Potential Diagnostic and Clinical Significance of Selected Genetic Alterations in Glioblastoma.

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.

Automatic brain-tumor diagnosis using cascaded deep convolutional neural networks with symmetric U-Net and asymmetric residual-blocks.

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.

Diagnostic and Prognostic Value of Circulating DNA Fragments in Glioblastoma Multiforme Patients.

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.

The development of a custom RNA-sequencing panel for the identification of predictive and diagnostic biomarkers in glioma.

PURPOSE: Various molecular profiles are needed to classify malignant brain tumors, including gliomas, based on the latest classification criteria of the World Health Organization, and their poor prognosis necessitates new therapeutic targets. The Todai OncoPanel 2 RNA Panel (TOP2-RNA) is a custom-target RNA-sequencing (RNA-seq) using the junction capture method to maximize the sensitivity of detecting 455 fusion gene transcripts and analyze the expression profiles of 1,390 genes. This study aimed to classify gliomas and identify their molecular targets using TOP2-RNA. METHODS: A total of 124 frozen samples of malignant gliomas were subjected to TOP2-RNA for classification based on their molecular profiles and the identification of molecular targets. RESULTS: Among 55 glioblastoma cases, gene fusions were detected in 11 cases (20%), including novel MET fusions. Seven tyrosine kinase genes were found to be overexpressed in 15 cases (27.3%). In contrast to isocitrate dehydrogenase (IDH) wild-type glioblastoma, IDH-mutant tumors, including astrocytomas and oligodendrogliomas, barely harbor fusion genes or gene overexpression. Of the 34 overexpressed tyrosine kinase genes, MDM2 and CDK4 in glioblastoma, 22 copy number amplifications (64.7%) were observed. When comparing astrocytomas and oligodendrogliomas in gene set enrichment analysis, the gene sets related to 1p36 and 19q were highly enriched in astrocytomas, suggesting that regional genomic DNA copy number alterations can be evaluated by gene expression analysis. CONCLUSIONS: TOP2-RNA is a highly sensitive assay for detecting fusion genes, exon skipping, and aberrant gene expression. Alterations in targetable driver genes were identified in more than 50% of glioblastoma. Molecular profiling by TOP2-RNA provides ample predictive, prognostic, and diagnostic biomarkers that may not be identified by conventional assays and, therefore, is expected to increase treatment options for individual patients with glioma.

FT-Raman spectra in combination with machine learning and multivariate analyses as a diagnostic tool in brain tumors.

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.

MicroRNA biosensors for detection of glioblastoma.

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.

5-aminolevulinic enhanced brain lesions mimic glioblastoma: A case report and literature review.

RATIONALE: Glioblastoma multiforme (GBM) is a highly malignant primary brain tumor for which maximal tumor resection plays an important role in the treatment strategy. 5-aminolevulinic (5-ALA) is a powerful tool in fluorescence-guided surgery for GBM. However, 5-ALA- enhancing lesion can also be observed with different etiologies. PATIENTS CONCERNS: Three cases of 5-ALA-enhancing lesions with etiologies different from glioma. DIAGNOSES: The final diagnosis was abscess in 1 patient and diffuse large B-cell in the other 2 patients. INTERVENTIONS: Three patients received 5-aminolevulinic acid-guided tumor resection under microscope with intraoperative neuromonitoring. OUTCOMES: All of our patients showed improvement or stable neurological function outcomes. The final pathology revealed etiologies different from GBM. LESSONS: The 5-aminolevulinic acid fluorescence-guided surgery has demonstrated its maximal extent of resection and safety profile in patients with high-grade glioma. Non-glioma etiologies may also mimic GBM in 5-ALA-guided surgeries. Therefore, patient history taking and consideration of brain images are necessary for the interpretation of 5-ALA-enhanced lesions.

Survival prediction of glioblastoma patients using modern deep learning and machine learning techniques.

In this study, we utilized data from the Surveillance, Epidemiology, and End Results (SEER) database to predict the glioblastoma patients' survival outcomes. To assess dataset skewness and detect feature importance, we applied Pearson's second coefficient test of skewness and the Ordinary Least Squares method, respectively. Using two sampling strategies, holdout and five-fold cross-validation, we developed five machine learning (ML) models alongside a feed-forward deep neural network (DNN) for the multiclass classification and regression prediction of glioblastoma patient survival. After balancing the classification and regression datasets, we obtained 46,340 and 28,573 samples, respectively. Shapley additive explanations (SHAP) were then used to explain the decision-making process of the best model. In both classification and regression tasks, as well as across holdout and cross-validation sampling strategies, the DNN consistently outperformed the ML models. Notably, the accuracy were 90.25% and 90.22% for holdout and five-fold cross-validation, respectively, while the corresponding R2 values were 0.6565 and 0.6622. SHAP analysis revealed the importance of age at diagnosis as the most influential feature in the DNN's survival predictions. These findings suggest that the DNN holds promise as a practical auxiliary tool for clinicians, aiding them in optimal decision-making concerning the treatment and care trajectories for glioblastoma patients.

Is simultaneous occurrence of meningioma and glioblastoma a mere coincidence?

BACKGROUND: It is extremely unusual for multiple tumors to arise from different cell types and occur at the same time inside the brain. It is still unknown whether or not the coexistence of meningioma and glioblastoma is connected in any way or if their simultaneous appearance is merely a coincidence. OBJECTIVE: We conduct a comprehensive literature review on cases of concurrent meningioma and glioblastoma occurrence to elucidate the underlying concepts that may constitute this coexistence. METHODS: We searched for articles on the topic of glioblastoma coexisting with meningioma in Google Scholar, PubMed, and Scopus. First, the initial literature searches were conducted for study selection and the data collection processes. After evaluating the title and abstract, the papers were selected. RESULTS: We analyzed 21 studies describing 23 patients who had both glioblastoma and meningioma. There were ten male patients (47.6 %) and thirteen female patients (61.9 %). The mean age of patients at diagnosis was 61 years old (the range 30 to 86). In 17 cases, both tumors were in the same hemisphere (80.9 %). In 5 cases, they were in the other hemisphere (23.8 %), and in one case, the glioblastoma was in the left hemisphere and the olfactory meningioma was In 5 cases, they were in the other hemisphere (23.8 %), and in one case, the glioblastoma was in the left hemisphere and the olfactory meningioma was in the anterior cranial fossa. In 61.9 % of cases, headache was the predominant symptom. CONCLUSION: Understanding the unique challenges posed by the coexistence of glioblastoma and meningioma is crucial for developing effective treatment strategies. Further investigation into the underlying molecular mechanisms and genetic factors involved in this rare occurrence could pave the way for personalized therapies tailored to each patient's specific needs.

Exosomal long non-coding RNAs in glioblastoma.

Glioblastoma multiforme (GBM) is the most prevalent primary tumor found in the central nervous system, accounting for 70% of all adult brain tumors. The median overall survival rate is one year post-diagnosis with treatment, and only four months without treatment. Current GBM diagnostic methods, such as magnetic resonance imaging (MRI), surgery, and brain biopsies, have limitations. These include difficulty distinguishing between tumor recurrence and post-surgical necrotic regions, and operative risks associated with obtaining histological samples through direct surgery or biopsies. Consequently, there is a need for rapid, inexpensive, and minimally invasive techniques for early diagnosis and improved subsequent treatment. Research has shown that tumor-derived exosomes containing various long non-coding RNAs (lncRNAs) play critical regulatory roles in immunomodulation, cancer metastasis, cancer development, and drug resistance in GBM. They regulate genes that enhance cancer growth and progression and alter the expression of several key signaling pathways. Due to the specificity and sensitivity of exosomal lncRNAs, they have the potential to be used as biomarkers for early diagnosis and prognosis, as well as to monitor a patient's response to chemotherapy for GBM. In this review, we discuss the role of exosomal lncRNAs in the pathogenesis of GBM and their potential clinical applications for early diagnosis.

A Retrospective Genomic Landscape of 661 Young Adult Glioblastomas Diagnosed Using 2016 WHO Guidelines for Central Nervous System Tumors.

The authors present a cohort of 661 young adult glioblastomas diagnosed using 2016 WHO World Health Organization Classification of Tumors of the Central Nervous System, utilizing comprehensive genomic profiling (CGP) to explore their genomic landscape and assess their relationship to currently defined disease entities. This analysis explored variants with evidence of pathogenic function, common copy number variants (CNVs), and several novel fusion events not described in literature. Tumor mutational burden (TMB) mutational signatures, anatomic location, and tumor recurrence are further explored. Using data collected from CGP, unsupervised machine-learning techniques were leveraged to identify 10 genomic classes in previously assigned young adult glioblastomas. The authors relate these molecular classes to current World Health Organization guidelines and reference current literature to give therapeutic and prognostic descriptions where possible.

Extent of Resection and Outcomes of Patients with Primary Malignant Brain Tumors.

OPINION STATEMENT: We have level II evidence that attempting a gross total resection of newly diagnosed suspected glioblastoma is preferred when a maximally safe resection can be attempted. This recommendation extends to elderly patients and those with butterfly gliomas. However, in cases where patients are poor surgical candidates, or for lesions in eloquent areas, subtotal resection or biopsy may be indicated. Recent studies have discussed "supramaximal surgery," which is defined in different ways by different teams, but there is not enough evidence, yet, to make a consistent recommendation for supramaximal resection for specific patients.

Comparative Analysis of Amino Acid Profiles in Patients with Glioblastoma and Meningioma Using Liquid Chromatography Electrospray Ionization Tandem Mass Spectrometry (LC-ESI-MS/MS).

Brain tumors account for 1% of all cancers diagnosed de novo. Due to the specificity of the anatomical area in which they grow, they can cause significant neurological disorders and lead to poor functional status and disability. Regardless of the results of biochemical markers of intracranial neoplasms, they are currently of no diagnostic significance. The aim of the study was to use LC-ESI-MS/MS in conjunction with multivariate statistical analyses to examine changes in amino acid metabolic profiles between patients with glioblastoma, meningioma, and a group of patients treated for osteoarthritis of the spine as a control group. Comparative analysis of amino acids between patients with glioblastoma, meningioma, and the control group allowed for the identification of statistically significant differences in the amino acid profile, including both exogenous and endogenous amino acids. The amino acids that showed statistically significant differences (lysine, histidine, α-aminoadipic acid, phenylalanine) were evaluated for diagnostic usefulness based on the ROC curve. The best results were obtained for phenylalanine. Classification trees were used to build a model allowing for the correct classification of patients into the study group (patients with glioblastoma multiforme) and the control group, in which cysteine turned out to be the most important amino acid in the decision-making algorithm. Our results indicate amino acids that may prove valuable, used alone or in combination, toward improving the diagnosis of patients with glioma and meningioma. To better assess the potential utility of these markers, their performance requires further validation in a larger cohort of samples.

Identification of circulating miRNA as early diagnostic molecular markers in malignant glioblastoma base on decision tree joint scoring algorithm.

PURPOSE: The lack of clinical markers prevents early diagnosis of glioblastoma (GBM). Many studies have found that circulating microRNAs (miRNAs) can be used as early diagnostic markers of malignant tumours. Therefore, the identification of novel circulating miRNA biomolecular markers could be beneficial to clinicians in the early diagnosis of GBM. METHODS: We developed a decision tree joint scoring algorithm (DTSA), systematically integrating significance analysis of microarray (SAM), Pearson hierarchical clustering, T test, Decision tree and Entropy weight score algorithm, to screen out circulating miRNA molecular markers with high sensitivity and accuracy for early diagnosis of GBM. RESULTS: DTSA was developed and applied for GBM datasets and three circulating miRNA molecular markers were identified, namely, hsa-miR-2278, hsa-miR-555 and hsa-miR-892b. We have found that hsa-miR-2278 and hsa-miR-892b regulate the GBM pathway through target genes, promoting the development of GBM and affecting the survival of patients. DTSA has better classification effect in all data sets than other classification algorithms, and identified miRNAs are better than existing markers of GBM. CONCLUSION: These results suggest that DTSA can effectively identify circulating miRNA, thus contributing to the early diagnosis and personalised treatment of GBM.

Engineering a tunable micropattern-array assay to sort single extracellular vesicles and particles to detect RNA and protein in situ.

The molecular heterogeneity of extracellular vesicles (EVs) and the co-isolation of physically similar particles, such as lipoproteins (LPs), confounds and limits the sensitivity of EV bulk biomarker characterization. Herein, we present a single-EV and particle (siEVP) protein and RNA assay (siEVP PRA) to simultaneously detect mRNAs, miRNAs, and proteins in subpopulations of EVs and LPs. The siEVP PRA immobilizes and sorts particles via positive immunoselection onto micropatterns and focuses biomolecular signals in situ. By detecting EVPs at a single-particle resolution, the siEVP PRA outperformed the sensitivities of bulk-analysis benchmark assays for RNA and protein. To assess the specificity of RNA detection in complex biofluids, EVs from various glioma cell lines were processed with small RNA sequencing, whereby two mRNAs and two miRNAs associated with glioblastoma multiforme (GBM) were chosen for cross-validation. Despite the presence of single-EV-LP co-isolates in serum, the siEVP PRA detected GBM-associated vesicular RNA profiles in GBM patient siEVPs. The siEVP PRA effectively examines intravesicular, intervesicular, and interparticle heterogeneity with diagnostic promise.

Self-Functionalized Superlattice Nanosensor Enables Glioblastoma Diagnosis Using Liquid Biopsy.

Glioblastoma (GBM), the most aggressive and lethal brain cancer, is detected only in the advanced stage, resulting in a median survival rate of 15 months. Therefore, there is an urgent need to establish GBM diagnosis tools to identify the tumor accurately. The clinical relevance of the current liquid biopsy techniques for GBM diagnosis remains mostly undetermined, owing to the challenges posed by the blood-brain barrier (BBB) that restricts biomarkers entering the circulation, resulting in the unavailability of clinically validated circulating GBM markers. GBM-specific liquid biopsy for diagnosis and prognosis of GBM has not yet been developed. Here, we introduce extracellular vesicles of GBM cancer stem cells (GBM CSC-EVs) as a previously unattempted, stand-alone GBM diagnosis modality. As GBM CSCs are fundamental building blocks of tumor initiation and recurrence, it is desirable to investigate these reliable signals of malignancy in circulation for accurate GBM diagnosis. So far, there are no clinically validated circulating biomarkers available for GBM. Therefore, a marker-free approach was essential since conventional liquid biopsy relying on isolation methodology was not viable. Additionally, a mechanism capable of trace-level detection was crucial to detecting the rare GBM CSC-EVs from the complex environment in circulation. To break these barriers, we applied an ultrasensitive superlattice sensor, self-functionalized for surface-enhanced Raman scattering (SERS), to obtain holistic molecular profiling of GBM CSC-EVs with a marker-free approach. The superlattice sensor exhibited substantial SERS enhancement and ultralow limit of detection (LOD of attomolar 10-18 M concentration) essential for trace-level detection of invisible GBM CSC-EVs directly from patient serum (without isolation). We detected as low as 5 EVs in 5 µL of solution, achieving the lowest LOD compared to existing SERS-based studies. We have experimentally demonstrated the crucial role of the signals of GBM CSC-EVs in the precise detection of glioblastoma. This was evident from the unique molecular profiles of GBM CSC-EVs demonstrating significant variation compared to noncancer EVs and EVs of GBM cancer cells, thus adding more clarity to the current understanding of GBM CSC-EVs. Preliminary validation of our approach was undertaken with a small amount of peripheral blood (5 µL) derived from GBM patients with 100% sensitivity and 97% specificity. Identification of the signals of GBM CSC-EV in clinical sera specimens demonstrated that our technology could be used for accurate GBM detection. Our technology has the potential to improve GBM liquid biopsy, including real-time surveillance of GBM evolution in patients upon clinical validation. This demonstration of liquid biopsy with GBM CSC-EV provides an opportunity to introduce a paradigm potentially impacting the current landscape of GBM diagnosis.

Laser Interstitial Thermal Therapy Induces Robust Local Immune Response for Newly Diagnosed Glioblastoma With Long-term Survival and Disease Control.

Laser interstitial thermal therapy (LITT) is a minimally invasive neurosurgical technique used to ablate intra-axial brain tumors. The impact of LITT on the tumor microenvironment is scarcely reported. Nonablative LITT-induced hyperthermia (33-43˚C) increases intra-tumoral mutational burden and neoantigen production, promoting immunogenic cell death. To understand the local immune response post-LITT, we performed longitudinal molecular profiling in a newly diagnosed glioblastoma and conducted a systematic review of anti-tumoral immune responses after LITT. A 51-year-old male presented after a fall with progressive dizziness, ataxia, and worsening headaches with a small, frontal ring-enhancing lesion. After clinical and radiographic progression, the patient underwent stereotactic needle biopsy, confirming an IDH-WT World Health Organization Grade IV Glioblastoma, followed by LITT. The patient was subsequently started on adjuvant temozolomide, and 60 Gy fractionated radiotherapy to the post-LITT tumor volume. After 3 months, surgical debulking was conducted due to perilesional vasogenic edema and cognitive decline, with H&E staining demonstrating perivascular lymphocytic infiltration. Postoperative serial imaging over 3 years showed no evidence of tumor recurrence. The patient is currently alive 9 years after diagnosis. Multiplex immunofluorescence imaging of pre-LITT and post-LITT biopsies showed increased CD8 and activated macrophage infiltration and programmed death ligand 1 expression. This is the first depiction of the in-situ immune response to LITT and the first human clinical presentation of increased CD8 infiltration and programmed death ligand 1 expression in post-LITT tissue. Our findings point to LITT as a treatment approach with the potential for long-term delay of recurrence and improving response to immunotherapy.

Low Expression of A3C and PLP2 Indicating a Favorable Prognosis in Human Gliomas.

The roles of apolipoprotein B mRNA-editing enzyme catalytic polypeptide-like 3C (A3C) in various human malignancies are not consistent. A3C expression is correlated with early-stage breast cancer and is presented as a good prognostic factor; however, it induces fewer therapeutic effects of cytotoxic drugs in low-grade gliomas. To explore the impact of A3C on gliomas, a statistical analysis of several public databases was conducted. The results showed that enhanced A3C expression was associated with advanced tumor grades and poor expression of prognostic factors. Similarly, our in vitro study revealed that glioblastoma (GBM) cell lines had higher A3C mRNA and protein expression than that of normal brain tissue cDNA and lysates. We first performed an immunohistochemical stain (IHC) to prove that gliomas with high A3C expression presented the wild type-Isocitrate dehydrogenase 1 (IDH1), and they had an unfavorable prognosis in human glioma tissues. In addition, the oncological factors associated with A3C expression suggested that DNA repair pathways are important mechanisms for inducing tumorigenesis and chemoresistance in gliomas. Moreover, a significant correlation was observed between A3C expression and proteolipid protein 2  (PLP2). Reactive oxygen species (ROS) -activated PLP2 prevents DNA damage-induced cell apoptosis. Compared to high immunostaining scores for A3C and/or PLP2 expression, combined low immunostaining scores for A3C and PLP2 correlated with improved survival in gliomas; however, the detailed mechanism is to be elucidated. In conclusion, our results not only confirmed A3C played an important role in glioma development, but the A3C IHC test could successfully predict the therapeutic effects and disease prognosis.