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Background and Objectives: Histopathological features and molecular biomarkers have been studied as potential prognostic factors. This study aimed to investigate the clinical features, molecular phenotypes, and survival prognosis of isocitrate dehydrogenase (IDH)-mutant (IDHmt) gliomas with histone H3 alterations (H3-alterations)., Methods: A total of 236 and 657 patients with whole-exome sequencing data were separately collected from the Chinese Glioma Genome Atlas and The Cancer Genome Atlas databases. Survival analysis of patients with glioma was performed using Kaplan-Meier survival curves stratified by histone H3 status. Univariate and multivariate analyses were used to identify the associations between histone H3 status and other clinicopathological factors with survival in patients with IDH-mutant gliomas., Results: Diffuse gliomas with H3 alterations are more likely to be high grade in 2 cohorts ( P = .025 and P = .021, respectively). IDHmt glioma patients with H3-alteration had significantly less life expectancy than histone H3 wild-type ( P = .041 and P = .008, respectively). In the Chinese Glioma Genome Atlas cohort, Karnofsky performance scores ≤ 80 (HR 2.394, 95% CI 1.257-4.559, P = .008), extent of resection (HR 0.971, 95% CI 0.957-0.986, P < .001), high WHO grade (HR 6.938, 95% CI 2.787-17.269, P < .001), H3-alteration (HR 2.482, 95% CI 1.183-4.981, P = .016), and 1p/19q codeletion (HR 0.169, 95% CI 0.073-0.390, P < .001) were independently associated with IDHmt gliomas. In the The Cancer Genome Atlas cohort, age (HR 1.034, 95% CI 1.008-1.061, P = .010), high WHO grade (HR 2.365, 95% CI 1.263-4.427, P = .007), and H3-alteration (HR 2.501, 95% CI 1.312-4.766, P = .005) were independently associated with IDHmt gliomas., Conclusion: Identification and assessment of histone H3 status in clinical practice might help improve prognostic prediction and develop therapeutic strategies for these patient subgroups., (Copyright © 2023 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the Congress of Neurological Surgeons.)

Triggering receptor expressed on myeloid cells 2 (TREM2) is expressed in myeloid cells of the central nervous system (CNS), which mediate the immunological response in a variety of diseases. Uncertain is the function of TREM2 in glioma and tumor immune responses. In this research, the expression patterns of TREM2 in glioma were analyzed, along with its prognostic value and functional roles. TREM2 expression is increased in glioblastomas, gliomas with a mesenchymal subtype, gliomas with wild-type isocitrate dehydrogenase, and gliomas without 1p/19q deletion, all of which suggest the aggressiveness and poor prognosis of gliomas. Gene ontology, KEGG, and Gene set variation analyses indicated that TREM2 may serve as an immune response mediator. However, the function of T cells against tumor cells was negatively correlated with TREM2, suggesting that TREM2 may suppress tumor immunity. Further investigation demonstrated a correlation between TREM2 expression and immune checkpoint expression. CIBERSORT research revealed a link between a higher TREM2 expression level and the enrichment of tumor-associated macrophages, especially M2 subtype. Single-cell analysis and multiple immunohistochemical staining results showed that microglia and macrophage cells expressed TREM2. Immunofluorescent staining indicated that knocking down the expression of TREM2 would result in a decrease in M2 polarization. TREM2 was discovered to be an independent prognostic factor in glioma. In conclusion, our findings revealed that TREM2 was significantly expressed in microglia and macrophage cells and was intimately associated with the tumor immune microenvironment. Thus, it is expected that small-molecule medications targeting TREM2 or monoclonal antibodies would enhance the efficacy of glioma immunotherapy., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The reviewers XH, FC declared a shared affiliation with the author(s) to the handling editor at the time of review., (Copyright © 2023 Yu, Chang, Zhai, Pang, Wang, Li, Jiang and Zeng.)

Preoperative MRI is one of the most important clinical results for the diagnosis and treatment of glioma patients. The objective of this study was to construct a stable and validatable preoperative T2-weighted MRI-based radiomics model for predicting the survival of gliomas. A total of 652 glioma patients across three independent cohorts were covered in this study including their preoperative T2-weighted MRI images, RNA-seq and clinical data. Radiomic features (1731) were extracted from preoperative T2-weighted MRI images of 167 gliomas (discovery cohort) collected from Beijing Tiantan Hospital and then used to develop a radiomics prediction model through a machine learning-based method. The performance of the radiomics prediction model was validated in two independent cohorts including 261 gliomas from the The Cancer Genomae Atlas database (external validation cohort) and 224 gliomas collected in the prospective study from Beijing Tiantan Hospital (prospective validation cohort). RNA-seq data of gliomas from discovery and external validation cohorts were applied to establish the relationship between biological function and the key radiomics features, which were further validated by single-cell sequencing and immunohistochemical staining. The 14 radiomic features-based prediction model was constructed from preoperative T2-weighted MRI images in the discovery cohort, and showed highly robust predictive power for overall survival of gliomas in external and prospective validation cohorts. The radiomic features in the prediction model were associated with immune response, especially tumour macrophage infiltration. The preoperative T2-weighted MRI radiomics prediction model can stably predict the survival of glioma patients and assist in preoperatively assessing the extent of macrophage infiltration in glioma tumours., (© The Author(s) (2021). Published by Oxford University Press on behalf of the Guarantors of Brain.)

Background: Glioblastoma is a paradigm of cancer-associated immunosuppression, limiting the effects of immunotherapeutic strategies. Thus, identifying the molecular mechanisms underlying immune surveillance evasion is critical. Recently, the preferential expression of inhibitory natural killer (NK) cell receptor CD161 on glioma-infiltrating cytotoxic T cells was identified. Focusing on the molecularly annotated, large-scale clinical samples from different ethnic origins, the data presented here provide evidence of this immune modulator's essential roles in brain tumor biology., Methods: Retrospective RNA-seq data analysis was conducted in a cohort of 313 patients with glioma in the Chinese Glioma Genome Atlas (CGGA) database and 603 patients in The Cancer Genome Atlas (TCGA) database. In addition, single-cell sequencing data from seven surgical specimens of glioblastoma patients and a model in which patient-derived glioma stem cells were cocultured with peripheral lymphocytes, were used to analyze the molecular evolution process during gliomagenesis., Results: CD161 was enriched in high-grade gliomas and isocitrate dehydrogenase (IDH)-wildtype glioma. CD161 acted as a potential biomarker for the mesenchymal subtype of glioma and an independent prognostic factor for the overall survival (OS) of patients with glioma. In addition, CD161 played an essential role in inhibiting the cytotoxicity of T cells in glioma patients. During the process of gliomagenesis, the expression of CD161 on different lymphocytes dynamically evolved., Conclusion: The expression of CD161 was closely related to the pathology and molecular pathology of glioma. Meanwhile, CD161 promoted the progression and evolution of gliomas through its unique effect on T cell dysfunction. Thus, CD161 is a promising novel target for immunotherapeutic strategies in glioma treatment., (© 2021 The Authors. Cancer Science published by John Wiley & Sons Australia, Ltd on behalf of Japanese Cancer Association.)

Gliomas with chromosome 1p/19q codeletion were considered a specific tumor entity. This study was designed to reveal the biological function alterations tightly associated with 1p/19q codeletion in gliomas. Clinicopathological and RNA sequencing data from glioma patients were obtained from The Cancer Genome Atlas and Chinese Glioma Genome Atlas databases. Gene set variation analysis was performed to explore the differences in biological functions between glioma subgroups stratified by 1p/19q codeletion status. The abundance of immune cells in each sample was detected using the CIBERSORT analytical tool. Single-cell sequencing data from public databases were analyzed using the t-distributed stochastic neighbor embedding (t-SNE) algorithm, and the findings were verified by in vitro and in vivo experiments and patient samples.We found that the activation of immune and inflammatory responses was tightly associated with 1p/19q codeletion in gliomas. As the most important transcriptional regulator of Galectin-9 in gliomas, the expression level of CCAAT enhancer-binding protein alpha in samples with 1p/19q codeletion was significantly decreased, which led to the downregulation of the immune checkpoints Galectin-9 and TIM-3. These results were validated in three independent datasets. The t-SNE analysis showed that the loss of chromosome 19q was the main reason for the promotion of the antitumor immune response. IHC protein staining, in vitro and in vivo experiments verified the results of bioinformatics analysis. In gliomas, 1p/19q codeletion can promote the antitumor immune response by downregulating the expression levels of the immune checkpoint TIM-3 and its ligand Galectin-9., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Li, Huang, Fan, Wang, Wu, Zeng, Yu, Zhai, Chang, Pan, Jiang, Yan, Wang and Zhang.)

Background: Lower-grade gliomas (LGGs) show highly metabolic heterogeneity and adaptability. To develop effective therapeutic strategies targeting metabolic processes, it is necessary to identify metabolic differences and define metabolic subtypes. Here, we aimed to develop a classification system based on metabolic gene expression profile in LGGs., Methods: The metabolic gene profile of 402 diffuse LGGs from the Cancer Genome Atlas (TCGA) was used for consensus clustering to determine robust clusters of patients, and the reproducibility of the classification system was evaluated in three Chinese Glioma Genome Atlas (CGGA) cohorts. Then, the metadata set for clinical characteristics, immune infiltration, metabolic signatures and somatic alterations was integrated to characterise the features of each subtype., Results: We successfully identified and validated three highly distinct metabolic subtypes in LGGs. M2 subtype with upregulated carbohydrate, nucleotide and vitamin metabolism correlated with worse prognosis, whereas M1 subtype with upregulated lipid metabolism and immune infiltration showed better outcome. M3 subtype was associated with low metabolic activities and displayed good prognosis. Three metabolic subtypes correlated with diverse somatic alterations. Finally, we developed and validated a metabolic signature with better performance of prognosis prediction., Conclusions: Our study provides a new classification based on metabolic gene profile and highlights the metabolic heterogeneity within LGGs., (© 2021. The Author(s), under exclusive licence to Springer Nature Limited.)

Aims: We aimed to create a tumor recurrent-based prediction model to predict recurrence and survival in patients with low-grade glioma., Methods: This study enrolled 291 patients (188 in the training group and 103 in the validation group) with clinicopathological information and transcriptome sequencing data. LASSO-COX algorithm was applied to shrink predictive factor size and build a predictive recurrent signature. GO, KEGG, and GSVA analyses were performed for function annotations of the recurrent signature. The calibration curves and C-Index were assessed to evaluate the nomogram's performance., Results: This study found that DNA repair functions of tumor cells were significantly enriched in recurrent low-grade gliomas. A predictive recurrent signature, built by the LASSO-COX algorithm, was significantly associated with overall survival and progression-free survival in low-grade gliomas. Moreover, function annotations analysis of the predictive recurrent signature exhibited that the signature was associated with DNA repair functions. The nomogram, combining the predictive recurrent signature and clinical prognostic predictors, showed powerful prognostic ability in the training and validation groups., Conclusion: An individualized prediction model was created to predict 1-, 2-, 3-, 5-, and 10-year survival and recurrent rate of patients with low-grade glioma, which may serve as a potential tool to guide postoperative individualized care., (© 2020 The Authors. CNS Neuroscience & Therapeutics Published by John Wiley & Sons Ltd.)

Glioma is the most common and malignant cancer of the central nervous system, and the prognosis is poor. Metabolic reprogramming is a common phenomenon that plays an important role in tumor progression including gliomas. Searching the representative process among numerous metabolic processes to evaluate the prognosis aside from the glycolytic pathway may be of great significance. A novel prediction signature was constructed in the present study based on gene expression. A total of 1027 glioma samples with clinical and RNA-seq data were used in the present study. Lasso-Cox, gene set variation analysis, Kaplan-Meier survival curve analysis, Cox regression, receiver operating characteristic curve, and elastic net were performed for constructing and verifying predictive models. The R programming language was used as the main tool for statistical analysis and graphical work. This signature was found to be stable in prognostic prediction in the Chinese Glioma Genome Atlas Network and the Cancer Genome Atlas databases. The possible mechanism was also explored, revealing that the aforementioned signature was closely related to DNA replication and ATP binding. In summary, a prognosis prediction signature for patients with glioma based on five genes was constructed and showed great potential for clinical application., (© 2021 The Author(s).)

Glutaredoxin is central to cellular redox chemistry and regulates redox homeostasis and malignant progression of many cancers. In glioma, the role of its coding gene ( GLRX ) remains unclear. We aimed to elucidate the role of glutaredoxin at the transcriptome level and its clinical prognostic value in glioma. In total, we evaluated 1,717 glioma samples with transcriptome data and corresponding clinical data as well as single-cell sequencing data from 6 glioma patients from publicly available databases. Gene set variation analysis and gene ontology analysis were performed to reveal the biological function of GLRX . The immune cell enrichment score was calculated by GSVA analysis. Single-cell sequencing data was visualized by t -distributed stochastic neighbor embedding analysis. The prognostic value of GLRX in glioma was verified by the Kaplan-Meier curve and multivariate COX analysis. GLRX was found to be highly enriched in gliomas of higher grades with wild-type IDH , without 1p/19q co-deletion, and with a methylated MGMT promoter. Moreover, GLRX could be a potential marker for the mesenchymal molecular subtype of gliomas. The expression of GLRX was closely related to the tumor immune process, immune checkpoints, and inflammatory factors with GLRX being specifically expressed in M0 macrophages. GLRX is also shown to be an independent prognostic factor in glioma. Altogether, our study outcomes show that GLRX is highly enriched in malignant gliomas and is closely related to the tumor immune microenvironment. Therefore, GLRX -targeted cell redox regulatory therapy may enhance the efficacy of glioma immunotherapy., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2020 Chang, Li, Zhai, Huang, Feng, Wang, Zhang and Hu.)

Glioblastoma is one of the most common neoplasms in the central nervous system characterized by limited immune response and unlimited expansion capability. Cancer stem cells (GSCs), a small fraction of the tumor cells, possess a pivotal regulation capability in the tumor microenvironment with a superior proliferation ability. We aimed to reveal the interaction between glioma stem cells (GSCs) and immune cells during tumorigenesis. Single-cell sequencing data from seven surgical specimens of glioblastoma patients and patient-derived GSCs cocultured with peripheral leukocytes were used for the analysis. Cell grouping and trajectory analysis were performed using Seurat and Monocle 3 packages in R software. The gene set of Cancer Genome Anatomy Project was used to define different cell types. Cells with the ability of proliferation and differentiation in glioblastoma tissue were defined as GSCs, which had a similar expression pattern to that in the GSCs in vitro . Astrocytes in glioblastoma were mainly derived from differentiated GSCs, while oligodendrocytes were most likely to be derived from different precursor cells. No remarkable evolutionary trajectory was observed among the subgroups of T cells in glioblastoma. The immune checkpoint interaction between GSCs and immune cells was changed from stimulatory to inhibitory during tumorigenesis. The patient-derived GSCs system is an ideal model for GSC research. The above research revealed that the interaction pattern between GSC glioma stem cells and immune cells during tumorigenesis provides a theoretical basis for GSC glioma stem cell-targeted immunotherapy., (Copyright © 2020 Zhai, Li, Li, Chang, Feng, Wang, Wu and Zhang.)

Transcriptomic data derived from bulk sequencing have been applied to delineate the tumor microenvironment (TME) and define immune subtypes in various cancers, which may facilitate the design of immunotherapy treatment strategies. We herein gathered published gene expression data from diffuse lower-grade glioma (LGG) patients to identify immune subtypes. Based on the immune gene profiles of 402 LGG patients from The Cancer Genome Atlas, we performed consensus clustering to determine robust clusters of patients, and evaluated their reproducibility in three Chinese Glioma Genome Atlas cohorts. We further integrated immunogenomics methods to characterize the immune environment of each subtype. Our analysis identified and validated three immune subtypes-Im1, Im2, and Im3-characterized by differences in lymphocyte signatures, somatic DNA alterations, and clinical outcomes. Im1 had a higher infiltration of CD8+ T cells, Th17, and mast cells. Im2 was defined by elevated cytolytic activity, exhausted CD8+ T cells, macrophages, higher levels of aneuploidy, and tumor mutation burden, and these patients had worst outcome. Im3 displayed more prominent T helper cell and APC coinhibition signatures, with elevated pDCs and macrophages. Each subtype was associated with distinct somatic alterations. Moreover, we applied graph structure learning-based dimensionality reduction to the immune landscape and revealed significant intracluster heterogeneity with Im2 subtype. Finally, we developed and validated an immune signature with better performance of prognosis prediction. Our results demonstrated the immunological heterogeneity within diffuse LGG and provided valuable stratification for the design of future immunotherapy., (© 2020 The Authors. Published by FEBS Press and John Wiley & Sons Ltd.)

Isocitrate dehydrogenase (IDH) wild-type diffuse lower-grade glioma (LGG) is usually associated with poor outcome, but there have been disputes over its clinical outcome and classification. We present here a robust gene expression-based molecular classification of IDH wild-type diffuse LGG into two subtypes with distinct biological and clinical features. A discovery cohort of 49 IDH wild-type diffuse LGGs from the Chinese Glioma Genome Atlas (CGGA) was subjected to clustering and function analysis. Seventy-three tumors from The Cancer Genome Atlas (TCGA) were used to validate our findings. Consensus clustering of transcriptional data uncovered concordant classification of two robust and prognostically significant subtypes of IDH wild-type LGG. Subtype 1, associated with poorer outcomes, was characterized by significantly higher immune and cytolytic scores, M2 macrophages, and up-regulation of immune exhaustion markers, while Subtype 2, which had elevated lymphocytes and plasma cells, showed relatively favorable survival. Somatic alteration analysis revealed that Subtype 1 showed more frequently deleted regions, such as the locus of CDKN2A/CDKN2B, DMRTA1, C9orf53, and MTAP. Furthermore, we developed and validated a five-gene signature for better application of this acquired stratification. Our data demonstrate the biological and prognostic heterogeneity within IDH wild-type diffuse LGGs and deepen our molecular understandi-g of this tumor entity. © 2020 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd., (© 2020 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.)

Background Poliovirus receptor interacts with 3 receptors: T-cell immunoglobulin immunoreceptor tyrosine-based inhibitory motif, CD96, and DNAX accessory molecule 1, which are predominantly expressed on T cells and natural killer (NK) cells. Many solid tumors, including IDH wild-type glioblastoma, have been reported to overexpress poliovirus receptor, and this overexpression is associated with poor prognosis. However, there are no preclinical or clinical trials investigating the use of cell-based immunotherapies targeting poliovirus receptor in IDH wild-type glioblastoma. Methods We analyzed poliovirus receptor expression in transcriptome sequencing databases and specimens from IDH wild-type glioblastoma patients. We developed poliovirus receptor targeting chimeric antigen receptor T cells using lentivirus. The antitumor activity of chimeric antigen receptor T cells was demonstrated in patient-derived glioma stem cells, intracranial and subcutaneous mouse xenograft models. Results We verified poliovirus receptor expression in primary glioma stem cells, surgical specimens from IDH wild-type glioblastoma patients, and organoids. Accordingly, we developed poliovirus receptor–based second-generation chimeric antigen receptor T cells. The antitumor activity of chimeric antigen receptor T cells was demonstrated in glioma stem cells and xenograft models. Tumor recurrence occurred in intracranial xenograft models because of antigen loss. The combinational therapy of tyrosine-based inhibitory motif extracellular domain–based chimeric antigen receptor T cells and NK-92 cells markedly suppressed tumor recurrence and prolonged survival. Conclusions Poliovirus receptor–based chimeric antigen receptor T cells were capable of killing glioma stem cells and suppressing tumor recurrence when combined with NK-92 cells. [ABSTRACT FROM AUTHOR]

Preoperative MRI is one of the most important clinical results for the diagnosis and treatment of glioma patients. The objective of this study was to construct a stable and validatable preoperative T2-weighted MRI-based radiomics model for predicting the survival of gliomas. A total of 652 glioma patients across three independent cohorts were covered in this study including their preoperative T2-weighted MRI images, RNA-seq and clinical data. Radiomic features (1731) were extracted from preoperative T2-weighted MRI images of 167 gliomas (discovery cohort) collected from Beijing Tiantan Hospital and then used to develop a radiomics prediction model through a machine learning-based method. The performance of the radiomics prediction model was validated in two independent cohorts including 261 gliomas from the The Cancer Genomae Atlas database (external validation cohort) and 224 gliomas collected in the prospective study from Beijing Tiantan Hospital (prospective validation cohort). RNA-seq data of gliomas from discovery and external validation cohorts were applied to establish the relationship between biological function and the key radiomics features, which were further validated by single-cell sequencing and immunohistochemical staining. The 14 radiomic features-based prediction model was constructed from preoperative T2-weighted MRI images in the discovery cohort, and showed highly robust predictive power for overall survival of gliomas in external and prospective validation cohorts. The radiomic features in the prediction model were associated with immune response, especially tumour macrophage infiltration. The preoperative T2-weighted MRI radiomics prediction model can stably predict the survival of glioma patients and assist in preoperatively assessing the extent of macrophage infiltration in glioma tumours. [ABSTRACT FROM AUTHOR]

Glioma is the most common malignant primary brain tumor diagnosed in adults. Current therapies are unable to improve its clinical prognosis, imposing the need for innovative therapeutic approaches. The main reason for the poor prognosis is the great cell heterogeneity of the tumor and its immunosuppressive microenvironment. Development of new therapies that avoid this immune evasion could improve the response to the current treatments. Natural killer (NK) cells are an intriguing candidate for the next wave of therapies because of several unique features that they possess. For example, NK cell-based immunotherapy causes minimal graft- versus -host disease. Cytokine release syndrome is less likely to occur during chimeric antigen receptor (CAR)-NK therapy, and CAR-NK cells can kill targets in a CAR-independent manner. However, NK cell-based therapy in treating glioma faces several difficulties. For example, CAR molecules are not sufficiently well designed so that they will thoroughly release functioning NK cells. Compared to hematological malignancies, the application of many potential NK cell-based therapies in glioma lags far behind. Here, we review several issues of NK cells and propose several strategies that will improve the efficacy of NK cell-based cancer immunotherapy in the treatment of glioma. [ABSTRACT FROM AUTHOR]