Search Results (425)

Background/Objectives: For low-grade glioma (LGG) patients, adjuvant treatment (AT) with radiotherapy and chemotherapy may adversely impact cognition. However, existing evidence is limited by methodological heterogeneity and shortcomings. This study explored the cognitive functioning of LGG patients who underwent resection with both radiotherapy and chemotherapy (AT+) or without AT (AT−), from before resection to one year after resection. Methods: We included patients with World Health Organization 2021 grade 2 isocitrate dehydrogenase-mutated glioma who underwent resection between 2011 and 2024. All patients completed a neuropsychological screening battery one week before (T0) and twelve months after resection (T12), measuring reaction time, attention span, information processing speed, working memory, inhibition, cognitive flexibility, and verbal fluency. We compared cognitive performance between AT+ and AT− patients at T0 and T12, as well as trajectories of cognitive functioning, at the group and individual level. Results: We included 60 LGG patients (M age = 38.8 years; 63.3% male). Compared to AT− patients (n = 35), AT+ patients (n = 25) were significantly older, more frequently had tumors that crossed the midline, and reported more depressive symptoms. At T0, no significant cognitive performance differences existed between AT+ and AT− patients, despite lower observed performance in the AT+ group. At T12, AT+ patients performed significantly worse than AT− patients on mean information processing speed, due to an improvement over time in the AT− group. Conclusions: Patients allocated to AT may show limited cognitive recovery of information processing speed up to 12 months after surgery, without pronounced effects on other cognitive functions. These findings can guide future studies into treatment-related cognitive decline of LGG patients. Full article

Background: Awake craniotomy with intraoperative mapping is the standard of care for gliomas located in language-eloquent regions, enabling maximal safe resection while preserving functional integrity. This study aimed to identify clinical and intraoperative predictors of postoperative language worsening and overall survival in patients undergoing awake surgery for malignant glioma. Methods: In this retrospective multicenter cohort study, 37 patients with malignant glioma in the dominant hemisphere underwent awake craniotomy with intraoperative mapping. Clinical, radiological, intraoperative, and postoperative variables were analyzed. Language outcome was classified as unchanged or worsened. Univariable and parsimonious multivariable logistic regression analyses were used to identify predictors of language worsening. Overall survival was assessed using univariable Cox regression. Results: Postoperative language worsening occurred in six patients (16.2%). Increasing age was associated with higher odds of postoperative language worsening in univariable logistic regression (OR 1.12 per year, 95% CI 1.02–1.23, p = 0.019). Due to the limited number of outcome events, multivariable logistic regression was not performed. In survival analysis, increasing age (HR 1.10, 95% CI 1.05–1.16, p < 0.001) and WHO grade 4 (HR 18.15, 95% CI 3.91–84.19, p < 0.001) were associated with shorter overall survival. No statistically significant association between extent of resection and overall survival was detected in this small cohort. Conclusions: Awake glioma surgery with intraoperative mapping was associated with favorable language outcomes in most patients at the 3-month follow-up. Increasing age was associated with postoperative language worsening in univariable analysis. These findings should be interpreted as exploratory because of the limited sample size and low number of outcome events. Larger prospective studies with standardized longitudinal language assessment are needed. Full article

Gliomas constitute the majority of primary brain tumors, and accurate diagnosis through MRI is essential for patient management. Existing computer-aided diagnosis approaches frequently rely on tumor segmentation frameworks. In this study, a segmentation-independent framework for volumetric low-grade versus high-grade glioma (LGG/HGG) classification is proposed using a Convolutional Neural Network (CNN) designed through task-oriented Neural Architecture Search (NAS). The proposed method was evaluated on a multi-center dataset comprising 1194 patients with pre-operative MRI scans, including T1-CE and FLAIR sequences from four publicly available cohorts. NAS was conducted within a controlled search space to optimize a 3D U-Net–based backbone using Tree-structured Parzen Estimator (TPE) combined with Hyperband pruning. The optimized backbone was enhanced with residual connections and Squeeze-and-Excitation (SE) attention mechanisms to improve feature representation and training stability. Internal validation employed repeated 5-fold cross-validation across all four multi-center datasets. An external experiment used REMBRANDT as a test cohort (49 LGG, 19 HGG). The proposed model achieved 88.25% internal accuracy and 75.51% external accuracy (macro-F1: 87.37% internal, 73.77% external), outperforming benchmark 3D CNNs. Explainable Artificial Intelligence (XAI) analysis based on Grad-CAM revealed robust tumor localization without segmentation supervision, validated against available ground-truth masks. Additional experiments demonstrated the model’s generalization capacity, achieving 89.51% accuracy for IDH mutation prediction and 78.74% for multi-grade classification. Full article

Gliomas represent the most prevalent type of brain tumor, with their most aggressive variant, glioblastoma multiforme, associated with high mortality rates. Due to their elevated molecular heterogeneity, accurate classification of gliomas has presented significant challenges. Therefore, considerable effort has been dedicated to identifying relevant biomarkers that improve early diagnosis and unveil new areas for treatment. Advances in high-throughput sequencing technology have enabled public resources such as The Cancer Genome Atlas (TCGA) to provide large-scale data from various cancers, allowing researchers to perform more comprehensive analysis of this disease. In this study, we introduce MOHVAE-B, a comprehensive framework designed for the integration of multi-omics data and biomarker discovery using data from TCGA. MOHVAE-B employs a supervised hierarchical variational autoencoder integrated with SHAP-based interpretability to effectively integrate high-dimensional multi-omics data and extract the most influential features driving the model’s predictions. Subsequently, Bayesian Networks (BNs) are constructed to model conditional dependencies between the selected features, providing insights into their possible relations. Applied to the TCGA glioma cohorts, MOHVAE-B achieved a near-perfect AUC of $0.9993$ and successfully identified high-impact features related to glioma classification. For glioblastoma multiforme, this included six novel candidates: LINC02172, NACA2, LINC01114, HNRNPA1P48, PPIAL4G, and LINC01558. For low-grade gliomas, the model highlighted AMER2 as a promising marker. Across both cohorts, PMP2 stood out as a particularly strong candidate for a potential role in glioma pathogenesis. The constructed BNs provided an additional layer of validation, reinforcing NACA2 as a candidate of interest in glioma biology. Full article

Background/Objectives: Accurate grading of brain gliomas is important, and amide proton transfer-weighted (APTw) MRI shows promise for non-invasive tumor differentiation. This study aimed to perform a comprehensive review and meta-analyses to demonstrate heterogeneity in both the diagnostic accuracy and quantitative consistency of APTw MRI in distinguishing high-grade gliomas (HGGs) from low-grade gliomas (LGGs), highlight issues with reporting standards and identify sources of heterogeneity through meta-regression. Methods: A systematic literature search was conducted between 1 January 2013 and 18 January 2026, following PRISMA guidelines. Peer-reviewed articles in English reporting diagnostic accuracy/contrast values of APTw MRI and study parameters were included. Principal component analysis (PCA) was used to extract the principal components (PCs) of the chemical exchange saturation transfer (CEST) contrast mechanism. Random-effects meta-analyses and univariate meta-regression models using individual CEST parameters and three PCs were performed. Forest plots with pooled estimates were generated. Leave-one-out meta-analysis (LOOMA) and complete case analysis were performed to examine the effects of outliers and missing data, respectively. Results: A total of 31 studies were included. Meta-analyses of the AUC and mean difference demonstrated significant heterogeneity across the studies (I² = 73.9% & 78.2%, p < 0.001). The mean difference was moderated by one SD within the mean of the readout PC (p = 0.034) and the total PC (p = 0.02). The heterogeneity for the AUC and group mean difference was not substantially reduced by moderating nor LOOMA. The results of the meta-regression using all the data were similar to those using only data with no missing parameters. Conclusions: While APTw MRI shows promise for non-invasively distinguishing glioma grades, substantial heterogeneity in the study parameters limits generalizability. To improve consistency and comparability across studies, full reports of imaging parameters and standardization of APTw protocols are essential. Full article

Astrocytes regulate key aspects of the neural microenvironment that can be co-opted by cancer to support tumor growth and invasion. Secreted protein acidic and rich in cysteine-like 1 (SPARCL1) is a matricellular glycoprotein expressed by astrocytes and stromal cells, whose expression varies across cancer types. While SPARCL1 is downregulated in many peripheral cancers, reports of its expression in gliomas, specifically glioblastoma (GBM), are inconsistent. The biological context underlying these divergent findings, and the role of SPARCL1 in GBM malignancy, remains unclear. Publicly available transcriptomic datasets from the Ivy Glioblastoma Atlas Project (Ivy GAP), GlioVis, and TCGA were analyzed to evaluate SPARCL1 expression across GBM cohorts. Spatially resolved gene expression data from Ivy GAP were used to assess SPARCL1 expression from defined tumor regions. Microarray and RNA sequencing datasets from GlioVis and TCGA, respectively, were used to assess SPARCL1 expression across whole-tumor samples. Spatial transcriptomics from Ivy GAP show SPARCL1 expression was upregulated along the leading edge and in infiltrating tumor regions. Microarray datasets showed greater SPARCL1 expression in tumors of astrocyte lineage as opposed to oligodendrocyte lineage. Bulk RNA sequencing showed high SPARCL1 expression in low-grade gliomas, which is consistent with astrocytic lineage, IDH mutation, and spatial averaging effects that might obscure regional associations. These findings demonstrate that SPARCL1 expression in GBM is shaped by tumor architecture, molecular classification, and microenvironment interactions. Enrichment of SPARCl1 at invasive tumor margins is consistent with prior studies linking SPARCL1 to neuron–glioma synapse formation and angiogenesis. Full article

Background: Amyloid-related pathways are well studied in neurodegenerative diseases but remain poorly characterized in gliomas. Amyloid-related transcriptional programs in low-grade gliomas (astrocytoma grade II-III) and oligodendrogliomas, and their association with patient survival, were analyzed in this study. Methods: Transcriptomic data from 193 grade II-III astrocytomas and 191 oligodendrogliomas were analyzed to evaluate histology-specific expression patterns and prognostic significance. Differential and single-sample gene set enrichment analyses (ssGSEA) were used to calculate per-sample enrichment scores for 30 amyloid-related Gene Ontology biological process gene sets across the combined cohort. These scores were used to compare pathway activity between grade II-III astrocytoma and oligodendroglioma samples. Pathway-level survival analyses were performed for each tumor type using ssGSEA enrichment scores to evaluate associations with overall survival. Results: Distinct amyloid-related transcriptional programs were identified between glioma subtypes. Grade II-III astrocytomas showed enrichment of pathways related to amyloid precursor protein (APP) processing and amyloid-β clearance, whereas oligodendrogliomas were enriched in lipid transport and negative regulation of amyloid formation. Survival analyses revealed that higher activity of the positive regulation of APP biosynthetic process and amyloid-β clearance by transcytosis was significantly associated with worse overall survival in grade II-III astrocytoma, but not in oligodendroglioma. Gene-level analyses in astrocytoma demonstrated consistent survival associations across multiple genes within these pathways, supporting coordinated pathway-level effects rather than isolated single-gene prognostic markers. Conclusions: Amyloid-related transcriptional programs differ substantially between diffuse glioma subtypes. Increased APP biosynthesis and amyloid-β transcytosis pathways are associated with poorer survival specifically in grade II-III astrocytoma, suggesting a potential role for amyloid metabolism in tumor progression. These findings identify APP-related pathways as candidates for further mechanistic investigation and potential therapeutic targeting in grade II-III astrocytoma. Full article

Background and Objectives: Cortical plasticity assessment using navigated transcranial magnetic stimulation (nTMS) represents a promising non-invasive strategy for predicting reorganisation of cortical circuits in neuro-oncological patients. This study examined how glioma grade influences cognitive network reorganisation by multiparametric analysis. Materials and Methods: We conducted a prospective monocentric study at the Neurosurgical Department in Florence and a comparative analysis of motor (M), language (Ln), calculation (C), and visuo-spatial functions (VS) between patients with low-grade gliomas (LGGs) and high-grade gliomas (HGGs) undergoing pre- and postoperative nTMS mapping (at 5 ± 2, 30 ± 10, and 90 ± 10 days of follow-up). Results: Between January2024 and September 2025, we enrolled 69 patients, and the total number of nTMS mapping procedures was 70: one relapse, (M:F = 345:365), level of scholarship 8–15 years, 21 LGGs, 30 HGGs, 19 non-glial lesions (excluded), left lesions n = 37, right lesions n = 31, bilateral n = 2, bi-hemispheric nTMS = 80%. Considering LGGs and HGGs, the major motor function displacement was obtained in the right hemisphere (Rh; predominantly for HGGs 64 mm vs. LGGs 39 mm), with more restrained displacement in the left hemisphere (Lh; LGGs 20 mm vs. HGGs 21 mm). For Ln, displacement was higher for HGGs (57 mm vs. LGGs 31 mm). However, surprisingly for HGGs in the Lh, the displacement was more significant (60 mm), whereas for LGGs it was major in the Rh (~80 mm). For C, displacement for HGGs was 72 mm Lh vs. 48.11 mm Rh, and for LGGs 50 mm Lh vs. 41 mm Rh. Insufficient data were obtained for the network. Qualitative analyses further characterised this reorganisation: motor f. demonstrated reshaping around the primary motor cortex; linguistic f. displaced from temporo-parietal areas to the inferior frontal gyrus; calculation and VS functions reorganised within frontoparietal circuits. The correlation between cognitive results and BPI revealed that higher BPI values were associated with prolonged recovery periods. Nevertheless, functional recovery was achieved in up to 90% of patients across all assessed functions. Conclusions: We propose non-invasively measuring cortical plasticity across different cognitive domains with a quantitative–qualitative framework for assessing functional reorganisation with a multimodal assessment in glioma patients. Full article

Objective: Optic pathway glioma (OPG) diagnosed in adults represents a rare and understudied tumor subtype. While pediatric OPGs are typically benign and associated with NF1 and RAS/MAPK pathway dysregulation, less is known about the clinical characteristics and prognostic drivers of OPGs in adults. Methods: A systematic review was conducted in accordance with PRISMA guidelines across multiple databases. Studies reporting patient-level data and follow-up for patients diagnosed with optic pathway glioma at age ≥ 18 years were included. Results: Ninety-six studies comprising 149 adult patients were analyzed. Median patient age was 47 years (range: 18–90), and 51.0% of tumors were high-grade (WHO grade 3–4). Increasing age at diagnosis was significantly correlated with higher WHO grade (ρ = 0.600, p < 0.001), and optic tract involvement was associated with high-grade disease (χ² = 8.08, p = 0.004; ϕ = 0.26). Median follow-up was 12 months, with 74 patients alive and 75 deceased at last follow-up. WHO grade was strongly associated with overall survival (log-rank p < 0.0001), with 24-month survival ranging from 96.9% for grade 1 tumors to 11.3% for grade 4 tumors. Compared with observation or steroid-only management, both surgical and non-surgical oncologic treatments were associated with longer observed survival, although no significant difference was observed between active treatment modalities. Conclusions: Optic pathway gliomas in adults exhibit a multimodal biologic distribution, encompassing both indolent low-grade tumors and aggressive high-grade malignancies. Survival outcomes appear to be primarily driven by tumor biology, with age and anatomic involvement correlating with tumor grade. Prospective, multicenter studies with comprehensive molecular profiling are needed to refine prognostic stratification and guide evidence-based management of this rare disease. Full article

Background: Glioblastoma (GBM) is the most common and aggressive brain tumor in adults. Changes in the ubiquitination system in GBM cells can promote uncontrolled tumor growth and reduce the effectiveness of treatments. However, the exact targets and regulatory elements of the ubiquitin–proteasome system involved in GBM are still not well understood. Methods: All data were obtained by using in silico analysis, immunohistochemistry, Western blot, RT-qPCR, gene silencing and proliferation assay. Results: Computational and protein analyses show that aggressive gliomas have higher expression of the RING ligase RNF182, with significantly greater levels in glioblastoma (GBM) than in low-grade gliomas. Elevated RNF182 is strongly associated with GBM growth. Experiments using siRNA to inhibit RNF182 in the human glioblastoma cell line U87MG significantly reduced cell proliferation, suggesting that RNF182 promotes tumor growth and may be a potential therapeutic target. Conclusions: These findings create a connection between the ubiquitin–proteasome system and the unchecked growth observed in GBM, identifying RNF182 as a new marker associated with GBM proliferation and an additional target for GBM treatment. Full article

Gliomas are the most common type of primary brain tumors in adults, with a high level of recurrence and mortality. Their complex biology and adaptive resistance mechanisms pose major obstacles to existing treatment strategies. Non-coding RNAs (ncRNAs), particularly microRNAs (miRNAs), are crucial in tumor development and progression. Small RNA sequencing technology was performed in 25 patients with high-grade gliomas (HGGs) to analyze ncRNA expression in gliomas compared to normal adjacent tissues (NATs) aiming to elucidate their possible roles in these malignancies. Samples from patients with gliomas were examined, revealing an overall upregulation of ncRNAs. Specific ncRNA classes, including miRNAs, transfer RNAs (tRNAs), Piwi-interacting RNAs (piRNAs), and small nucleolar RNAs (snoRNAs) showed notable shifts in abundance between tumor and normal samples. Among the upregulated miRNAs, a set of top five, such as miR-21, miR-221, miR-1321, miR-1306-5p, and miR-374a-5p, were validated by real-time quantitative PCR (RT-qPCR) in a cohort of 17 low-grade gliomas (LGGs) and 52 HGGs. These miRNAs are associated with critical oncogenic pathways and correlated with a worse prognosis. This study expanded the understanding of glioma biology and further confirmed the role of ncRNAs in the pathogenesis, supporting their potential use as novel possible biomarkers or therapeutic targets. Moreover, it provided an integrated analysis of multiple ncRNA classes, offering validation across both LGG and HGG, and uniquely incorporating a Kurdish cohort. Full article

Background: Non-invasive approaches to brain tumour detection and diagnosis are limited by the absence of clinically validated circulating biomarkers. This study utilised a miniaturised tissue perfusion model to maintain human brain tumour tissue ex vivo with the aim of identifying tissue-derived proteins with potential biomarker utility. Methods: 55 tumour samples from 11 different brain tumours (glioblastoma n = 4, low-grade glioma n = 4, brain metastases n = 3) were micro-dissected and maintained ex vivo on a continuous-flow perfusion device for 168 h. Proteomic analysis of tumour effluent was performed by reversed-phase capillary liquid chromatography-mass spectrometry. Two candidate proteins—extracellular matrix protein 1 (ECM1) and cathepsin D—were quantified using ELISA. Results: All tumour subtypes retained tissue viability over 168 h of perfusion. Proteomic profiling identified 90 tissue-derived proteins in the tumour effluent. Many proteins corresponded to previously described cancer biomarkers such as glial fibrillary acidic protein (GFAP) while others, including Serpin A12 and collapsin response mediator protein-2 (CRMP2), had not yet been described in a brain tumour context. ELISA confirmed significantly higher ECM1 levels in high-grade glioma effluent compared with low-grade glioma (p = 0.0407), whereas cathepsin D levels did not differ significantly between tumour types. Conclusions: The ex vivo perfusion model effectively preserved primary and metastatic human brain tumour tissue and enabled direct characterisation of tumour-secreted proteins. The proteins identified here warrant further validation as tumour biomarkers in patient serum or cerebrospinal fluid. Full article

Background: Difficult benign intracranial tumors (including meningiomas, schwannomas, neurofibromatosis-related tumors, and pituitary neuroendocrine tumors) have substantial morbidity in patients. Due to their limited treatment options, there is a need for individualized treatment beyond histological and surgical approaches. Objective: To summarize how novel treatment innovations have been implemented for these tumors, meningiomas and schwannomas are prioritized, followed by NF-associated neoplasms, and then pituitary neuroendocrine tumors in comparison to low-grade gliomas. Methods: We summarize the current knowledge relating to targeted therapies for gliomas, meningiomas, schwannomas, neurofibromatosis (NF) tumors, and pituitary neuroendocrine tumors to investigate an individual’s treatment options for difficult benign brain tumors. This review synthesizes evidence on tumor genomics and molecular markers, supported by methylation-based classification, immunohistochemistry, and functional assays, emphasizing current clinical applications. Evidence Synthesis: The recent data show that DNA methylation-based models can predict post-surgical outcomes and radiotherapy responses, enabling risk stratification and radiotherapy benefit prediction. Early signals support target-directed treatment, including cMET blockade that radiosensitizes NF2 schwannoma models, brigatinib-associated tumor shrinkage in NF2-deficient models, and PitNET organoid data. Conclusions: We support clinical decision-making that utilizes molecular profiling with functional testing to guide targeted treatment. We also identify evidence gaps such as biomarker-defined prospective trials that are needed for broader clinical implementation. Full article

The underlying metabolism of tumor cells in gliomas has become an area of focus secondary to the difficulties in diagnosis and treatment of these tumors. Heterogeneity in both molecular and phenotypic features of tumor cells in pediatric and adult gliomas presents a significant barrier to traditional treatment options such as radiotherapy and chemotherapy. Low-grade gliomas in pediatric and adult populations have relatively high survival rates, while high-grade gliomas have no effective treatments. Recent advancements in metabolomic techniques have uncovered key metabolic abnormalities, such as increased glutamine and creatinine in invasive edge cells and increased purines in viable tumor cells, distinguishing tumor cells in gliomas. Spatial metabolic heterogeneity and metabolic plasticity enable gliomas to adapt to diverse microenvironments and oxidative stress, necessitating precision medicine approaches that target subtype-specific metabolic vulnerabilities. Further, gliomas are characterized by high intratumoral heterogeneity, with metabolic distinctions between core, edge, viable, and necrotic regions. Altered metabolism of tumor cells has an impact on cells within the tumor microenvironment, resulting in a dysfunctional phenotypic state in resident cells. These metabolic abnormalities differentiate tumor cells from the surrounding microenvironment. Enhanced understanding of the metabolic abnormalities in gliomas could inform targeted therapies, increasing therapeutic response in patients. This review synthesizes emerging evidence on intratumoral and intertumoral heterogeneity in gliomas, highlights the role of tumor-immune cell crosstalk in shaping the metabolic landscape, and discusses how these vulnerabilities may be exploited to develop novel therapies. Full article

Glial brain tumours, including astrocytoma IDH (Isocitrate Dehydrogenase) mutant and glioblastoma IDH wild-type, are highly malignant brain tumours with poor clinical outcomes. Genomic instability, encompassing microsatellite (MIN) and chromosomal instability (CIN), drives tumour heterogeneity and evolution. In this study, genomic instability was analysed in 85 patients using AP-PCR (Arbitrarily Primed Polymerase Chain Reaction) by comparing tumour and normal tissue (blood) DNA profiles of the same patient. Both types of alterations were present in all analysed samples, contributing almost equally to the total level of genomic instability. The dominant pattern of genomic instability in our cohort was low overall instability, predominantly manifesting as low-degree microsatellite instability. A general decrease in genomic instability was observed with increasing tumour grade. Glioblastoma IDH wild-type was more prevalent in older patients, whereas astrocytoma IDH mutant predominated in younger individuals. Notably, low genomic instability (both MIN and CIN) was associated with poorer survival in patients over 50 years of age. Females, compared to males, exhibited higher MIN in grade 2 tumours and elevated CIN in grade 4 tumours. Our results confirm that genomic instability contributes to tumour progression, MIN being the pivotal factor, and could serve as a prognostic biomarker in malignant gliomas. Full article