Prognosis Of High-grade Gliomas Research Articles

Diagnostic and Prognostic Values of miRNAs in High-Grade Gliomas: A Systematic Review

Abstract* Background Gliomas, particularly glioblastomas, have grim prognoses, necessitating early diagnostic and prognostic indicators. MicroRNAs (miRNAs), influential in cancer research, show potential as glioma biomarkers. This systematic review aimed to examine the efficacy of miRNAs in the diagnosis and prognosis of high-grade glioma. Methods A comprehensive search was conducted of PubMed, EMBASE, Cochrane Library, and Web of Science for studies published from 2013 to 2023. The eligibility criteria included high-grade glioma, histopathological confirmation, miRNA samples from cerebrospinal fluid or plasma, and relevant outcome data. Studies were excluded if they were experimental or reviews and not in English. Results Of the 1120 initial results, 8 studies involving 660 subjects met the inclusion criteria. Several studies have assessed miRNA expression and its association with diagnosis and prognosis of high-grade gliomas. Overexpression of miR-221, miR-222, miR-210, miR-21, miR-125b, and miR-223 and under-expression of miR-15b and miR-124-3p showed significant potential in differentiating high-grade glioma patients from controls. Additionally, miRNAs are associated with distinct tumorigenic pathways. Conclusion Elevated or depressed expression levels of specific circulating miRNAs hold significant promise as noninvasive biomarkers for the diagnosis and prognosis of high-grade glioma. These miRNAs offer valuable insights into disease progression and patient outcome. Further validation through extensive clinical trials and in-depth mechanistic studies is essential to realize their full clinical utility.

Characterizing the Linkage of Systemic Hypoxia and Angiogenesis in High-Grade Glioma to Define the Changes in Tumor Microenvironment for Predicting Prognosis.

High-grade gliomas (HGG) comprising WHO grades 3 and 4 have a poor overall survival (OS) that has not improved in the past decade. Herein, markers representing four components of the tumor microenvironment (TME) were identified to define their linked expression in TME and predict the prognosis in HGG, namely, interleukin6 (IL6, inflammation), inducible nitric oxide synthase(iNOS), heat shock protein-70 (HSP70, hypoxia), vascular endothelial growth receptor (VEGF), and endothelin1 (ET1) (angiogenesis) and matrix metalloprotease-14 (MMP14) and intercellular adhesion molecule1 (ICAM1, extracellular matrix). To establish a non-invasive panel of biomarkers for precise prognostication in HGG. Eighty-six therapy-naive HGG patients with 45 controls were analyzed for the defined panel. Systemic expression of extracellular/secretory biomarkers was screened dot-immune assay (DIA), quantified by ELISA, and validated by immunocytochemistry (ICC). Expression of iNOS, HSP70, IL-6, VEGF, ET1, MMP14, and ICAM1 was found to be positively associated with grade. Quantification of circulating levels of the markers by ELISA and ICC presented a similar result. The biomarkers were observed to negatively correlate with OS (p < 0.0001). Cox-regression analysis yielded all biomarkers as good prognostic indicators and independent of confounders. On applying combination statistics, the biomarker panel achieved higher sensitivity than single markers to define survival. The intra-association of all seven biomarkers was significant, hinting of a cross-talk between the TME components and a hypoxia driven systemic inflammation upregulating the expression of other components. This is a first ever experimental study of a marker panel that can distinguish between histopathological grades and also delineate differential survival using liquid biopsy, suggesting that markers of hypoxia can be a cornerstone for personalized therapy. The panel of biomarkers of iNOS, HSP70, IL-6, VEGF, ET1, MMP14, and ICAM1 holds promise for prognostication in HGG.

Accurate and rapid molecular subgrouping of high-grade glioma via deep learning-assisted label-free fiber-optic Raman spectroscopy.

Molecular genetics is highly related with prognosis of high-grade glioma. Accordingly, the latest WHO guideline recommends that molecular subgroups of the genes, including IDH, 1p/19q, MGMT, TERT, EGFR, Chromosome 7/10, CDKN2A/B, need to be detected to better classify glioma and guide surgery and treatment. Unfortunately, there is no preoperative or intraoperative technology available for accurate and comprehensive molecular subgrouping of glioma. Here, we develop a deep learning-assisted fiber-optic Raman diagnostic platform for accurate and rapid molecular subgrouping of high-grade glioma. Specifically, a total of 2,354 fingerprint Raman spectra was obtained from 743 tissue sites (astrocytoma: 151; oligodendroglioma: 150; glioblastoma (GBM): 442) of 44 high-grade glioma patients. The convolutional neural networks (ResNet) model was then established and optimized for molecular subgrouping. The mean area under receiver operating characteristic curves (AUC) for identifying the molecular subgroups of high-grade glioma reached 0.904, with mean sensitivity of 83.3%, mean specificity of 85.0%, mean accuracy of 83.3%, and mean time expense of 10.6 s. The diagnosis performance using ResNet model was shown to be superior to PCA-SVM and UMAP models, suggesting that high dimensional information from Raman spectra would be helpful. In addition, for the molecular subgroups of GBM, the mean AUC reached 0.932, with mean sensitivity of 87.8%, mean specificity of 83.6%, and mean accuracy of 84.1%. Furthermore, according to saliency maps, the specific Raman features corresponding to tumor-associated biomolecules (e.g. nucleic acid, tyrosine, tryptophan, cholesteryl ester, fatty acid, and collagen) were found to contribute to the accurate molecular subgrouping. Collectively, this study opens up new opportunities for accurate and rapid molecular subgrouping of high-grade glioma, which would assist optimal surgical resection and instant post-operative decision-making.

Clinical Theranostics in Recurrent Gliomas: A Review.

Gliomas represent the most commonly occurring tumors in the central nervous system and account for approximately 80% of all malignant primary brain tumors. With a high malignancy and recurrence risk, the prognosis of high-grade gliomas is poor, with a mean survival time of 12-18 months. While contrast-enhanced MRI serves as the standard diagnostic imaging modality for gliomas, it faces limitations in the evaluation of recurrent gliomas, failing to distinguish between treatment-related changes and tumor progression, and offers no direct therapeutic options. Recent advances in imaging modalities have attempted to address some of these limitations, including positron emission tomography (PET), which has demonstrated success in delineating tumor margins and guiding the treatment of recurrent gliomas. Additionally, with the advent of theranostics in nuclear medicine, PET tracers, when combined with therapeutic agents, have also evolved beyond a purely diagnostic modality, serving both diagnostic and therapeutic roles. This review will discuss the growing involvement of theranostics in diagnosing and treating recurrent gliomas and address the associated impact on quality of life and functional recovery.

Cell Membrane Fragment-Wrapped Parenteral Nanoemulsions: A New Drug Delivery Tool to Target Gliomas.

Poor prognosis in high-grade gliomas is mainly due to fatal relapse after surgical resection in the absence of efficient chemotherapy, which is severely hampered by the blood-brain barrier. However, the leaky blood-brain-tumour barrier forms upon tumour growth and vascularization, allowing targeted nanocarrier-mediated drug delivery. The homotypic targeting ability of cell-membrane fragments obtained from cancer cells means that these fragments can be exploited to this aim. In this experimental work, injectable nanoemulsions, which have a long history of safe clinic usage, have been wrapped in glioma-cell membrane fragments via co-extrusion to give targeted, homogeneously sized, sterile formulations. These systems were then loaded with three different chemotherapeutics, in the form of hydrophobic ion pairs that can be released into the target site thanks to interactions with physiological components. The numerous assays performed in two-dimensional (2D) and three-dimensional (3D) cell models demonstrate that the proposed approach is a versatile drug-delivery platform with chemo-tactic properties towards glioma cells, with adhesive interactions between the target cell and the cell membrane fragments most likely being responsible for the effect. This approach's promising translational perspectives towards personalized nanomedicine mean that further in vivo studies are foreseen for the future.

MRI-based Machine Learning Radiomics Can Predict CSF1R Expression Level and Prognosis in High-grade Gliomas.

The purpose of this study is to predict the mRNA expression of CSF1R in HGG non-invasively using MRI (magnetic resonance imaging) omics technology and to evaluate the correlation between the established radiomics model and prognosis. We investigated the predictive value of CSF1R in the Cancer Genome Atlas (TCGA) and The Cancer Imaging Archive (TCIA) database. The Support vector machine (SVM) and the Logistic regression (LR) algorithms were used to create a radiomics_score (Rad_score), respectively. The effectiveness and performance of the radiomics model was assessed in the training (n = 89) and tenfold cross-validation sets. We further analyzed the correlation between Rad_score and macrophage-related genes using Spearman correlation analysis. A radiomics nomogram combining the clinical factors and Rad_score was constructed to validate the radiomic signatures for individualized survival estimation and risk stratification. The results showed that CSF1R expression was markedly elevated in HGG tissues, which was related to worse prognosis. CSF1R expression was closely related to the abundance of infiltrating immune cells, such as macrophages. We identified nine features for establishing a radiomics model. The radiomics model predicting CSF1R achieved high AUC in training (0.768 in SVM and 0.792 in LR) and tenfold cross-validation sets (0.706 in SVM and 0.717 in LR). Rad_score was highly associated with tumor-related macrophage genes. A radiomics nomogram combining the Rad_score and clinical factors was constructed and revealed satisfactory performance. MRI-based Rad_score is a novel way to predict CSF1R expression and prognosis in high-grade glioma patients. The radiomics nomogram could optimize individualized survival estimation for HGG patients.

A fusion model integrating magnetic resonance imaging radiomics and deep learning features for predicting alpha-thalassemia X-linked intellectual disability mutation status in isocitrate dehydrogenase-mutant high-grade astrocytoma: a multicenter study.

The mutational status of alpha-thalassemia X-linked intellectual disability (ATRX) is an important indicator for the treatment and prognosis of high-grade gliomas, but reliable ATRX testing currently requires invasive procedures. The objective of this study was to develop a clinical trait-imaging fusion model that combines preoperative magnetic resonance imaging (MRI) radiomics and deep learning (DL) features with clinical variables to predict ATRX status in isocitrate dehydrogenase (IDH)-mutant high-grade astrocytoma. A total of 234 patients with IDH-mutant high-grade astrocytoma (120 ATRX mutant type, 114 ATRX wild type) from 3 centers were retrospectively analyzed. Radiomics and DL features from different regions (edema, tumor, and the overall lesion) were extracted to construct multiple imaging models by combining different features in different regions for predicting ATRX status. An optimal imaging model was then selected, and its features and linear coefficients were used to calculate an imaging score. Finally, a fusion model was developed by combining the imaging score and clinical variables. The performance and application value of the fusion model were evaluated through the comparison of receiver operating characteristic curves, the construction of a nomogram, calibration curves, decision curves, and clinical application curves. The overall hybrid model constructed with radiomics and DL features from the overall lesion was identified as the optimal imaging model. The fusion model showed the best prediction performance with an area under curve of 0.969 in the training set, 0.956 in the validation set, and 0.949 in the test set as compared to the optimal imaging model (0.966, 0.916, and 0.936, respectively) and clinical model (0.677, 0.641, 0.772, respectively). The clinical trait-imaging fusion model based on preoperative MRI could effectively predict the ATRX mutation status of individuals with IDH-mutant high-grade astrocytoma and has the potential to help patients through the development of a more effective treatment strategy before treatment.

Multimodel habitats constructed by perfusion and/or diffusion MRI predict isocitrate dehydrogenase mutation status and prognosis in high-grade gliomas

To determine whether tumour vascular and cellular heterogeneity of high-grade glioma (HGG) is predictive of isocitrate dehydrogenase (IDH) mutation status and overall survival (OS) by using tumour habitat-based analysis constructed by perfusion and/or diffusion magnetic resonance imaging (MRI). Seventy-eight HGG patients that met the 2021 World Health Organization WHO Classification of Tumors of the Central Nervous System, 5th edition (WHO CNS5), were enrolled to predict IDH mutation status, of which 32 grade 4 patients with unmethylated O6-methylguanine-DNA methyltransferase (MGMT) promoter were enrolled for prognostic analysis. The deep-learning-based model nnU-Net and K-means clustering algorithm were applied to construct the Traditional Habitat, Vascular Habitat (VH), Cellular Density Habitat (DH), and their Combined Habitat (CH). Quantitative parameters were extracted and compared between IDH-mutant and IDH-wild-type patients, respectively, and the prediction potential was evaluated by receiver operating characteristic (ROC) curve analysis. OS was analysed using Kaplan-Meier survival analysis and the log-rank test. Compared with IDH-mutants, median relative cerebral blood volume (rCBVmedian) values in the whole enhancing tumour (WET), VH1, VH3, CH1-4 habitats were significantly increased in IDH-wild-type HGGs (all p<0.05). Additionally, the accuracy of rCBVmedian values in CH1 outperformed other habitats in identifying IDH mutation status (p<0.001) at a cut-off value of 4.83 with AUC of 0.815. Kaplan-Meier survival analysis highlighted significant differences in OS between the populations dichotomised by the median of rCBVmedian in WET, VH1, CH1-3 habitats (all p<0.05). The habitat imaging technique may improve the accuracy of predicting IDH mutation status and prognosis, and even provide a new direction for subsequent personalised precision treatment.

NEED OF THE HOUR TO USE MR PERFUSION TO DIFFERENTIATE BETWEEN RADIATION NECROSIS AND TUMOUR RECURRENCE IN KNOWN GLIOMA PATIENTS

Conventional MR imaging may be non-specific in diagnosing low-grade gliomas. Better modalities are used nowadays including MRS (spectroscopy), perfusion, and diffusion-weighted imaging (1).&#x0D; MR perfusion is better than CT perfusion in various aspects. MR perfusion does not expose the patient to ionizing radiation. CT perfusion, on the other hand, can be harmful if the patient receives repeated doses. However, CT perfusion may be more readily available in emergency settings and can be quicker to perform than MR perfusion, which requires a longer acquisition time. CT perfusion can also provide more precise measurements of blood flow in small vessels than MR perfusion. Ultimately, the choice between MR perfusion and CT perfusion depends on the specific clinical situation and the preferences of the medical team.&#x0D; Perfusion MRI studies have become an increasingly important tool in the management of high-grade gliomas as well. Perfusion MRI allows clinicians to assess the vascularity of these tumors, providing information on tumor grade, response to treatment, and prognosis. Perfusion imaging can be performed using various techniques, including dynamic susceptibility contrast (DSC) MRI and arterial spin labeling (ASL) MRI. DSC-MRI is the most commonly used technique, and it provides information on cerebral blood volume (CBV) and cerebral blood flow (CBF) in the tumor and surrounding tissue. ASL-MRI, on the other hand, measures blood flow by magnetically labeling arterial blood water protons and can provide information on tumor perfusion without the use of contrast agents. Overall, perfusion MRI studies have demonstrated significant potential for improving the diagnosis, management, and prognosis of high-grade gliomas. MR brain perfusion is a type of imaging technique that can be used to differentiate between radiation-related changes and tumor recurrence in patients who have undergone radiation therapy for brain tumors. (2).&#x0D; &#x0D; Tumor resection followed by chemoradiation remains the current criterion standard treatment for high-grade gliomas. Radiation therapy can cause changes in the brain that may appear similar to tumor recurrence on conventional MRI scans. However, MR brain perfusion can detect differences in blood flow patterns between tumor tissue and normal brain tissue, which can help to differentiate between radiation-related changes/necrosis and tumor recurrence (3).&#x0D; Every institute should start using MR perfusion studies to differentiate these. This will eventually help in patient correct diagnosis and reduction in morbidity and mortality.&#x0D; Key-words:&#x0D; Gliomas, MR Perfusion, CT Perfusion, Tumour Recurrence, Radiation Necrosis.&#x0D; &#x0D; Conflict of Interest: All authors declare no conflict of interest.&#x0D; &#x0D; Funding Role: None to declare.&#x0D; &#x0D; Author Contributions: All authors contributed equally to the drafting of study.&#x0D; &#x0D; Ethical Statement: Written as per the ethical guidelines of hospital board.&#x0D; &#x0D;

Radiomics Features on Magnetic Resonance Images Can Predict C5aR1 Expression Levels and Prognosis in High-Grade Glioma.

The complement component C5a receptor 1 (C5aR1) regulates cancer immunity. This retrospective study aimed to assess its prognostic value in high-grade glioma (HGG) and predict C5aR1 expression using a radiomics approach. Among 298 patients with HGG, 182 with MRI data were randomly divided into training and test groups for radiomics analysis. We examined the association between C5aR1 expression and prognosis through Kaplan-Meier and Cox regression analyses. We used maximum relevance-minimum redundancy and recursive feature elimination algorithms for radiomics feature selection. We then built a support vector machine (SVM) and a logistic regression model, investigating their performances using receiver operating characteristic, calibration curves, and decision curves. C5aR1 expression was elevated in HGG and was an independent prognostic factor (hazard ratio = 3.984, 95% CI: 2.834-5.607). Both models presented with >0.8 area under the curve values in the training and test datasets, indicating efficient discriminatory ability, with SVM performing marginally better. The radiomics score calculated using the SVM model correlated significantly with overall survival (p < 0.01). Our results highlight C5aR1's role in HGG development and prognosis, supporting its potential as a prognostic biomarker. Our radiomics model can noninvasively and effectively predict C5aR1 expression and patient prognosis in HGG.

P09.03.A DEVELOPMENT OF A CORE OUTCOME SET FOR USE IN ADULT PRIMARY GLIOMA PHASE III INTERVENTIONAL TRIALS - A MIXED METHODS STUDY

Abstract BACKGROUND Glioma patients’ high symptom burden and the poor prognosis of high grade glioma has emphasised quality of survival and overall health-related quality of life (HRQOL) throughout the disease trajectory in clinical trials. Patient-reported outcomes (PROs) allow insight into how investigative treatment affects patients’ perceived functioning, complementing other outcome data (e.g. survival, radiological response) in clinical decision-making. Creating robust evidence requires effective data synthesis and meta-analyses across trials, underpinned by consistent trial outcome measurement, analysis and reporting. However, a standard ontology is lacking in cancer clinical trials, and brain tumours specifically, and selective outcome reporting is common. Core outcome set (COS) development is one means to improve data synthesis. The COBra project aimed to develop a COS for use in glioma phase III trials in support of those goals. Given stakeholder interest in PROs and prioritising the patient perspective, we also undertook initial exploration of which items could be patient reportable. MATERIAL AND METHODS A COS for adult primary glioma phase III interventional trials was developed through: (1) trial registry review and systematic review of qualitative studies; (2) interviews with glioma patients and caregivers; (3) outcome list de-duplication; (4) two-round Delphi process and outcome list rating; (5) a consensus meeting to finalise the COS. Patient-reportable COS outcomes were identified by cross-referencing the final COS outcomes with outcomes and PRO measures (PROMs) reported in the trial registry review. Activities were guided by UK Patient and Public Involvement standards and public contributors were core research team members. RESULTS A COS consisting of 19 outcomes grouped into seven outcome domains (Survival, Adverse Events, Activities of Daily Living, HRQOL, Seizure Activity, Cognitive Function and Physical Function) was finalised for use in glioma trials using this structured approach. Mapping PROMs identified in the registry review suggests that sub-domains of 6/7 outcome domains in the final COS can be patient-reported (adverse events, activities of daily living, HRQOL, seizure activity, neurocognitive function, physical function). Other currently unidentified instruments may also be suitable. Further research will identify appropriate measurement tools, promote accessible PRO delivery, and validate this COS for wider use. CONCLUSION A COS for glioma trials was developed, comprising seven outcome domains. Development of this COS coincides with growing regulatory interest in patient-reported data. To optimise data synthesis and prioritise the patient perspective, opportunities to collect data in this way should be promoted and taken. As such, this COS could be considered for use in registrational trials after further validation.

Transcriptional induction of NF-κB-inducing kinase by E2F4/5 facilitates collective invasion of GBM cells

The prognosis of high-grade gliomas, such as glioblastoma multiforme (GBM), is extremely poor due to the highly invasive nature of these aggressive cancers. Previous work has demonstrated that TNF-weak like factor (TWEAK) induction of the noncanonical NF-κB pathway promotes the invasiveness of GBM cells in an NF-κB-inducing kinase (NIK)-dependent manner. While NIK activity is predominantly regulated at the posttranslational level, we show here that NIK (MAP3K14) is upregulated at the transcriptional level in invading cell populations, with the highest NIK expression observed in the most invasive cells. GBM cells with high induction of NIK gene expression demonstrate characteristics of collective invasion, facilitating invasion of neighboring cells. Furthermore, we demonstrate that the E2F transcription factors E2F4 and E2F5 directly regulate NIK transcription and are required to promote GBM cell invasion in response to TWEAK. Overall, our findings demonstrate that transcriptional induction of NIK facilitates collective cell migration and invasion, thereby promoting GBM pathogenesis.

Comparative dosimetric study of radiotherapy in high-grade gliomas based on the guidelines of EORTC and NRG-2019 target delineation.

Radiotherapy is one of the most important treatments for high-grade glioma (HGG), but the best way to delineate the target areas for radiotherapy remains controversial, so our aim was to compare the dosimetric differences in radiation treatment plans generated based on the European Organization for Research and Treatment of Cancer (EORTC) and National Research Group (NRG) consensus to provide evidence for optimal target delineation for HGG. We prospectively enrolled 13 patients with a confirmed HGG from our hospital and assessed dosimetric differences in radiotherapy treatment plans generated according to the EORTC and NRG-2019 guidelines. For each patient, two treatment plans were generated. Dosimetric parameters were compared by dose-volume histograms for each plan. The median volume for planning target volume (PTV) of EORTC plans, PTV1 of NRG-2019 plans, and PTV2 of NRG-2019 plans were 336.6 cm3 (range, 161.1-511.5 cm3), 365.3 cm3 (range, 123.4-535.0 cm3), and 263.2 cm3 (range, 116.8-497.7 cm3), respectively. Both treatment plans were found to have similar efficiency and evaluated as acceptable for patient treatment. Both treatment plans showed well conformal index and homogeneity index and were not statistically significantly different (P = 0.397 and P = 0.427, respectively). There was no significant difference in the volume percent of brain irradiated to 30, 46, and 60 Gy according to different target delineations (P = 0.397, P = 0.590, and P = 0.739, respectively). These two plans also showed no significant differences in the doses to the brain stem, optic chiasm, left and right optic nerves, left and right lens, left and right eyes, pituitary, and left and right temporal lobes (P = 0.858, P = 0.858, P = 0.701 and P = 0.794, P = 0.701 and P = 0.427, P = 0.489 and P = 0.898, P = 0.626, and P = 0.942 and P = 0.161, respectively). The NRG-2019 project did not increase the dose of organs at risk (OARs) radiation. This is a significant finding that further lays the groundwork for the application of the NRG-2019 consensus in the treatment of patients with HGGs. The effect of radiotherapy target area and glial fibrillary acidic protein (GFAP) on the prognosis of high-grade glioma and its mechanism, number ChiCTR2100046667. Registered 26 May 2021.

Impact of redox-related genes on tumor microenvironment immune characteristics and prognosis of high-grade gliomas.

High-grade glioma (HGG) defines a group of brain gliomas characterized by contrast enhancement, high tumor heterogeneity, and poor clinical outcome. Disturbed reduction-oxidation (redox) balance has been frequently associated with the development of tumor cells and their microenvironment (TME). To study the influence of redox balance on HGGs and their microenvironment, we collected mRNA-sequencing and clinical data of HGG patients from TCGA and CGGA databases and our own cohort. Redox-related genes (ROGs) were defined as genes in the MSigDB pathways with keyword "redox" that were differentially expressed between HGGs and normal brain samples. Unsupervised clustering analysis was used to discover ROG expression clusters. Over-representation analysis (ORA), gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA) were also employed to understand the biological implication of differentially expressed genes between HGG clusters. CIBERSORTx and ESTIMATE were used to profile the immune TME landscapes of tumors, and TIDE was used to evaluated the potential response to immune checkpoint inhibitors. Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression was used to construct HGG-ROG expression risk signature (GRORS). Seventy-five ROGs were found and consensus clustering using the expression profile of ROGs divided the both IDH-mutant (IDHmut) and IDH-wildtype (IDHwt) HGGs into subclusters with different prognosis. Functional enrichment analysis revealed that the differential aggressiveness between redox subclusters in IDHmut HGGs were significantly associated with cell cycle regulation pathways, while IDHwt HGG redox subclusters showed differentially activated immune-related pathways. In silico TME analysis on immune landscapes in the TME showed that the more aggressive redox subclusters in both IDHmut and IDHwt HGGs may harbor a more diverse composition of tumor-infiltrating immune cells, expressed a higher level of immune checkpoints and were more likely to respond to immune checkpoint blockade. Next, we established a GRORS which showed AUCs of 0.787, 0.884, and 0.917 in predicting 1-3-year survival of HGG patients in the held-out validation datasets, and the C-index of a nomogram combining the GRORS and other prognostic information reached 0.835. Briefly, our results suggest that the expression pattern of ROGs was closely associated with the prognosis as well as the TME immune profile of HGGs, and may serve as a potential indicator for their response to immunotherapies.

Spectral Analysis Based on Hemodynamic Habitat Imaging Predicts Isocitrate Dehydrogenase Status and Prognosis in High-Grade Glioma

The intratumoral heterogeneity of high-grade gliomas (HGGs) is associated with isocitrate dehydrogenase (IDH) status and prognosis, which can be established by quantitative radioanalysis of spatial tumor habitats. Therefore, we designed a framework for tackling tumors based on spatial metabolism using the hemodynamic tissue signature (HTS), focusing on metabolic changes in tumor habitat to predict IDH status and assess prognosis in patients with HGG. Preoperative data for 121 patients with HGG with subsequent histologic confirmation of HGG were prospectively collected (January 2016 to December 2020). The HTS was mapped from the image data, chemical shift imaging voxels were selected from the HTS habitat as the region of interest, and the metabolic ratio of the HTS was calculated using weighted least square method fitting. The metabolic rate of the tumor enhancement area was used as a control to analyze the efficacy of each HTS metabolic rate in predicting the IDH status and prognosis of HGG. Total choline (Cho)/total creatine and Cho/N-acetyl-aspartate showed significant differences between IDH-wildtype and IDH-mutant in high- and low-angiogenic enhanced tumor sites (P < 0.05); Cho/total creatine was an independent risk factor for prognosis of HGG patients in high-angiogenic enhanced tumor habitats, with significant differences in survival time between groups (P < 0.05). The metabolic ratio in the tumor enhanced area could not predict IDH status or evaluate prognosis. Spectral analysis based on hemodynamic habitat imaging can clearly distinguish IDH mutations and the prognosis assessment is more accurate, rendering it superior to traditional spectral analysis in tumor enhancement areas.

Mikro RNK kod visokogradusnih glioma - koja je njihova uloga?

High-grade gliomas are malignant tumours of the central nervous system with poor overall survival. Equivalently, glioblastoma is one of the most devastating brain tumours. Treatment for most high-grade gliomas includes surgical resection, radiotherapy, and chemotherapy. Even with all treatment modalities, at a certain point, disease progression occurs. Moreover, each of the treatment modalities can lead to different toxicities. In the last ten years, many studies have aimed to find a stable and unique biomarker that can help diagnose brain tumours, overcome treatment resistance, and improve overall survival. MicroRNAs are non-coding elements of the genome that are relatively stable in serum and plasma and can be isolated from the tissue as well. It has been discovered that the alteration of many microRNAs can be seen in high-grade gliomas. The determined microRNA could potentially play a part in the diagnosis and prognosis of high-grade gliomas, have a therapeutic role in the treatment of high-grade gliomas or act as a predictive biomarker of treatment-induced toxicity. To achieve this, every high-grade glioma should have its own microRNA signature. Numerous studies have detected a big potential of certain microRNAs. The disadvantages of these studies are that they mostly included a small number of samples. Moreover, research into microRNA as potential therapeutic agents has primarily been based on cell lines, or xenografts. On the other hand, many microRNAs show significant alterations in high-grade gliomas, but still, their altered expression can be detected in other cancers and some non-oncological diseases. In this article, we made a critical mini-review of the role of microRNAs in high-grade gliomas.

Upregulated Immunogenic Cell-Death-Associated Gene Signature Predicts Reduced Responsiveness to Immune-Checkpoint-Blockade Therapy and Poor Prognosis in High-Grade Gliomas.

Background: Immunogenic cell death (ICD) has emerged as a potential mechanism mediating adaptive immune response and tumor immunity in anti-cancer treatment. However, the signature of ICD in high-grade gliomas (HGGs) remains largely unknown, and its relevance to immunotherapies is still undetermined. The purpose of this study is to identify ICD-associated genotypes in order to explore their relevance to tumor immunity, patient prognosis and therapeutic efficacy of immune checkpoint blockade (ICB) therapy in HGGs. Methods: Bulk RNA-seq data and clinical information on 169 and 297 patients were obtained from the Cancer Genome Atlas (TCGA) and China Glioma Genome Atlas (CGGA), respectively. The functional enrichment and characterization of ICD genotyping were detected, and the ICD prognostic signature prediction model was constructed using least absolute shrinkage and selection operator (LASSO) regression. The responsiveness to immunotherapy was predicted according to the scoring of the ICD prognostic signature. Results: The HGG patients with high ICD gene signature (C1) showed poor outcomes, increased activity of immune modulation and immune escape, high levels of immune-checkpoint markers, and HLA-related genes, which may explain their reduced response to ICB immunotherapy. A gene set of the ICD signature, composing FOXP3, IL6 LY96, MYD88 and PDIA3, showed an independent prognostic value in both the TCGA and the CGGA HGG cohort. Conclusions: Our in silico analyses identified the ICD gene signature in HGGs with potential implications for predicting the responsiveness to ICB immune therapy and patient outcomes.

Liquid Biopsy in Diagnosis and Prognosis of High-Grade Gliomas; State-of-the-Art and Literature Review.

Gliomas, particularly high-grade gliomas, represent the most common and aggressive tumors of the CNS and are still burdened by high mortality and a very poor prognosis, regardless of the type of therapy. Their diagnosis and monitoring rely on imaging techniques and direct biopsy of the pathological tissue; however, both procedures have inherent limitations. To address these limitations, liquid biopsies have been proposed in this field. They could represent an innovative tool that could help clinicians in the early diagnosis, monitoring, and prognosis of these tumors. Furthermore, the rapid development of next-generation sequencing (NGS) technologies has led to a significant reduction in sequencing cost, with improved accuracy, providing a molecular profile of cancer and leading to better survival results and less disease burden. This paper focuses on the current clinical application of liquid biopsy in the early diagnosis and prognosis of cancer, introduces NGS-related methods, reviews recent progress, and summarizes challenges and future perspectives.

High Expression of PLAGL2 is Associated With Poor Prognosis in High-Grade Glioma.

Pleomorphic adenoma gene like-2 (PLAGL2) has been implicated in the development and progression of diverse malignancies, including glioblastoma. An increasing number of studies have reported that dysregulated expression of PLAGL2 is a common phenomenon in different malignancies. However, the mechanism and biological functions of PLAGL2 in patients with high-grade glioma (HGG) remain unclear. In addition, the expression and clinical significance of PLAGL2 in HGG have not yet been reported. Herein, we investigated the expression patterns and prognostic values of PLAGL2 in patients with HGG by using various databases, including Tumor Immune Estimation Resource 2.0 (TIMER2.0), GENT2, ONCOMINE, GEPIA, Human Protein Atlas, and Gene Expression Omnibus datasets. In the present study, we analyzed the relationship between PLAGL2 mRNA expression and clinical parameters in 184 HGG cases and found that PLAGL2 presented positively high expression and was relevant to poor prognosis. Immunohistochemistry analysis confirmed the overexpression of PLAGL2 protein, which is mainly expressed in the nucleus of glioma. Additionally, a high level of expression of the PLAGL2 gene was associated with lower survival in progression-free survival and overall survival in GBM patients. The correlation analysis between PLAGL2 and immune infiltration related to the abundance of B cells, CD8+ T cells, CD4+ T cells, macrophages, DCs, and neutrophils was also performed using TIMER2.0. GSEA results showed that high PLAGL2 expression was associated with cell migration, proliferation, actin cytoskeletal, and angiogenesis. To sum up, our findings indicated that PLAGL2 could serve as an independent prognostic biomarker and might be a potential therapeutic target for HGG, which should be further investigated.

The epithelial-mesenchymal transition of glioma cells promotes tissue factor expression via the miR200a/ZEB1 axis

Glioma is the most common brain tumor and the main cause of death from primary brain tumors. Due to the limitations of current diagnostic and treatment methods, the prognosis of high-grade glioma is not optimistic and is prone to venous thrombosis. Epithelial-mesenchymal transition (EMT) is a vital step for glioma cells to obtain a highly migratory and invasive cell phenotype. Tissue factor (TF) is the downstream target of several carcinogenic pathways. According to reports, the TF gene is highly methylated and down-regulated in IDH1 mutant gliomas with good prognosis. We aimed to investigate the impact of EMT on the expression of TF in glioma cells, as well as the corresponding mechanism. Our data indicated that the expression level of TF in glioma tissues increased, and was positively correlated with the WHO classification of glioma. After inducing EMT in glioma cells in vitro, TF expression increased significantly, indicating that EMT in glioma cells can promote TF expression. Further studies have shown that the expression level of ZEB1 is positively correlated with the WHO classification of glioma tissues and the expression level of TF in glioma tissues. This study proved that EMT promotes the expression of TF in glioma cells through the miR-200a/ZEB1 axis. In summary, these results indicated that EMT can promote the expression of TF in glioma cells via the miR-200a/ZEB1 axis. For gliomas, TF is related to EMT and has the potential to become an effective target against EMT and thrombotic events.