Grade Gliomas Research Articles

Effect of Intraoperative Opioid Dose on Perioperative Neutrophil-to-Lymphocyte Ratio and Lymphocyte-to-Monocyte Ratio in Glioma.

The neutrophil-to-lymphocyte ratio (NLR) and lymphocyte-to-monocyte ratio (LMR) are inflammatory biomarkers. Until now, it is unknown the impact of opioid dosage on perioperative immunity in glioma patients. The aim of this study was to explore the effect of intraoperative opioid dosage on perioperative immune perturbations using NLR and LMR as inflammatory biomarkers and evaluate the correlation between inflammatory biomarkers and pathological grade of glioma. The study included 208 patients with primary glioma who underwent glioma resection from February 2012 to November 2019 at Harbin Medical University Cancer Hospital. Complete blood count (CBC) was collected at 3 time points: one week before surgery, and 24 hours and one week after surgery. Patients were divided into high-dose and low-dose groups, based on the median value of intraoperative opioid dose. The relationships between perioperative NLR, LMR and intraoperative opioid dosage were analyzed using repeated measurement analysis of variance (ANOVA). Correlations between preoperative various factors and pathological grade were analyzed by Spearman analysis. Receiver operating characteristic (ROC) curves were performed to assess the predictive performance of the NLR and LMR for pathological grade. The NLR (P=0.020) and lower LMR (P=0.037) were statistically significant different between high-dose and low-dose groups one week after surgery. The area under the curve (AUC) of the NLR to identify poor diagnosis was 0.685, which was superior to the LMR (AUC: 0.607) and indicated a correlation between the NLR with pathological grade. The preoperative NLR (P=0.000), LMR (P=0.009), age (P=0.000) and tumor size (P=0.001) exhibited a significant correlation with the pathological grade of glioma. Intraoperative opioids in the high-dose group were associated with higher NLR and lower LMR in postoperative glioma patients. The preoperative NLR and LMR demonstrated predictive value for distinguishing between high-grade and low-grade gliomas.

1243 FOXM1*3 Overexpression Correlates to Staging of Astrocytic Glioma

INTRODUCTION: Diffuse gliomas are a group of primary brain tumors characterized by the presence of neoplastic glial-lineage cells. WHO 2021 classification of gliomas recognizes the mutational status of the isocitrate dehydrogenase-1(IDH-1,G395A) gene and the 1p/19q codeletion as identification of cell lineage and a prognosis marker. Transcription factors are binding proteins of genomic DNA and can activate or suppress gene expression. Forkhead box M1(FOXM1) is a transcription factor for Forkhead box(Fox) proteins, a superfamily which is defined by a conserved winged helix DNA binding domain. FOXM1 is a key regulator of both G1/S and G2/M phases of cell cycle and mitotic spindle integrity, it also plays an important role in angiogenesis, DNA damage repair and tissue regeneration. The human FOXM1 gene consists of 10 exons, two of them alternatively spliced. This splicing gives rise to three isoforms: FOXM1*1,*2 and *3. FOXM1*3 is the smallest isoform and is overexpressed in proliferative tissue such as malignant tumors. METHODS: A control group of 10 brain cortex, 22 A2, 13 O2, 11 A3, 3 O3, 6 A4 and 46 GBM with histological and molecular diagnosis of astrocytoma, oligodendroglioma and glioblastoma from 2011 to 2022 were included. The diagnosis correlated with patient outcome. mRNA FOXM1*3 and its housekeeping gen IPO8 was quantified by qPCR with Taqman probes. Data analysis was conducted by MannWhitney U test. RESULTS: There was a statistically significant difference between expression of mRNA FOXM1*3 in normal brain tissue compared to tumor tissue (p < 0.001). The transcript allowed differentiation between grade 2, 3 and 4 astrocytomas (p < 0.005), nevertheless there was no difference between astrocytic versus oligodendrocytic lineage, and grade 4 astrocytoma versus glioblastoma. CONCLUSIONS: The normalized expression of transcript FOXM1*3 allows differentiation of astrocytic gliomas grades.

Modular Hub Genes in DNA Microarray Suggest Potential Signaling Pathway Interconnectivity in Various Glioma Grades.

Gliomas have displayed significant challenges in oncology due to their high degree of invasiveness, recurrence, and resistance to treatment strategies. In this work, the key hub genes mainly associated with different grades of glioma, which were represented by pilocytic astrocytoma (PA), oligodendroglioma (OG), anaplastic astrocytoma (AA), and glioblastoma multiforme (GBM), were identified through weighted gene co-expression network analysis (WGCNA) of microarray datasets retrieved from the Gene Expression Omnibus (GEO) database. Through this, four highly correlated modules were observed to be present across the PA (GSE50161), OG (GSE4290), AA (GSE43378), and GBM (GSE36245) datasets. The functional annotation and pathway enrichment analysis done through the Database for Annotation, Visualization, and Integrated Discovery (DAVID) showed that the modules and hub genes identified were mainly involved in signal transduction, transcription regulation, and protein binding, which collectively deregulate several signaling pathways, mainly PI3K/Akt and metabolic pathways. The involvement of several hub genes primarily linked to other signaling pathways, including the cAMP, MAPK/ERK, Wnt/β-catenin, and calcium signaling pathways, indicates potential interconnectivity and influence on the PI3K/Akt pathway and, subsequently, glioma severity. The Drug Repurposing Encyclopedia (DRE) was used to screen for potential drugs based on the up- and downregulated hub genes, wherein the synthetic progestin hormones norgestimate and ethisterone were the top drug candidates. This shows the potential neuroprotective effect of progesterone against glioma due to its influence on EGFR expression and other signaling pathways. Aside from these, several experimental and approved drug candidates were also identified, which include an adrenergic receptor antagonist, a PPAR-γ receptor agonist, a CDK inhibitor, a sodium channel blocker, a bradykinin receptor antagonist, and a dopamine receptor agonist, which further highlights the gene network as a potential therapeutic avenue for glioma.

Transcranial Magnetic Stimulation-Based Machine Learning Prediction of Tumor Grading in Motor-Eloquent Gliomas.

Navigated transcranial magnetic stimulation (nTMS) is a well-established preoperative mapping tool for motor-eloquent glioma surgery. Machine learning (ML) and nTMS may improve clinical outcome prediction and histological correlation. This was a retrospective cohort study of patients who underwent surgery for motor-eloquent gliomas between 2018 and 2022. Ten healthy subjects were included. Preoperative nTMS-derived variables were collected: resting motor threshold (RMT), interhemispheric RMT ratio (iRMTr)-abnormal if above 10%-and cortical excitability score-number of abnormal iRMTrs. World Health Organization (WHO) grade and molecular profile were collected to characterize each tumor. ML models were fitted to the data after statistical feature selection to predict tumor grade. A total of 177 patients were recruited: WHO grade 2-32 patients, WHO grade 3-65 patients, and WHO grade 4-80 patients. For the upper limb, abnormal iRMTr were identified in 22.7% of WHO grade 2, 62.5% of WHO grade 3, and 75.4% of WHO grade 4 patients. For the lower limb, iRMTr was abnormal in 23.1% of WHO grade 2, 67.6% of WHO grade 3%, and 63.6% of WHO grade 4 patients. Cortical excitability score ( P = .04) was statistically significantly related with WHO grading. Using these variables as predictors, the ML model had an accuracy of 0.57 to predict WHO grade 4 lesions. In subgroup analysis of high-grade gliomas vs low-grade gliomas, the accuracy for high-grade gliomas prediction increased to 0.83. The inclusion of molecular data into the model-IDH mutation and 1p19q codeletion status-increases the accuracy of the model in predicting tumor grading (0.95 and 0.74, respectively). ML algorithms based on nTMS-derived interhemispheric excitability assessment provide accurate predictions of HGGs affecting the motor pathway. Their accuracy is further increased when molecular data are fitted onto the model paving the way for a joint preoperative approach with radiogenomics.

MiR-26-3p regulates proliferation, migration, invasion and apoptosis of glioma cells by targeting CREB1

To investigate the regulatory role of miR-26b-3p in proliferation, migration and invasion of glioma. The expressions of miR-26b-3p and cAMP-responsive element binding protein 1 (CREB1) in gliomas of different pathological grades were detected with RT-qPCR and Western blotting. Bioinformatic methods were used to analyze the target sequence of miRNA-26b-3p binding to CREB1, and dual luciferase gene reporter experiment was performed to explore the mechanism for targeted regulation of CREB1 by miR-26b-3p. Glioma U251 cells were treated with miR-26b-3p mimic or inhibitor, and the changes in CREB1 expression and cell proliferation, migration, invasion and apoptosis were determined with Western blotting, CCK-8 assay, wound healing assay, Transwell assay, and flow cytometry. The expression of miR-26b-3p decreased while CREB1 expression increased significantly as the pathological grade of gliomas increased (P < 0.05). Dual luciferase gene reporter experiment confirmed that CREB1 was a downstream target of miR-26b-3p. Inhibition of miR-26b-3p significantly upregulated the expression of CERB1, suppressed apoptosis and promoted proliferation and invasion of glioma cells, and overexpression of miR-26b-3p produced the opposite effects (P < 0.05). MiR-26b-3p regulates CREB1 expression to modulate apoptosis, proliferation, migration and invasion of glioma cells, thereby participating in tumorigenesis and progression of glioma.

Lycorine hydrochloride interferes with energy metabolism to inhibit chemoresistant glioblastoma multiforme cell growth through suppressing PDK3.

Glioblastoma multiforme (GBM) is the highest grade of glioma. Tumours, including GBM, possess reprogrammed metabolism, such as altered aerobic glycolysis and aberrant energy production. Lycorine hydrochloride (LH) was extracted from the bulb of Lycoris radiata. The previous study indicated that LH exerts antiviral, anti-inflammatory and antitumour effects. However, the effect of LH on GBM and the underlying molecular mechanism remain unclear. Our study revealed that LH restrained chemoresistant GBM cells growth by inhibiting PDK3 expression in vitro and in vivo. Functionally, LH inhibited the proliferation and invasive capacity of chemoresistant GBM cells in dose-dependent manner. Metabolomics and cellular energy analyses showed that LH decreased extracellular acidification rates while increased oxidative respiration and ROS levels. Mechanistically, LH inhibits the growth of GBM chemoresistant cells by regulating the expression of apoptosis-related proteins, while overexpression of of PDK3 can reverse the antitumor effect of LH. In conclusion, our study revealed that LH could reprogramme cell energy metabolism, including aerobic glycolysis suppression and oxidative phosphorylation hyperactivation by inhibiting PDK3. PDK3 may be a candidate therapeutic target for chemoresistant GBM treatment with LH.

Impact of [11C]methionine PET with Bayesian penalized likelihood reconstruction on glioma grades based on new WHO 2021 classification.

The uptake of [11C]methionine in positron emission tomography (PET) imaging overlapped in earlier images of tumors. Bayesian penalized likelihood (BPL) reconstruction increases the quantitative values of tumors compared with conventional ordered subset-expectation maximization (OSEM). The present study aimed to grade glioma malignancy based on the new WHO 2021 classification using [11C]methionine PET images reconstructed using BPL. We categorized 32 gliomas in 28 patients as grades 2/3 (n = 15) and 4 (n = 17) based on the WHO 2021 classification. All [11C]methionine images were reconstructed using OSEM + time-of-flight (TOF) and BPL + TOF (β = 200). Maximum standardized uptake value (SUVmax) and tumor-to-normal tissue ratio (T/Nmax) were measured at each lesion. The mean SUVmax was 4.65 and 4.93 in grade 2/3 and 6.38 and 7.11 in grade 4, and the mean T/Nmax was 7.08 and 7.22 in grade 2/3 and 9.30 and 10.19 in grade 4 for OSEM and BPL, respectively. The BPL significantly increased these values in grade 4 gliomas. The area under the receiver operator characteristic (ROC) curve (AUC) for SUVmax was the highest (0.792) using BPL. The BPL increased mean SUVmax and mean T/Nmax in lesions with higher contrast such as grade 4 glioma. The discrimination power between grades 2/3 and 4 in SUVmax was also increased using [11C]methionine PET images reconstructed with BPL.

ABCG2 Expression as a Potential Survival Predictor in Human Gliomas.

Gliomas are notably challenging to treat due to their invasive nature and resistance to conventional therapies. The ABCG2 protein has attracted attention for its role in multidrug resistance, complicating treatment effectiveness. This study scrutinized the relationship between ABCG2 expression and glioma grade and the role of ABCG2 in the process of glioma progression, aiming to evaluate ABCG2 expression as a predictive factor of tumor progression and patient survival. Conducted at Dubrava University Hospital, Zagreb, Croatia, the study analyzed 152 glioma specimens from 2013 to 2022, assessing ABCG2 expression alongside standard clinical markers. A significant association was found between patients' survival and the ABCG2 profile (p = 0.003, r = 0.24), separately for patients who underwent chemotherapy (p = 0.0004, r = 0.32) and radiotherapy (p = 0.003, r = 0.29). Furthermore, the ABCG2 profile was significantly associated with disease progression (p = 0.007, r = 0.23), tumor grade (p = 0.0002, r = 0.31), and Ki67 expression (p = 0.0004, r = 0.31). ABCG2-positive tumor cells only showed association with Ki67 expression (p = 0.002, r = 0.28). The ABCG2 profile was found to affect the overall patient survival (p = 0.02) and represent a moderate indicator of tumor progression (p = 0.01), unlike the percentage of ABCG2-positive tumor cells. ABCG2 may serve as a marker of angiogenesis and vascular abnormalities within tumors, predicting glioma progression and treatment response. Targeting ABCG2 could enhance chemoradiotherapy efficacy and improve patient outcomes, which highlights its value in assessing tumor aggressiveness and designing treatment strategies.

Brain Tumor Detection and Categorization with Segmentation of Improved Unsupervised Clustering Approach and Machine Learning Classifier.

There is no doubt that brain tumors are one of the leading causes of death in the world. A biopsy is considered the most important procedure in cancer diagnosis, but it comes with drawbacks, including low sensitivity, risks during biopsy treatment, and a lengthy wait for results. Early identification provides patients with a better prognosis and reduces treatment costs. The conventional methods of identifying brain tumors are based on medical professional skills, so there is a possibility of human error. The labor-intensive nature of traditional approaches makes healthcare resources expensive. A variety of imaging methods are available to detect brain tumors, including magnetic resonance imaging (MRI) and computed tomography (CT). Medical imaging research is being advanced by computer-aided diagnostic processes that enable visualization. Using clustering, automatic tumor segmentation leads to accurate tumor detection that reduces risk and helps with effective treatment. This study proposed a better Fuzzy C-Means segmentation algorithm for MRI images. To reduce complexity, the most relevant shape, texture, and color features are selected. The improved Extreme Learning machine classifies the tumors with 98.56% accuracy, 99.14% precision, and 99.25% recall. The proposed classifier consistently demonstrates higher accuracy across all tumor classes compared to existing models. Specifically, the proposed model exhibits accuracy improvements ranging from 1.21% to 6.23% when compared to other models. This consistent enhancement in accuracy emphasizes the robust performance of the proposed classifier, suggesting its potential for more accurate and reliable brain tumor classification. The improved algorithm achieved accuracy, precision, and recall rates of 98.47%, 98.59%, and 98.74% on the Fig share dataset and 99.42%, 99.75%, and 99.28% on the Kaggle dataset, respectively, which surpasses competing algorithms, particularly in detecting glioma grades. The proposed algorithm shows an improvement in accuracy, of approximately 5.39%, in the Fig share dataset and of 6.22% in the Kaggle dataset when compared to existing models. Despite challenges, including artifacts and computational complexity, the study's commitment to refining the technique and addressing limitations positions the improved FCM model as a noteworthy advancement in the realm of precise and efficient brain tumor identification.

IGSF8 is a potential target for the treatment of gliomas

BackgroundImmunoglobulin superfamily member 8, or IGSF8, is a member of the recently identified immunoglobulin family of proteins. It is mostly produced on cell membranes and has a unique transmembrane structure. It has recently been demonstrated that there is a strong correlation between the expression variation of IGSF8 and the growth of gliomas. Therefore, we used data from the TCGA and CGGA databases to evaluate the function of IGSF8. MethodsThe TCGA and GTEx data sets' RNA-seq data were utilized to examine IGSF8 expression. The Gene Cards database was utilized to get IGSF8 protein data. The Cluster Profiler data package was used to carry out the IGSF8 enrichment study. The GO and KEGG databases were used to examine the relationship between IGSF8 and cellular physiological and biochemical processes. The TCGA immune cell infiltration scores were obtained from online databases and published studies. Clinical survival data from TCGA and CGGA were used to investigate the predictive significance of IGSF8. ResultsTGGA revealed that the majority of cancers had differential expression of IGSF8. IGSF8 was discovered to be enriched in numerous significant pathways in tumor cells by GO and KEGG. Moreover, a strong correlation was seen between the expression of IGSF8 and the immunomodulatory interactions that occur between non-lymphocytes and lymphocytes. T-cell infiltration, immunological checkpoints, immune-activating and immune-suppressive genes, chemokines, and chemokine receptors were all strongly correlated with IGSF8 expression. Lastly, the TCGA and CGGA databases showed a strong correlation between IGSF8 and the grade and prognosis of gliomas. ConclusionAccording to our findings, IGSF8 may be a glioma marker. In order to control the immunological microenvironment, IGSF8 may cooperate with a number of immune checkpoints. This information may be utilized to create novel targeted immunotherapy medications.

Self-consuming DNA nanogear retrieval exosomes for grading analysis of gliomas

Direct and accurate analysis of low concentrations of tumor-derived exosomes in complex biological samples is essential for non-invasive cancer diagnosis, but this remains challenging due to the lack of convenient and highly sensitive methods. Herein, we have proposed a novel strategy based on super spherical nucleic acids (SSNAs) combined with nicking enzyme triggering signal amplification for the direct detection of exosomes derived from gliomas. SSNAs utilize AuNPs as carriers, loaded with multifunctional DNA probes. And exosomes are simultaneously labeled by two aptamers. When SSNAs accurately recognize the labeled exosomes, the two make relative gear movement. During the gear movement, the probes act as an exosome-specific retrieval on the one hand, and serves as a fuel that is continuously consumed by the exosomes on the other hand, thus outputting a high-gain fluorescent signal. This strategy exhibits excellent specificity and ultra-high sensitivity for the detection of target exosomes as low as 20 particles/μL. The practicability of this method is further verified by the detection of clinical samples, which retains potential for early glioma diagnosis and grading analysis.

CPLX2 is a novel tumor suppressor and improves the prognosis in glioma.

Glioma is a type of malignant cancer that affect the central nervous system. New predictive biomarkers have been investigated in recent years, but the clinical prognosis for glioma remains poor. The function of CPLX2 in glioma and the probable molecular mechanism of tumor suppression were the focus of this investigation. The glioma transcriptome profile was downloaded from The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) databases for analysis of CPLX2 expression in glioma. RT-qPCR was performed to detect the expression of CPLX2 in 68 glioma subjects who have been followed up. Kaplan-Meier survival analyses were conducted to assess the effect of CPLX2 on the prognosis of glioma patients. The knockdown and overexpressed cell lines of CPLX2 were constructed to investigate the impact of CPLX2 on glioma. The cell growth, colony formation, and tumor formation in xenograft were performed. The expression of CPLX2 was downregulated in glioma and was negatively correlated with the grade of glioma. The higher expression of CPLX2 predicted a longer survival, as indicated by the analysis of Kaplan-Meier survival curves. Overexpressed CPLX2 impaired tumorigenesis in glioma progression both in vivo and in vitro. Knocking down CPLX2 promoted the proliferation of glioma cells. The analysis of GSEA and co-expression analysis revealed that CPLX2 may affect the malignancy of glioma by regulating the hypoxia and inflammation pathways. Our data indicated that CPLX2 functions as a tumor suppressor and could be used as a potential prognostic marker in glioma.

Global health status and fatigue score in isocitrate dehydrogenase-mutant diffuse glioma grades 2 and 3: A longitudinal population-based study from surgery to 12-month follow-up.

At the group level, health-related quality of life (HRQoL) in patients with IDH-mutant diffuse glioma grades 2 and 3 seems to remain stable over time. However, clinical experience indicates that there are patients with unfavorable outcomes on key HRQoL subdomains. The aim of this longitudinal population-based study, following patients over a period of 12 months from surgery, was to describe individual-level data on global health status and fatigue score and explore possible predictors of deterioration. All patients undergoing surgery for presumed glioma grades 2 or 3 at the Sahlgrenska University Hospital during 2017-2022, were screened for the study. Patients were invited to complete the European Organization of Research and Treatment of Cancer core questionnaires and brain module at baseline, 3 and 12 months postoperatively. Data is reported with respect to minimal clinical important difference (MCID). We included 51 patients with IDH-mutant diffuse glioma grades 2 or 3. There was no difference in group-level data of either global health status or fatigue score from baseline to the 12-month follow-up (P-value > .05). Unfavorable individual changes (beyond MCID) in global health status and fatigue score were observed in 12 and in 17 patients, respectively (23.5% and 33.3%). A lower proportion of proton radiotherapy was found in patients with unfavorable changes in fatigue (10/15, 66.7%) compared to all other patients undergoing radiotherapy (22/23, 95.7%, P-value .03). Deterioration beyond MCID was seen in approximately one-third of patients. Changes in global health status could not be predicted, but changes in fatigue may be influenced by tumor-targeted and symptomatic treatment.

Normalizing Flow-Based Distribution Estimation of Pharmacokinetic Parameters in Dynamic Contrast-Enhanced Magnetic Resonance Imaging.

The pharmacokinetic (PK) parameters estimated from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) provide valuable information for clinical research and diagnosis. However, these estimated PK parameters suffer from many sources of variability. Thus, the estimation of the posterior distributions of these PK parameters could provide a way to simultaneously quantify the values and uncertainties of the PK parameters. Our objective is to develop an efficient and flexible method to more closely approximate and estimate the underlying posterior distributions of the PK parameters. The normalizing flow model-based parameters distribution estimation neural network (FPDEN) is proposed to adaptively learn and estimate the posterior distributions of the PK parameters. The maximum likelihood estimation (MLE) loss is directly constructed based on the parameter distributions learned by the normalizing flow model, rather than pre-defined distributions. Experimental analysis shows that the proposed method can improve parameter estimation accuracy. Moreover, the uncertainty derived from the parameter distribution constitutes an effective indicator to exclude unreliable parametric results. A successful demonstration is the improved classification performance of the glioma World Health Organization (WHO) grading task, specifically in terms of distinguishing between low and high grades, as well as between Grade III and Grade IV. The FPDEN method offers improved accuracy for estimation of PK parameters and boosts the performance of the glioma grading task. By enhancing the precision and reliability of DCE-MRI, the proposed method promotes its further applications in clinical practice.

Efficient glioma grade prediction using learned features extracted from convolutional neural networks

Efficient glioma grade prediction using learned features extracted from convolutional neural networks

Deep Learning Glioma Grading with the Tumor Microenvironment Analysis Protocol for Comprehensive Learning, Discovering, and Quantifying Microenvironmental Features.

Gliomas are primary brain tumors that arise from neural stem cells, or glial precursors. Diagnosis of glioma is based on histological evaluation of pathological cell features and molecular markers. Gliomas are infiltrated by myeloid cells that accumulate preferentially in malignant tumors, and their abundance inversely correlates with survival, which is of interest for cancer immunotherapies. To avoid time-consuming and laborious manual examination of images, a deep learning approach for automatic multiclass classification of tumor grades was proposed. As an alternative way of investigating characteristics of brain tumor grades, we implemented a protocol for learning, discovering, and quantifying tumor microenvironment elements on our glioma dataset. Using only single-stained biopsies we derived characteristic differentiating tumor microenvironment phenotypic neighborhoods. The study was complicated by the small size of the available human leukocyte antigen stained on glioma tissue microarray dataset - 206 images of 5 classes - as well as imbalanced data distribution. This challenge was addressed by image augmentation for underrepresented classes. In practice, we considered two scenarios, a whole slide supervised learning classification, and an unsupervised cell-to-cell analysis looking for patterns of the microenvironment. In the supervised learning investigation, we evaluated 6 distinct model architectures. Experiments revealed that a DenseNet121 architecture surpasses the baseline's accuracy by a significant margin of 9% for the test set, achieving a score of 69%, increasing accuracy in discerning challenging WHO grade 2 and 3 cases. All experiments have been carried out in a cross-validation manner. The tumor microenvironment analysis suggested an important role for myeloid cells and their accumulation in the context of characterizing glioma grades. Those promising approaches can be used as an additional diagnostic tool to improve assessment during intraoperative examination or subtyping tissues for treatment selection, potentially easing the workflow of pathologists and oncologists.

Clinical roles of EGFR amplification in diffuse gliomas: a real-world study using the 2021 WHO classification of CNS tumors.

The 2021 World Health Organization Classification of Central Nervous System Tumors updates glioma subtyping and grading system, and incorporates EGFR amplification (Amp) as one of diagnostic markers for glioblastoma (GBM). This study aimed to describe the frequency, clinical value and molecular correlation of EGFR Amp in diffuse gliomas based on the latest classification. We reviewed glioma patients between 2011 and 2022 at our hospital, and included 187 adult glioma patients with available tumor tissue for detection of EGFR Amp and other 59 molecular markers of interest. Clinical, radiological and pathological data was analyzed based on the status of EGFR Amp in different glioma subtypes. 163 gliomas were classified as adult-type diffuse gliomas, and the number of astrocytoma, oligodendroglioma and GBM was 41, 46, and 76. EGFR Amp was more common in IDH-wildtype diffuse gliomas (66.0%) and GBM (85.5%) than IDH-mutant diffuse gliomas (32.2%) and its subtypes (astrocytoma, 29.3%; oligodendroglioma, 34.8%). EGFR Amp did not stratify overall survival (OS) in IDH-mutant diffuse gliomas and astrocytoma, while was significantly associated with poorer OS in IDH-wildtype diffuse gliomas, histologic grade 2 and 3 IDH-wildtype diffuse astrocytic gliomas and GBM. Our study validated EGFR Amp as a diagnostic marker for GBM and still a useful predictor for shortened OS in this group.

A multi-class brain tumor grading system based on histopathological images using a hybrid YOLO and RESNET networks

Gliomas are primary brain tumors caused by glial cells. These cancers’ classification and grading are crucial for prognosis and treatment planning. Deep learning (DL) can potentially improve the digital pathology investigation of brain tumors. In this paper, we developed a technique for visualizing a predictive tumor grading model on histopathology pictures to help guide doctors by emphasizing characteristics and heterogeneity in forecasts. The proposed technique is a hybrid model based on YOLOv5 and ResNet50. The function of YOLOv5 is to localize and classify the tumor in large histopathological whole slide images (WSIs). The suggested technique incorporates ResNet into the feature extraction of the YOLOv5 framework, and the detection results show that our hybrid network is effective for identifying brain tumors from histopathological images. Next, we estimate the glioma grades using the extreme gradient boosting classifier. The high-dimensional characteristics and nonlinear interactions present in histopathology images are well-handled by this classifier. DL techniques have been used in previous computer-aided diagnosis systems for brain tumor diagnosis. However, by combining the YOLOv5 and ResNet50 architectures into a hybrid model specifically designed for accurate tumor localization and predictive grading within histopathological WSIs, our study presents a new approach that advances the field. By utilizing the advantages of both models, this creative integration goes beyond traditional techniques to produce improved tumor localization accuracy and thorough feature extraction. Additionally, our method ensures stable training dynamics and strong model performance by integrating ResNet50 into the YOLOv5 framework, addressing concerns about gradient explosion. The proposed technique is tested using the cancer genome atlas dataset. During the experiments, our model outperforms the other standard ways on the same dataset. Our results indicate that the proposed hybrid model substantially impacts tumor subtype discrimination between low-grade glioma (LGG) II and LGG III. With 97.2% of accuracy, 97.8% of precision, 98.6% of sensitivity, and the Dice similarity coefficient of 97%, the proposed model performs well in classifying four grades. These results outperform current approaches for identifying LGG from high-grade glioma and provide competitive performance in classifying four categories of glioma in the literature.

Circulating serum miR-362-3p and miR-6721-5p as potential biomarkers for classification patients with adult-type diffuse glioma.

According to the fifth edition of the WHO Classification of Tumours of the Central Nervous System (CNS) published in 2021, grade 4 gliomas classification includes IDH-mutant astrocytomas and wild-type IDH glioblastomas. Unfortunately, despite precision oncology development, the prognosis for patients with grade 4 glioma remains poor, indicating an urgent need for better diagnostic and therapeutic strategies. Circulating miRNAs besides being important regulators of cancer development could serve as promising diagnostic biomarkers for patients with grade 4 glioma. Here, we propose a two-miRNA miR-362-3p and miR-6721-5p screening signature for serum for non-invasive classification of identified glioma cases into the highest-grade 4 and lower-grade gliomas. A total of 102 samples were included in this study, comprising 78 grade 4 glioma cases and 24 grade 2-3 glioma subjects. Using the NanoString platform, seven miRNAs were identified as differentially expressed (DE), which was subsequently confirmed via RT-qPCR analysis. Next, numerous combinations of DE miRNAs were employed to develop classification models. The dual panel of miR-362-3p and miR-6721-5p displayed the highest diagnostic value to differentiate grade 4 patients and lower grade cases with an AUC of 0.867. Additionally, this signature also had a high AUC = 0.854 in the verification cohorts by RT-qPCR and an AUC = 0.842 using external data from the GEO public database. The functional annotation analyses of predicted DE miRNA target genes showed their primary involvement in the STAT3 and HIF-1 signalling pathways and the signalling pathway of pluripotency of stem cells and glioblastoma-related pathways. For additional exploration of miRNA expression patterns correlated with glioma, we performed the Weighted Gene-Co Expression Network Analysis (WGCNA). We showed that the modules most associated with glioma grade contained as many as six DE miRNAs. In conclusion, this study presents the first evidence of serum miRNA expression profiling in adult-type diffuse glioma using a classification based on the WHO 2021 guidelines. We expect that the discovered dual miR-362-3p and miR-6721-5p signatures have the potential to be utilised for grading gliomas in clinical applications.

Looking through the imaging perspective: the importance of imaging necrosis in glioma diagnosis and prognostic prediction - single centre experience.

The aim of the study was to investigate the diagnostic value of imaging necrosis (Imnecrosis) in grading, predict the genotype and prognosis of gliomas, and further assess tumor necrosis by dynamic contrast-enhanced MR perfusion imaging (DCE-MRI). We retrospectively included 150 patients (104 males, mean age: 46 years old) pathologically proved as adult diffuse gliomas and all diagnosis was based on the 2021 WHO central nervous system (CNS) classification. The pathological necrosis (Panecrosis) and gene mutation information were collected. All patients underwent conventional and DCE-MRI examinations and had been followed until May 31, 2021. The Imnecrosis was determined by two experienced neuroradiologists. DCE-MRI derived metric maps have been post-processed, and the mean value of each metric in the tumor parenchyma, peritumoral and contralateral area were recorded. There was a strong degree of inter-observer agreement in defining Imnecrosis (Kappa = 0.668, p < 0.001) and a strong degree of agreement between Imnecrosis and Panecrosis (Kappa = 0.767, p < 0.001). Compared to low-grade gliomas, high-grade gliomas had more Imnecrosis (85.37%, p < 0.001), and Imnecrosis significantly increased with the grade of gliomas increasing. And Imnecrosis was significantly more identified in IDH-wildtype, 1p19q-non-codeletion, and CDKN2A/B-homozygous-deletion gliomas. Using multivariate Cox regression analysis, Imnecrosis was an independent and unfavorable prognosis factor (Hazard Ratio = 2.113, p = 0.046) in gliomas. Additionally, extravascular extracellular volume fraction (ve) in tumor parenchyma derived from DCE-MRI demonstrated the highest diagnostic efficiency in identifying Panecrosis and Imnecrosis with high specificity (83.3% and 91.9%, respectively). Imnecrosis can provide supplementary evidence beyond Panecrosis in grading, predicting the genotype and prognosis of gliomas, and ve in tumor parenchyma can help to predict tumor necrosis with high specificity.