Search Results (608)

The Gliomas account for 81% of all malignant central nervous system tumors and are classified by WHO into four grades of malignancy. Glioblastoma multiforme (GBM), the most common grade IV glioma, exhibits an extremely aggressive phenotype and a dismal five-year survival rate of only 6%, underscoring the urgent need for novel therapeutic approaches. Immunotherapy has emerged as a promising strategy, and photodynamic therapy (PDT) in particular has attracted attention for its dual cytotoxic and immunostimulatory effects. In GBM models, PDT induces immunogenic cell death characterized by the release of damage-associated molecular patterns (DAMPs), which promote antigen presentation and activate T cell responses. Additionally, PDT transiently increases blood–brain barrier permeability, facilitating immune cell infiltration into the tumor microenvironment, and enhances clearance of waste products via stimulation of meningeal lymphatic vessels. Importantly, PDT can reprogram or inactivate immunosuppressive tumor-associated macrophages, thereby counteracting the pro-tumoral microenvironment. Despite these encouraging findings, further preclinical and clinical studies are required to elucidate PDT’s underlying immunological mechanisms fully and to optimize treatment regimens that maximize its efficacy as part of integrated immunotherapeutic strategies against GBM. Full article

Brain cancer is a heterogeneous collection of malignant neoplasms, such as glioblastoma multiforme (GBM), astrocytomas and medulloblastomas, with high morbidity and mortality. Its treatment is complicated by the tumor’s site, infiltrative growth mode and selective permeability of the blood–brain barrier (BBB). During tumor formation, the BBB dynamically remodels into the blood–brain tumor barrier (BBTB), disrupting homeostasis and preventing drug delivery. Furthermore, the TME (Tumor Micro Environment) supports drug resistance, immune evasion and treatment failure. This review points out the ways in which nanomedicine overcomes these obstacles with custom-designed delivery systems, sophisticated diagnostics and personalized therapies. Traditional treatments fail through a lack of BBB penetration, non-specific cytotoxicity and swift tumor adaptation. Nanomedicine provides greater drug solubility, protection against enzymatic degradation, target drug delivery and control over the release. Nanotheranostics’ confluence of therapeutic and diagnostic modalities allows for dynamic adjustment and real-time monitoring. Nanotechnology has paved the way for the initiation of a new era in precision neuro-oncology. Transcending the limitations of conventional therapy protocols, nanomedicine promises to deliver better outcomes by way of enhanced targeting, BBB penetration and real-time monitoring. Multidisciplinary collaboration, regulatory advancements and patient-centered therapy protocols customized to the individual patient’s tumor biology will be necessary to facilitate translation success in the future. Full article

Glioblastoma multiforme (GBM) is an aggressive and molecularly heterogeneous brain cancer with a poor prognosis. Despite advancements in standard-of-care therapies, including surgery, radiotherapy, and temozolomide (TMZ), the median survival remains approximately 15 months, with a 5-year survival rate of less than 10%. We and others have demonstrated that FOXM1 is a critical oncogenic driver of GBM cell proliferation. However, the role of FOXM1 and its interaction with other oncogenic signaling pathways in GBM remains incompletely understood. In this study, we identified FOXM1, AXL, and eEF2K as highly upregulated oncogenes in GBM patient tumors. We demonstrated, for the first time, that FOXM1 directly interacts with AXL and eEF2K, regulating their expression and promoting GBM cell proliferation, migration, and invasion. Knockdown of these genes disrupted cell proliferation, spheroid formation, migration, and invasion, and induced apoptosis and ferroptosis. Additionally, inhibiting the FOXM1–AXL/eEF2K signaling axis sensitized GBM cells to TMZ, further enhancing apoptotic and ferroptotic responses. These findings highlight the critical role of the FOXM1–AXL/eEF2K signaling pathway in GBM progression and suggest that targeting this axis may offer a novel multitargeted therapeutic strategy in GBM. Full article

Glioblastoma multiforme (GBM), the most aggressive and therapy-resistant glioma subtype, remains an urgent clinical challenge due to its invasive nature, molecular heterogeneity, and the protective constraints of the blood–brain barrier (BBB). Liposomes and extracellular vesicles (EVs) have emerged as two of the most promising nanocarrier systems capable of overcoming these limitations through improved drug delivery and cellular targeting. Their applications in glioma therapy span chemotherapy, immunotherapy, and gene therapy, each presenting distinct advantages and mechanisms of action. Liposomes offer structural flexibility, controlled release, and a well-established clinical framework, while EVs provide innate biocompatibility, low immunogenicity, and the ability to mimic natural intercellular communication. Both systems demonstrate the capacity to traverse the BBB and selectively accumulate in tumor tissue, yet they differ in scalability, cargo loading efficiency, and translational readiness. Comparative evaluation of their functions across therapeutic modalities reveals complementary strengths that may be leveraged in the development of more effective, targeted strategies for glioma treatment. Full article

Being one of the most aggressive primary brain tumors, glioblastoma multiforme (GBM) is known from the median survivals of just 15 months following diagnosis. Conventional treatments, including surgical resection, radiotherapy, and chemotherapy, have limited efficiency due to the invasive nature of glioma cells and the presence of a blood–brain barrier. Therefore, adjuvant therapy in the form of a localized delivery of chemotherapeutic agents is indispensable to increase the chances of patients. Among a variety of advanced drug carriers, collagen has recently emerged as an excellent choice for regional chemotherapy, mainly due to its biocompatibility, biodegradability, weak antigenicity, biomimetics, and well-known safety profile, as well as its native presence in the extracellular matrix of the central nervous system. The aim of this paper is to highlight the most recent studies describing the application of collagen as a drug carrier able to provide an extended delivery of chemotherapeutic agents directly to the GBM site, and to provide exciting opportunities for its future applications. Full article

Background: Gliomas represent the most prevalent and aggressive primary brain tumors, requiring precise classification to guide treatment strategies and improve patient outcomes. Purpose: This study aimed to develop and evaluate a machine learning-driven approach for glioma classification by identifying the most relevant genetic and clinical biomarkers while demonstrating clinical utility. Methods: A dataset from The Cancer Genome Atlas (TCGA) containing 23 features was analyzed using an integrative approach combining Boruta, Least Absolute Shrinkage and Selection Operator (LASSO), and SHapley Additive exPlanations (SHAP) for feature selection. The refined feature set was used to train four machine learning models: Random Forest, Support Vector Machine, XGBoost, and Logistic Regression. Comprehensive evaluation included class distribution analysis, calibration assessment, and decision curve analysis. Results: The feature selection approach identified 13 key predictors, including IDH1, TP53, ATRX, PTEN, NF1, EGFR, NOTCH1, PIK3R1, MUC16, CIC mutations, along with Age at Diagnosis and race. XGBoost achieved the highest AUC (0.93), while Logistic Regression recorded the highest testing accuracy (88.09%). Class distribution analysis revealed excellent GBM detection (Average Precision 0.840–0.880) with minimal false negatives (5–7 cases). Calibration analysis demonstrated reliable probability estimates (Brier scores 0.103–0.124), and decision curve analysis confirmed substantial clinical utility with net benefit values of 0.36–0.39 across clinically relevant thresholds. Conclusions: The integration of feature selection techniques with machine learning models enhances diagnostic precision, interpretability, and clinical utility in glioma classification, providing a clinically ready framework that bridges computational predictions with evidence-based medical decision-making. Full article

Background/Objectives: Glioblastoma multiforme (GBM) is an aggressive and heterogeneous brain tumor with poor prognosis, emphasizing the need for reliable molecular biomarkers to improve patient stratification and treatment planning. This study aimed to identify key genes associated with overall survival in GBM by employing and comparing machine learning (ML) and deep learning (DL) approaches using RNA-Seq gene expression data. Methods: RNA-Seq expression and clinical data for primary GBM tumors were obtained from The Cancer Genome Atlas (TCGA). A univariate Cox proportional hazards regression was used to identify survival-associated genes. For survival prediction, ML-based feature selection techniques—RF, GB, SVM-RFE, RF-RFE, and PCA—were used to construct multivariate Cox models. Separately, DeepSurv, a DL-based survival model, was trained using the significant genes from the univariate analysis. Gradient-based importance scoring was applied to determine key genes from the DeepSurv model. Results: Univariate analysis yielded 694 survival-associated genes. The best ML-based Cox model (RF-RFE with 90% training data) achieved a c-index of 0.725. In comparison, DeepSurv demonstrated superior performance with a c-index of 0.822. The top 10 genes were identified from the DeepSurv analysis, including CMTR1, GMPR, and PPY. Kaplan–Meier survival curves confirmed their prognostic significance, and network analysis highlighted their roles in processes such as purine metabolism, RNA processing, and neuroendocrine signaling. Conclusions: This study demonstrates the effectiveness of combining ML and DL models to identify prognostic gene expression biomarkers in GBM, with DeepSurv providing higher predictive accuracy. The findings offer valuable insights into GBM biology and highlight candidate biomarkers for further validation and therapeutic development. Full article

Glioblastoma multiforme (GBM) is a highly aggressive, treatment-resistant grade IV brain tumor with poor prognosis that grows rapidly and invades surrounding tissues, complicating surgery and frequently recurring. Although the crucial role of endogenous peptides has been highlighted for several tumors, the specific peptidomic profile of GBM remains unexplored to date. This study aimed to perform a preliminary characterization of the low molecular mass proteome fraction of Cavitron Ultrasonic Surgical Aspirator (CUSA) fluid collected from different tumor zones, i.e., the core and tumor periphery of newly diagnosed (ND) and recurrent (R) GBM. The samples, pooled by tumor type and collection zone, were centrifuged through molecular cut-off filter devices to collect the non-retained fraction of the proteome <10 kDa for direct full-length LC-MS analysis. A total of 40 and 24 peptides, fragments of 32 and 18 proteins, were marked as ND and R GBM COREs, respectively, while 132 peptides, fragments of 46 precursor proteins, were identified as common and included proteins which were cancer-related or involved in GBM pathophysiology. Besides providing a preliminary overview of the unexplored peptidome of GBM, this pilot study confirms peptidomics as a promising tool to discover potential GBM biomarkers in the perspective of clinical applications increasingly oriented towards a precision medicine approach. Data are available via ProteomeXchange with the identifier PXD060807. Full article

Glioblastoma multiforme (GBM) is the most aggressive and common primary brain tumor. Magnetic resonance imaging (MRI) provides detailed visualization of tumor morphology, edema, and necrosis. However, manually segmenting GBM from MRI scans is time-consuming, subjective, and prone to inter-observer variability. Therefore, automated and reliable segmentation methods are crucial for improving diagnostic accuracy. This study employs an image semantic segmentation model to segment brain tumors in MRI scans of GBM patients. The MRI recall images include T1-weighted imaging (T1WI) and fluid-attenuated inversion recovery (FLAIR) sequences. To enhance the performance of the semantic segmentation model, image preprocessing techniques were applied before analyzing and comparing commonly used segmentation models. Additionally, a survival model was constructed using discrete genotype attributes of GBM patients. The results indicate that the DeepLabV3+ model achieved the highest accuracy for semantic segmentation, with an accuracy of 77.9% on T1WI image sequences, while the U-Net model achieved 80.1% accuracy on FLAIR image sequences. Furthermore, in constructing the survival model using a discrete attribute dataset, the dataset was divided into three subsets based on different missing value handling strategies. This study found that replacing missing values with 1 resulted in the highest accuracy, with the Bernoulli Bayesian model and the multinomial Bayesian model achieving an accuracy of 94.74%. This study integrates image preprocessing techniques and semantic segmentation models to improve the accuracy and efficiency of brain tumor segmentation while also developing a highly accurate survival model. The findings aim to assist physicians in saving time and facilitating preliminary diagnosis and analysis. Full article

Malignant gliomas, including glioblastoma multiforme (GBM), are highly aggressive brain tumors with a poor prognosis and limited treatment options. This study investigates the antitumor potential of bioactive compounds derived from Cannabis sativa and Piper nigrum using molecular docking, cell viability assays, and transcriptomic and expression analyses from public databases in humans and cell lines. Cannabichromene (CBC), cannabigerol (CBG), cannabidiol (CBD), and Piper nigrum derivates exhibited strong binding affinities relative to glioblastoma-associated targets GPR55 and PINK1. In vitro analyses demonstrated their cytotoxic effects on glioblastoma cell lines (U87MG, T98G, and CCF-STTG1), as well as on neuroblastoma (SH-SY5Y) and oligodendroglial (MO3.13) cell lines, revealing interactions among these compounds. The differential expression of GPR55 and PINK1 in tumor versus normal tissues further supports their potential as biomarkers and therapeutic targets. These findings provide a basis for the development of novel therapies and suggest unexplored molecular pathways for the treatment of malignant glioma. Full article

Glioblastoma multiforme (GBM) is a highly aggressive primary brain tumor with a dismal prognosis despite advances in multimodal treatment. Conventional therapies fail to achieve durable responses due to GBM’s molecular heterogeneity and capacity to evade therapeutic pressures. Epigenetic alterations have emerged as critical contributors to GBM pathobiology, including aberrant DNA methylation, histone modifications, and non-coding RNA (ncRNA) dysregulation. These mechanisms drive oncogenesis, therapy resistance, and immune evasion. This scoping review evaluates the current state of knowledge on epigenetic modifications in GBM, synthesizing findings from original articles and preclinical and clinical trials published over the last decade. Particular attention is given to MGMT promoter hypermethylation status as a biomarker for temozolomide (TMZ) sensitivity, histone deacetylation and methylation as modulators of chromatin structure, and microRNAs as regulators of pathways such as apoptosis and angiogenesis. Therapeutically, epigenetic drugs, like DNA methyltransferase inhibitors (DNMTis) and histone deacetylase inhibitors (HDACis), appear as promising approaches in preclinical models and early trials. Emerging RNA-based therapies targeting dysregulated ncRNAs represent a novel approach to reprogram the tumor epigenome. Combination therapies, pairing epigenetic agents with immune checkpoint inhibitors or chemotherapy, are explored for their potential to enhance treatment response. Despite these advancements, challenges such as tumor heterogeneity, the blood–brain barrier (BBB), and off-target effects remain significant. Future directions emphasize integrative omics approaches to identify patient-specific targets and refine therapies. This article thus highlights the potential of epigenetics in reshaping GBM treatment paradigms. Full article

Glioblastoma Multiforme (GBM) is one of the most common brain tumors and is associated with aggressive tumor characteristics and extremely poor patient survival. The median survival time for GBM patients is around 12–15 months. Temozolomide (TMZ) is a key chemotherapeutic drug used in the treatment of GBM. However, at least 50% of GBM patients do not respond to TMZ, necessitating the identification of novel therapeutic strategies sensitizing patients to TMZ. In this study, we aimed to investigate the effects of two different tumor suppressor microRNAs (miR-329 and miR-449b) on cell proliferation and migration of GBM cells, and their potential for sensitizing GBM cells to TMZ. Our findings show that MiR-329/449b treatments suppressed spheroid formation and migration of GBM (LN229 and U87) cells. When miR treatments were combined with Temozolomide (TMZ), we also observed that they synergistically enhanced the suppressive effects of TMZ and inhibited the activity of clinically significant NF-KB and Src/FAK signaling pathways, making the combination therapy a viable option to treat GBM, with greater impact on patient survival. Full article

Glioblastoma multiforme (GBM) is a deadly disease known for its genetic heterogeneity. LTR12C is an endogenous retrovirus-derived regulator of pro-apoptotic genes and is normally silenced by epigenetic regulation. In this study, we found that the treatment of two glioblastoma cell lines, T98-G and U87-MG, with DNA methyltransferase (DNMT) and histone deacetylase (HDAC) inhibitors activated LTR12C expression. Combined treatment with these epigenetic drugs exerted a synergistic action on the LTR12C activation in both cell lines, while treatment with each drug as a single agent had a far weaker effect. A strong induction of the expression of the TP63 gene was seen in both cell lines, with the pro-apoptotic isoform GTA-p63 accounting for most of this increase. Coherently, downstream targets of p63, such as p21 and PUMA, were also induced by the combined treatment. Furthermore, we observed a significant reduction in the GBM cell growth and viability following the dual DNMT/HDAC inhibition. These findings reveal that the reactivation of LTR12C expression has the potential to modulate survival pathways in glioblastoma and provide information regarding possible epigenetic mechanisms that can be used to treat this deadly disease. Full article

Background: Three-dimensional (3D) cell models may bridge the gap between two-dimensional (2D) cell cultures and animal models. Technical advances have led to the development of 3D-bioprinted cell models, characterized by greater reproducibility and the ability to mimic in vivo conditions. Glioblastoma multiforme (GBM) is a highly aggressive brain tumor with poor clinical outcomes due to its heterogeneity, angiogenic activity, and invasiveness. Src family kinases (SFKs) play a crucial role in GBM progression, making them attractive targets for drug development. Here, we show results about the pharmacological profile of a new prodrug synthesized from a Src inhibitor, SI306. Methods: Three-dimensional-bioprinted GBM cell models were used in predicting the antitumor activity of the prodrug SI306-PD2 with respect to its precursor, SI306. Results: Since the prodrug releases the active inhibitor through the cleavage by specific enzymes, SI306-PD2 was analyzed for stability and release kinetics in various media, including fetal bovine serum (FBS), which is normally used in cell culture. In comparison to SI306, SI306-PD2 demonstrated higher solubility in water, higher permeability across gastrointestinal and blood–brain barrier membranes, and the ability to release the drug in the presence of FBS progressively. In the 2D GBM cell model, using U87 and U251 cell lines, both compounds similarly reduced tumor cell viability. In 3D-bioprinted cell models, in the presence of an FBS-free medium, SI306-PD2 exhibited a more effective antitumor activity compared to SI306, reducing the proliferation and diameter of U251 spheroids grown within the bioprinted scaffold in a statistically significant manner. The analysis of proteins extracted from 3D scaffolds confirmed that SI306-PD2 inhibited Src activation more efficiently than SI306. Conclusions: Our study suggests that, when tissue permeability represents a discriminating characteristic, bioprinted cell models can provide a valid alternative for studying the cytotoxicity of new antitumor compounds. This approach has permitted us to ascertain the potential of the prodrug SI306-PD2 as a therapeutic agent for GBM, demonstrating better tissue penetration and antiproliferative efficacy compared to the precursor compound SI306. Full article

Brain tumors, in particular gliomas and glioblastoma multiforme (GBM), are thought to originate from different cells facing specific founding insults, a feature that partly justifies the complexity and heterogeneity of these severe forms of cancer. However, gliomas and GBM are usually reproduced in animal models by inducing molecular alterations in mature glial cells, which, though being part of the puzzle, do not represent the whole picture. To fill this conceptual gap, we previously developed a neurogenic model of brain cancer in Drosophila, demonstrating that the loss of cell polarity in neural stem cells (called neuroblasts in the fruit fly) is sufficient to promote the formation of malignant masses that continue to grow in the adult, displaying several phenotypic traits typical of human GBM. Here, we expand on previous work by restricting polarity disruption to Drosophila type II neuroblasts, whose self-renewal is comparable to that of mammalian neural progenitors, with the aim to capture the molecular signature of the resulting cancers in a specific and reproducible context. A comparison of the most deregulated transcripts with those found in human primary GBMs confirmed that our model can be proficiently used to delve into the roots of human brain tumorigenesis. Full article