL signal of EuBBN significantly. The EuBBN/CuS@Au heterostructure array-based sensing system has been prepared for the detection of miRNA-128 with a wide linear range from 1 fM to 1 nM and a detection limit of 0.24 fM. Finally, miRNA-128 in the clinic GBM tissue sample has been analysis for the distinguish of tumor grade successfully. The results demonstrated that the dual-ligand MOF /CuS@Au heterostructure array-based ECL sensor can provide important support for the development of GBM diagnosis. [ABSTRACT FROM AUTHOR]

"Tumor-to-tumor metastasis," an uncommon phenomenon, refers to a primary tumor metastasis into another tumor, with the most frequent donor being lung carcinoma and common recipients being renal cell carcinoma and meningioma. Tumor-to-tumor metastasis occurring in gliomas is rare with less than 20 reports described so far, and that into a glioblastoma is even rarer. We report a 54-year man, diagnosed with glioblastoma, IDH-wildtype, with metastasis of an adenocarcinoma into it. On histomorphology, the glial component was composed of astrocytic cells and showed increased mitosis, microvascular proliferation, and focal necrosis. This was intermingled with an adenocarcinomatous tumor with pleomorphic epithelial cells in glands, nests, and sheets. On immunohistochemistry, the adenocarcinomatous areas were positive for AE1/AE3 and TTF1 but negative for glial markers, ruling out adenoid glioblastoma. Further cytogenetic analysis showed EGFR amplification in the glial component but not in the adenocarcinoma component, ruling out glioblastoma with true epithelial metaplasia, and supporting the diagnosis of adenocarcinoma metastasis into glioblastoma. Glioblastomas may be susceptible to intratumoral metastasis due to the proliferating leaky vascular channels, however, the short survival of patients with glioblastoma may be responsible for the rarity of this occurrence. The documentation of these tumors is important as they may be important for clinical diagnosis and further treatment and prognosis., Competing Interests: Declaration of Conflicting InterestsThe authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

In this work, miRNA-10b in the glioblastoma (GBM) tumor tissues has been detected by a novel electrochemiluminescence (ECL) biosensor. Firstly, a new kind of bright luminescent Zn 2 GeO 4 :Mn NPs were prepared as ECL nanoprobe, which possessed high fluorescence quantum yield and ECL quantum efficiency. Secondly, Ti 3 C 2 MXene hydrogel (MXG) have been developed as the sensing interface. The MXG retained the inherent biocompatibility and mechanical features of hydrogel. Furthermore, the uniform distribution of metallic Ti 3 C 2 MXene in the hydrogel microstructure provided the good conductivity and multiple binding sites for biomolecules. MXene also can promote the separation of the electrons and holes to accelerate the electron-transfer rate and improve ECL efficiency. Due to these synergistic effects, the screen printed electrode was successfully modified with MXG as sensing platform to enhance the ECL intensity of Zn 2 GeO 4 :Mn NP, which greatly improved the detection efficiency and facilitated the high-throughput analysis. Finally, the toehold mediated strand displacement (TMSD) strategy was employed with then biosensor to detect miRNA-10b with the range of 10 fM to 1 nM. The limit of detection was 5 fM. This ECL biosensor has been used to analyze miRNA-10b expression in GBM tumor tissues, which possessed the great potential value for clinical diagnosis., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:Qiang MA reports financial support was provided by National Natural Science Foundation of China.., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Exosomes are crucial for the growth and spread of glioblastomas, an aggressive form of brain cancer. These tiny vesicles play a crucial role in the activation of signaling pathways and intercellular communication. They can also transfer a variety of biomolecules such as proteins, lipids and nucleic acids from donor to recipient cells. Exosomes can influence the immune response by regulating the activity of immune cells, and they are crucial for the growth and metastasis of glioblastoma cells. In addition, exosomes contribute to drug resistance during treatment, which is a major obstacle in the treatment of glioblastoma. By studying them, the diagnosis and prognosis of glioblastoma can be improved. Due to their high biocompatibility and lack of toxicity, they have become an attractive option for drug delivery. The development of exosomes as carriers of specific therapeutic agents could overcome some of the obstacles to effective treatment of glioblastoma. In this review, we address the potential of exosomes for the treatment of glioblastoma and show how they can be modified for this purpose., Competing Interests: Declaration of competing interest The authors report no conflict of interest., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Background: We aimed to investigate whether the HALP score was a predictor of survival in patients with Glioblastoma (GBM)., Methods: A total of 84 Glioblastoma (GBM) patients followed in our clinic were included in the study. HALP scores were calculated using the preoperative hemoglobin, albumin, lymphocyte and platelet results of the patients. For the HALP score, a cut-off value was found by examining the area below the receiver operating characteristic (ROC) curve. Patients were divided into two groups as low and high according to this cut-off value. The relationships among the clinical, dermographic and laboratory parameters of the patients were examined using these two groups., Results: Median OS, PFS, HALP score, NLR, PLR were 15 months (1.0-78.0), 8 months (1.0-66.0), 37.39 ± 23.84 (min 6.00-max 132.31), 4.14, 145.07 respectively. A statistically significant correlation was found between HALP score and OS, PFS, NLR, PLR, ECOG-PS status using Spearman's rho test (p = 0.001, p < 0.001, p < 0.001, p < 0.001, p = 0.026 respectively). For the HALP score, a cut-off value of = 37.39 (AUC = 0.698, 95% CI, p < 0.002) was found using ROC analysis. Median OS was 12 (6.99-17.01) months in the low HALP group and 21 (11.37-30.63) months in the high HALP group (p = 0.117). NLR and PLR were significantly lower in the HALP high group (p < 0.001, p < 0.001 respectively). The ratio of receiving treatment was significantly higher in the high HALP group (p < 0.05). In Multivariate analysis, significant results were found for treatment status and ECOG-PS status (p < 0.001, p = 0.038 respectively)., Conclusions: The HALP score measured at the beginning of treatment seems to have predictive importance in the prognosis of GBM patients. A HALP score of > 37.39 was associated with prolonged survival in high-grade brain tumors., (© 2024. The Author(s).)

Gliomas, particularly glioblastoma (GBM), represent the most prevalent and aggressive tumors of the central nervous system (CNS). Despite recent treatment advancements, patient survival rates remain low. The diagnosis of GBM traditionally relies on neuroimaging methods such as magnetic resonance imaging (MRI) or computed tomography (CT) scans and postoperative confirmation via histopathological and molecular analysis. Imaging techniques struggle to differentiate between tumor progression and treatment-related changes, leading to potential misinterpretation and treatment delays. Similarly, tissue biopsies, while informative, are invasive and not suitable for monitoring ongoing treatments. These challenges have led to the emergence of liquid biopsy, particularly through blood samples, as a promising alternative for GBM diagnosis and monitoring. Presently, blood and cerebrospinal fluid (CSF) sampling offers a minimally invasive means of obtaining tumor-related information to guide therapy. The idea that blood or any biofluid tests can be used to screen many cancer types has huge potential. Tumors release various components into the bloodstream or other biofluids, including cell-free nucleic acids such as microRNAs (miRNAs), circulating tumor DNA (ctDNA), circulating tumor cells (CTCs), proteins, extracellular vesicles (EVs) or exosomes, metabolites, and other factors. These factors have been shown to cross the blood-brain barrier (BBB), presenting an opportunity for the minimally invasive monitoring of GBM as well as for the real-time assessment of distinct genetic, epigenetic, transcriptomic, proteomic, and metabolomic changes associated with brain tumors. Despite their potential, the clinical utility of liquid biopsy-based circulating biomarkers is somewhat constrained by limitations such as the absence of standardized methodologies for blood or CSF collection, analyte extraction, analysis methods, and small cohort sizes. Additionally, tissue biopsies offer more precise insights into tumor morphology and the microenvironment. Therefore, the objective of a liquid biopsy should be to complement and enhance the diagnostic accuracy and monitoring of GBM patients by providing additional information alongside traditional tissue biopsies. Moreover, utilizing a combination of diverse biomarker types may enhance clinical effectiveness compared to solely relying on one biomarker category, potentially improving diagnostic sensitivity and specificity and addressing some of the existing limitations associated with liquid biomarkers for GBM. This review presents an overview of the latest research on circulating biomarkers found in GBM blood or CSF samples, discusses their potential as diagnostic, predictive, and prognostic indicators, and discusses associated challenges and future perspectives.

Background: Extracellular vesicles (EVs) obtained by noninvasive liquid biopsy from patient blood can serve as biomarkers. Here, we investigated the potential of circulating plasma EVs to serve as an indicator in the diagnosis, prognosis, and treatment response of glioblastoma patients., Methods: Plasma samples were collected from glioblastoma patients at multiple timepoints before and after surgery. EV concentrations were measured by nanoparticle tracking analysis and imaging flow cytometry. Tumor burden and edema were quantified by 3D reconstruction. EVs and tumors were further monitored in glioma-bearing mice., Results: Glioblastoma patients displayed a 5.5-fold increase in circulating EVs compared to healthy donors (P < .0001). Patients with higher EV levels had significantly shorter overall survival and progression-free survival than patients with lower levels, and the plasma EV concentration was an independent prognostic parameter for overall survival. EV levels correlated with the extent of peritumoral fluid-attenuated inversion recovery hyperintensity but not with the size of the contrast-enhancing tumor, and similar findings were obtained in mice. Postoperatively, EV concentrations decreased rapidly back to normal levels, and the magnitude of the decline was associated with the extent of tumor resection. EV levels remained low during stable disease, but increased again upon tumor recurrence. In some patients, EV resurgence preceded the magnetic resonance imaging detectability of tumor relapse., Conclusions: Our findings suggest that leakiness of the blood-brain barrier may primarily be responsible for the high circulating EV concentrations in glioblastoma patients. Elevated EVs reflect tumor presence, and their quantification may thus be valuable in assessing disease activity., (© The Author(s) 2024. Published by Oxford University Press on behalf of the Society for Neuro-Oncology. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)

Background: N6-methyladenosine (m6A) is one of the most common forms of mRNA modification, which is dynamically regulated by the m6A-related genes; however, its effect in glioblastoma (GBM) is still unknown., Objective: We sought to investigate the association between m6A-related genes (m6A-RGs) and GBM., Methods: Transcriptome data and the relevant clinical data were downloaded from The Cancer Genome Atlas and Gene Expression Omnibus databases. The m6A-RGs were identified from differently expressed genes, and COX and lasso regression models were applied to locate the prognosis-related genes., Results: We identified 15 out of 19 m6A-RGs differentially expressed between GBM and nontumor tissues. We identified two subgroups of GBM (clusters 1 and 2) by applying consensus clustering. Compared with the cluster 1 subgroup, the cluster 1 subgroup correlates with a poorer prognosis, and most of the 19 m6A-RGs are higher expressed in cluster 1. Through univariate Cox and lasso regression model, we identified three m6A-RGs, namely HNRNPC, ALKBH5, and FTO, which were used to construct a Cox regression risk model to predict the prognosis of GBM patients., Conclusion: We identified a valuable m6A model for predicting the prognosis of GBM patients, which can provide useful epigenetic biomarkers., (Copyright © 2024 Copyright: © 2024 Neurology India, Neurological Society of India.)

Purpose: Glioblastoma (GBM) is the most frequent glioma in adults with a high treatment resistance resulting into limited survival. The individual prognosis varies depending on individual prognostic factors, that must be considered while counseling patients with newly diagnosed GBM. The aim of this study was to elaborate a risk stratification algorithm based on reliable prognostic factors to facilitate a personalized prognosis estimation early on after diagnosis., Methods: A consecutive patient cohort with confirmed GBM treated between 2010 and 2021 was retrospectively analyzed. Clinical, radiological, and molecular parameters were assessed and included in the analysis. Overall survival (OS) was the primary outcome parameter. After identifying the strongest prognostic factors, a risk stratification algorithm was elaborated with estimated odds of survival., Results: A total of 462 GBM patients were analyzed. The strongest prognostic factors were Charlson Comorbidity Index (CCI), extent of tumor resection, and adjuvant treatment. Patients with CCI ≤ 1 receiving tumor resection had the highest survival odds (88% for 10 months). On the contrary, patients with CCI > 3 receiving no adjuvant treatment had the lowest survival odds (0% for 10 months). The 10-months survival rate in patients with CCI > 3 receiving adjuvant treatment was 56% for patients younger than 70 years and 22% for patients older than 70 years., Conclusion: A risk stratification algorithm based on significant prognostic factors allowed a personalized early prognosis estimation at the time of GBM diagnosis, that can contribute to a more personalized patient counseling., (© 2024. The Author(s).)

Objective: Glioblastoma (GBM) is one of the most aggressive brain tumors and often leads to poor outcomes. Studies have indicated that glycan levels are significantly correlated with the pathogenesis and development of cancers. However, whether glycan levels can serve as diagnostic or prognostic biomarkers in GBM remains unclear., Methods: We obtained glycomic profiles in tissue and serum samples from 55 individuals with GBM using a well-established lectin biochip platform probing with 11 specific lectins., Results: Our univariate analysis showed that 5 out of the 11 lectin-probed glycans (LPGs) were significantly higher in GBM tissues than in peri-tumoral tissues. After logistic regression analyses, only the Jacalin-probed T-antigen difference between the two groups remained significant (p = 0.037). Moreover, survival-related analyses showed that the level of Jacalin-probed T-antigen was significantly associated with the progression-free survival (p = 0.038) of patients. However, none of the LPG levels were correlated with the overall survival or the chemosensitivity to temozolomide therapy. The correlation coefficient analysis showed a moderate-to-strong correlation in the Jacalin-probed T-antigen levels between GBM tissues and serum samples, indicating its potential usefulness as a non-invasive GBM progression biomarker., Interpretation: Glycomics analyses can be helpful in the prediction of GBM recurrences and may provide information useful for GBM glycan-based target therapies or vaccine development., (© 2024 The Authors. Annals of Clinical and Translational Neurology published by Wiley Periodicals LLC on behalf of American Neurological Association.)

Background: Glioblastoma (GBM) is the most common and aggressive primary brain cancer. The treatment of GBM consists of a combination of surgery and subsequent oncological therapy, i.e., radiotherapy, chemotherapy, or their combination. If postoperative oncological therapy involves irradiation, magnetic resonance imaging (MRI) is used for radiotherapy treatment planning. Unfortunately, in some cases, a very early worsening (progression) or return (recurrence) of the disease is observed several weeks after the surgery and is called rapid early progression (REP). Radiotherapy planning is currently based on MRI for target volumes definitions in many radiotherapy facilities. However, patients with REP may benefit from targeting radiotherapy with other imaging modalities. The purpose of the presented clinical trial is to evaluate the utility of 11 C-methionine in optimizing radiotherapy for glioblastoma patients with REP., Methods: This study is a nonrandomized, open-label, parallel-setting, prospective, monocentric clinical trial. The main aim of this study was to refine the diagnosis in patients with GBM with REP and to optimize subsequent radiotherapy planning. Glioblastoma patients who develop REP within approximately 6 weeks after surgery will undergo 11 C-methionine positron emission tomography (PET/CT) examinations. Target volumes for radiotherapy are defined using both standard planning T1-weighted contrast-enhanced MRI and PET/CT. The primary outcome is progression-free survival defined using RANO criteria and compared to a historical cohort with REP treated without PET/CT optimization of radiotherapy., Discussion: PET is one of the most modern methods of molecular imaging. 11 C-Methionine is an example of a radiolabelled (carbon 11) amino acid commonly used in the diagnosis of brain tumors and in the evaluation of response to treatment. Optimized radiotherapy may also have the potential to cover those regions with a high risk of subsequent progression, which would not be identified using standard-of-care MRI for radiotherapy planning. This is one of the first study focused on radiotherapy optimization for subgroup of patinets with REP., Trial Registration: NCT05608395, registered on 8.11.2022 in clinicaltrials.gov; EudraCT Number: 2020-000640-64, registered on 26.5.2020 in clinicaltrialsregister.eu. Protocol ID: MOU-2020-01, version 3.2, date 18.09.2020., (© 2024. The Author(s).)

Safe and effective brain tumor surgery aims to remove tumor tissue, not non-tumoral brain. This is a challenge since tumor cells are often not visually distinguishable from peritumoral brain during surgery. To address this, we conducted a multicenter study testing whether the Sentry System could distinguish the three most common types of brain tumors from brain tissue in a label-free manner. The Sentry System is a new real time, in situ brain tumor detection device that merges Raman spectroscopy with machine learning tissue classifiers. Nine hundred and seventy-six in situ spectroscopy measurements and colocalized tissue specimens were acquired from 67 patients undergoing surgery for glioblastoma, brain metastases, or meningioma to assess tumor classification. The device achieved diagnostic accuracies of 91% for glioblastoma, 97% for brain metastases, and 96% for meningiomas. These data show that the Sentry System discriminated tumor containing tissue from non-tumoral brain in real time and prior to resection., (© 2024. The Author(s).)

We present a quantitative sandwich immunoassay for CD63 Extracellular Vesicles (EVs) and a constituent surface cargo, EGFR and its activity state, that provides a sensitive, selective, fluorophore-free and rapid alternative to current EV-based diagnostic methods. Our sensing design utilizes a charge-gating strategy, with a hydrophilic anion exchange membrane functionalized with capture antibodies and a charged silica nanoparticle reporter functionalized with detection antibodies. With sensitivity and robustness enhancement by the ion-depletion action of the membrane, this hydrophilic design with charged reporters minimizes interference from dispersed proteins, thus enabling direct plasma analysis without the need for EV isolation or sensor blocking. With a LOD of 30 EVs/μL and a high relative sensitivity of 0.01% for targeted proteomic subfractions, our assay enables accurate quantification of the EV marker, CD63, with colocalized EGFR by an operator/sample insensitive universal normalized calibration. We analysed untreated clinical samples of Glioblastoma to demonstrate this new platform. Notably, we target both total and "active" EGFR on EVs; with a monoclonal antibody mAb806 that recognizes a normally hidden epitope on overexpressed or mutant variant III EGFR. Analysis of samples yielded an area-under-the-curve (AUC) value of 0.99 and a low p-value of 0.000033, surpassing the performance of existing assays and markers., (© 2024. The Author(s).)

Background: Glioblastoma is the most common malignant brain tumor, and thus it is important to be able to identify patients with this diagnosis for population studies. However, this can be challenging as diagnostic codes are nonspecific. The aim of this study was to create a computable phenotype (CP) for glioblastoma multiforme (GBM) from structured and unstructured data to identify patients with this condition in a large electronic health record (EHR)., Methods: We used the University of Florida (UF) Health Integrated Data Repository, a centralized clinical data warehouse that stores clinical and research data from various sources within the UF Health system, including the EHR system. We performed multiple iterations to refine the GBM-relevant diagnosis codes, procedure codes, medication codes, and keywords through manual chart review of patient data. We then evaluated the performances of various possible proposed CPs constructed from the relevant codes and keywords., Results: We underwent six rounds of manual chart reviews to refine the CP elements. The final CP algorithm for identifying GBM patients was selected based on the best F1-score. Overall, the CP rule "if the patient had at least 1 relevant diagnosis code and at least 1 relevant keyword" demonstrated the highest F1-score using both structured and unstructured data. Thus, it was selected as the best-performing CP rule., Conclusions: We developed and validated a CP algorithm for identifying patients with GBM using both structured and unstructured EHR data from a large tertiary care center. The final algorithm achieved an F1-score of 0.817, indicating a high performance, which minimizes possible biases from misclassification errors., (© The Author(s) 2023. Published by Oxford University Press on behalf of the Society for Neuro-Oncology. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)

Purpose: Data on lines of therapy (LOTs) for cancer treatment are important for clinical oncology research, but LOTs are not explicitly recorded in electronic health records (EHRs). We present an efficient approach for clinical data abstraction and a flexible algorithm to derive LOTs from EHR-based medication data on patients with glioblastoma multiforme (GBM)., Methods: Nonclinicians were trained to abstract the diagnosis of GBM from EHRs, and their accuracy was compared with abstraction performed by clinicians. The resulting data were used to build a cohort of patients with confirmed GBM diagnosis. An algorithm was developed to derive LOTs using structured medication data, accounting for the addition and discontinuation of therapies and drug class. Descriptive statistics were calculated and time-to-next-treatment (TTNT) analysis was performed using the Kaplan-Meier method., Results: Treating clinicians as the gold standard, nonclinicians abstracted GBM diagnosis with a sensitivity of 0.98, specificity 1.00, positive predictive value 1.00, and negative predictive value 0.90, suggesting that nonclinician abstraction of GBM diagnosis was comparable with clinician abstraction. Of 693 patients with a confirmed diagnosis of GBM, 246 patients contained structured information about the types of medications received. Of them, 165 (67.1%) received a first-line therapy (1L) of temozolomide, and the median TTNT from the start of 1L was 179 days., Conclusion: We described a workflow for extracting diagnosis of GBM and LOT from EHR data that combines nonclinician abstraction with algorithmic processing, demonstrating comparable accuracy with clinician abstraction and highlighting the potential for scalable and efficient EHR-based oncology research.

Glioblastoma (GBM) is the most aggressive tumor among the gliomas and intracranial tumors and to date prognosis for GBM patients remains poor, with a median survival typically measured in months to a few years depending on various factors. Although standardized therapies are routinely employed, it is clear that these strategies are unable to cope with heterogeneity and invasiveness of GBM. Furthermore, diagnosis and monitoring of responses to therapies are directly dependent on tissue biopsies or magnetic resonance imaging (MRI) techniques. From this point of view, liquid biopsies are arising as key sources of a variety of biomarkers with the advantage of being easily accessible and monitorable. In this context, extracellular vesicles (EVs), physiologically shed into body fluids by virtually all cells, are gaining increasing interest both as natural carriers of biomarkers and as specific signatures even for GBM. What makes these vesicles particularly attractive is they are also emerging as therapeutical vehicles to treat GBM given their native ability to cross the blood-brain barrier (BBB). Here, we reviewed recent advances on the use of EVs as biomarker for liquid biopsy and nanocarriers for targeted delivery of anticancer drugs in glioblastoma., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Ltd.)

Epithelioid glioblastoma (E-GBM) is an exceedingly rare subtype of isocitrate dehydrogenase (IDH)-wildtype glioblastoma, first included in the WHO 2016 classification and characterized by a dominant population of epithelioid cells. Its histological and molecular defining features remain troublesome. The significance of BRAF mutations to pathological diagnosis and surgical outcome has drawn increasing attention given their promising potential for future adjuvant therapies. Herein, we describe a unique case of an E-GBM in the atrium of the left lateral ventricle and comprehensively analyze the importance of BRAF status in a cohort of 211 E-GBMs from the literature. Our patient was a 40-year-old man with occipital pain. His brain MRI revealed a large intraventricular tumor at the same location as a signal change found 10 years earlier with no additional follow-up. He underwent gross total tumor removal followed by conventional adjuvant treatment. Histopathological diagnosis was consistent with IDH-wildtype E-GBM WHO grade 4 with pleomorphic xanthoastrocytoma-like areas. BRAF p.V600 mutation was demonstrated in the tumoral genetic study. In the cohort analyzed, male patients predominated (63%), the median age was 32 years old, and the 5-year survival rate following diagnosis was 4.2%. BRAF mutations were found in 60.3% of the tumors overall, with this rate increasing to 78.3% in young adults (19-49 years, P  < .001). Presence of BRAF mutations associated with tumor progression ( P  = .001), the event usually leading to death ( P  < .001). In conclusion, our study supports the importance of genetic BRAF p.V600 mutation analysis because its presence not only points to an E-GBM diagnosis but may also promote tumor progression., Competing Interests: Declaration of Conflicting InterestsThe authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Background: Glioblastoma (GBM) is the most frequent glioma in adults. The prognosis of GBM is very poor and new prognostic biomarkers are in urgent need to better select high-risk patients and guide the individual treatments., Methods: In our study, we compared the expression of interleukin-17 receptor D (IL17RD) between GBMs and normal tissues from TCGA database, and detected IL17RD mRNA in 17 fresh GBM pairs with qPCR. With immunohistochemistry, we investigated the expression of IL17RD in 156 GBM tissues and further evaluated its clinical significance. The associations between IL17RD and clinicopathological factors were assessed by Chi-square test. The prognostic significance of IL17RD was evaluated by univariate analysis with Kaplan-Meier method, and by multivariate analysis with Cox-regression Hazard model., Results: The TPMs and mRNAs of IL17RD in GBM were substantially lower than those in normal brain tissues. The rates of low or high expression of IL17RD accounted for 41.67% and 58.33% respectively. IL17RD was significantly associated with higher survival rates of GBM. The 3-year overall survival rates of patients with low and high IL17RD were 7.2% and 19.5% respectively. In the Cox-regression model, the IL17RD expression was defined as an independent prognostic biomarker of GBM. Patients with high IL17RD expression had a more favorable outcome than those with low IL17RD., Conclusions: High IL17RD expression was an independent prognostic indicator of GBM, suggesting a more favorable prognosis. Our results suggested that IL17RD detection may help find the high-risk patients which may receive more severe surveillance and more individual treatments.

The ATP1A3 gene is associated with the development and progression of neurological diseases. However, the pathological function and therapeutic value of ATP1A3 in glioblastoma (GBM) remains unknown. In this study, we tried to explore the correlation between the ATP1A3 gene expression and immune features in GBM samples. We found that ATP1A3 gene expression levels showed significant negative correlation with immune checkpoints such as PD-L1, CTLA-4 and IDO1. Next, ATP1A3 gene expression levels showed significant negative correlation with the anti-cancer immune cell process, the immune score and stromal score. By grouping ATP1A3 expression levels, we found that that immunomodulator-related genes and tumor-associated immune cell effector gene expression levels were associated with lower ATP1A3 expression. In addition, immunotherapy prediction pathway activity and a majority of the anti-cancer immune cell process activity levels were also showed to be correlated with lower ATP1A3 gene expression. Further, nine prognostic factors were identified by prognostic analysis, and a GBM prognostic model (risk score) was established. We applied the model to the TCGA GBM training set sample and the GSE4412 validation set sample and found that patients in the high risk score subgroup had significantly shorter survival time, demonstrating the prognostic value and prognostic efficacy of the risk score. Furthermore, ATP1A3 overexpression has also been found to sensitize cancer cells to anti-PD-1 therapy. In conclusion, we showed that ATP1A3 is a highly promising treatment target in GBM and the risk score is an independent prognostic factor for cancer and can be used to help guide the prediction of survival time in patients with GBM., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Glioblastoma (GBM) is the most common malignant brain tumor in adults. Despite advancements in treatment, the prognosis for patients with GBM remains poor due to its aggressive nature and resistance to therapy. CRISPR-based genetic screening has emerged as a powerful tool for identifying genes crucial for tumor progression and treatment resistance, offering promising targets for tumor therapy. In this review, we provide an overview of the recent advancements in CRISPR-based genetic screening approaches and their applications in GBM. We highlight how these approaches have been used to uncover the genetic determinants of GBM progression and responsiveness to various therapies. Furthermore, we discuss the ongoing challenges and future directions of CRISPR-based screening methods in advancing GBM research.

Glioblastoma (GB) is a devastating tumor of the central nervous system characterized by a poor prognosis. One of the best-established predictive biomarker in IDH-wildtype GB is O6-methylguanine-DNA methyltransferase (MGMT) methylation (mMGMT), which is associated with improved treatment response and survival. However, current efforts to monitor GB patients through mMGMT detection have proven unsuccessful. Small extracellular vesicles (sEVs) hold potential as a key element that could revolutionize clinical practice by offering new possibilities for liquid biopsy. This study aimed to determine the utility of sEV-based liquid biopsy as a predictive biomarker and disease monitoring tool in patients with IDH-wildtype GB. Our findings show consistent results with tissue-based analysis, achieving a remarkable sensitivity of 85.7% for detecting mMGMT in liquid biopsy, the highest reported to date. Moreover, we suggested that liquid biopsy assessment of sEV-DNA could be a powerful tool for monitoring disease progression in IDH-wildtype GB patients. This study highlights the critical significance of overcoming molecular underdetection, which can lead to missed treatment opportunities and misdiagnoses, possibly resulting in ineffective therapies. The outcomes of our research significantly contribute to the field of sEV-DNA-based liquid biopsy, providing valuable insights into tumor tissue heterogeneity and establishing it as a promising tool for detecting GB biomarkers. These results have substantial implications for advancing predictive and therapeutic approaches in the context of GB and warrant further exploration and validation in clinical settings., (© 2024. The Author(s).)

Glioblastoma is a common and aggressive malignant central nervous system tumor in adults. This study aims to evaluate and analyze the scientific results, collaboration countries, main research topics, and topics over time reported about glioblastoma. A bibliometric analysis of glioblastoma publications was performed mainly using R and Multbiplot software for author, journal, and resume. Associated statistic methods Latent Dirichlet Allocation (LDA) and HJ-Biplot. Inclusion criteria were research articles from the PubMed database published in English between 1973 and December 2022. A total of 64,823 documents with an annual growth rate of 8.27% indicates a consistent increase in research output over time. The results for the number of citations and significant publications showed Cancer Res, J Neuro-Oncol, and Neuro-Oncology are the most influential journals in the field of glioblastoma. The countries that concentrated research were the tumor United States, China, Germany, and Italy. Finally, there has been a marked growth in studies on prognosis and patient survival, therapies, and treatments for glioblastoma. These findings reinforce the need for increased global resources to address glioblastoma, particularly in underdeveloped countries. Glioblastoma research's exponential growth reflects sustained interest in early diagnosis and patient survival., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

We discuss a rare instance of cryptococcoma caused by Cryptococcus gattii in a 55-year-old woman initially treated for suspected COVID bronchopneumonia. The diagnosis posed a challenge due to vague symptoms and unclear imaging findings suggesting malignancy. Postoperative samples confirmed the presence of Cryptococcus gattii through culture of brain tissue and blood. Appropriate therapy was initiated, but despite treatment, it led to a fatal outcome. The case emphasizes the crucial role of microbiologist in early diagnosis of fungal infections of Central Nervous System. Additionally, the delayed diagnosis in immunocompetent individuals highlights the critical need for early recognition and intervention to mitigate potentially fatal outcomes., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Indian Association of Medical Microbiologists. Published by Elsevier B.V. All rights reserved.)

The use of various kinds of magnetic resonance imaging (MRI) techniques for examining brain tissue has increased significantly in recent years, and manual investigation of each of the resulting images can be a time-consuming task. This paper presents an automatic brain-tumor diagnosis system that uses a CNN for detection, classification, and segmentation of glioblastomas; the latter stage seeks to segment tumors inside glioma MRI images. The structure of the developed multi-unit system consists of two stages. The first stage is responsible for tumor detection and classification by categorizing brain MRI images into normal, high-grade glioma (glioblastoma), and low-grade glioma. The uniqueness of the proposed network lies in its use of different levels of features, including local and global paths. The second stage is responsible for tumor segmentation, and skip connections and residual units are used during this step. Using 1800 images extracted from the BraTS 2017 dataset, the detection and classification stage was found to achieve a maximum accuracy of 99%. The segmentation stage was then evaluated using the Dice score, specificity, and sensitivity. The results showed that the suggested deep-learning-based system ranks highest among a variety of different strategies reported in the literature., (© 2024. The Author(s).)

Glioblastoma is currently considered the most common and, unfortunately, also the most aggressive primary brain tumor, with the highest morbidity and mortality rates. The average survival of patients diagnosed with glioblastoma is 14 months, and only 2% of patients survive 3 years after surgery. Based on our clinical experience and knowledge from extensive clinical studies, survival is mainly related to the molecular biological properties of glioblastoma, which are of interest to the general medical community. Our study examined a total of 71 retrospective studies published from 2016 through 2022 and available on PubMed that deal with mutations of selected genes in the pathophysiology of GBM. In conclusion, we can find other mutations within a given gene group that have different effects on the prognosis and quality of survival of a patient with glioblastoma. These mutations, together with the associated mutations of other genes, as well as intratumoral heterogeneity itself, offer enormous potential for further clinical research and possible application in therapeutic practice.

Novel blood-circulating molecules, as potential biomarkers for glioblastoma multiforme (GBM) diagnosis and monitoring, are attracting particular attention due to limitations of imaging modalities and invasive tissue biopsy procedures. This study aims to assess the diagnostic and prognostic values of circulating cell-free DNA (cfDNA) in relation to inflammatory status in GBM patients and to determine the concentration and average size of DNA fragments typical of tumour-derived DNA fractions. Preoperative plasma samples from 40 patients (GBM 65.0 ± 11.3 years) and 40 healthy controls (HC 70.4 ± 5.4 years) were compared. The cfDNA concentrations and lengths were measured using the electrophoresis platform, and inflammatory indices (NLR, PLR, LMR, and SII) were calculated from complete blood cell analysis. More fragmented cfDNA and 4-fold higher 50-700 bp cfDNA concentrations were detected in GBM patients than in healthy controls. The average cfDNA size in the GBM group was significantly longer (median 336 bp) than in the HC group (median 271 bp). Optimal threshold values were 1265 pg/μL for 50-700 bp cfDNA (AUC = 0.857) and 290 bp for average cfDNA size (AUC = 0.814). A Kaplan-Meier survival curves analysis also demonstrated a higher mortality risk in the GBM group with a cut-off >303 bp cfDNA. This study is the first to have revealed glioblastoma association with high levels of cfDNA > 1000 pg/μL of 50-700 bp in length, which can be aggravated by immunoinflammatory reactivity.

Brain tumors are one of the most dangerous, because the position of these are in the organ that governs all life processes. Moreover, a lot of brain tumor types were observed, but only one main diagnostic method was used - histopathology, for which preparation of sample was long. Consequently, a new, quicker diagnostic method is needed. In this paper, FT-Raman spectra of brain tissues were analyzed by Principal Component Analysis (PCA), Hierarchical Cluster Analysis (HCA), four different machine learning (ML) algorithms to show possibility of differentiating between glioblastoma G4 and meningiomas, as well as two different types of meningiomas (atypical and angiomatous). Obtained results showed that in meningiomas additional peak around 1503 cm -1 and higher level of amides was noticed in comparison with glioblastoma G4. In the case of meningiomas differentiation, in angiomatous meningiomas tissues lower level of lipids and polysaccharides were visible than in atypical meningiomas. Moreover, PCA analyses showed higher distinction between glioblastoma G4 and meningiomas in the FT-Raman range between 800 cm -1 and 1800 cm -1 and between two types of meningiomas in the range between 2700 cm -1 and 3000 cm -1 . Decision trees showed, that the most important peaks to differentiate glioblastoma and meningiomas were at 1151 cm -1 and 2836 cm -1 while for angiomatous and atypical meningiomas - 1514 cm -1 and 2875 cm -1 . Furthermore, the accuracy of obtained results for glioblastoma G4 and meningiomas was 88 %, while for meningiomas - 92 %. Consequently, obtained data showed possibility of using FT-Raman spectroscopy in diagnosis of different types of brain tumors., (Copyright © 2024. Published by Elsevier Inc.)

Backgrounds: The prognostic significance of the platelet volume indices (PVIs), including the platelet distribution width (PDW), mean platelet volume (MPV), and platelet distribution width-to-platelet count ratio (PDW/P) has been demonstrated in a variety of malignancies. This study aimed to evaluate the prognostic value of PVIs in patients with a newly diagnosed glioblastoma multiforme (GBM)., Methods: We retrospectively evaluated the clinical data of 143 patients with GBM who managed at our center between May 2010 and May 2019. Receiver operating characteristic curves (ROC) for cutoff value determination, Kaplan-Meier survival analysis, and univariate and multivariate Cox regression analyses were performed., Results: The corresponding cutoff values for MPV, PDW, and PDW/P were 9.05, 14.7, and 0.51, respectively. The Kaplan-Meier survival analyses showed that patients with an MPV < 9.05 and those with PDW <14.7 and cases with PDW/ p  < 0.51 had a longer overall survival (OS) ( p  < 0.05). Based on univariate analysis, age, Karnofsky Performance Status scores (KPS), tumor focality, MPV, PDW, and PDW/P were predictors of OS ( p  < 0.05). Final multivariate Cox regression analyses showed age (HR 1.040, 95% CI 1.009-1.071, P,0.011), KPS (HR 2.208, 95% CI 1.107-4.405, P,0.025), tumor focality (HR 4.596, 95% CI 1.988-10.626, p  < 0.001), and PDW/P (HR 1.786, 95% CI 1.103-3.072, P,0.037) as the independent predictors of OS in patients with newly diagnosed glioblastoma., Conclusions: Our results suggest an elevated preoperative PDW/P, along with previously established variables, as a simple and inexpensive prognostic factor for patients with GBM.

Glioblastoma (GBM) is the most common type of malignant brain tumor.The discovery of microRNAs and their unique properties have made them suitable tools as biomarkers for cancer diagnosis, prognosis, and evaluation of therapeutic response using different types of nanomaterials as sensitive and specific biosensors. In this review, we discuss microRNA-based electrochemical biosensing systems and the use of nanoparticles in the evolving development of microRNA-based biosensors in glioblastoma., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Glioblastoma multiforme (GBM) is the most common and malignant type of primary brain tumor in adults. Despite important advances in understanding the molecular pathogenesis and biology of this tumor in the past decade, the prognosis for GBM patients remains poor. GBM is characterized by aggressive biological behavior and high degrees of inter-tumor and intra-tumor heterogeneity. Increased understanding of the molecular and cellular heterogeneity of GBM may not only help more accurately define specific subgroups for precise diagnosis but also lay the groundwork for the successful implementation of targeted therapy. Herein, we systematically review the key achievements in the understanding of GBM molecular pathogenesis, mechanisms, and biomarkers in the past decade. We discuss the advances in the molecular pathology of GBM, including genetics, epigenetics, transcriptomics, and signaling pathways. We also review the molecular biomarkers that have potential clinical roles. Finally, new strategies, current challenges, and future directions for discovering new biomarkers and therapeutic targets for GBM will be discussed.