Glioma Subtypes Research Articles

Construction of a pyroptosis-related lncRNAs signature for predicting prognosis and immunotherapy response in glioma.

Recent studies have proved that pyroptosis-related long non-coding RNAs (PRlncRNAs) are closely linked to tumor progression, prognosis, and immunity. Here, we systematically evaluated the correlation of PRlncRNAs with glioma prognosis. This study included 3 glioma cohorts (The Cancer Genome Atlas, Chinese Glioma Genome Atlas, and Gravendeel). Through Pearson correlation analysis, PRlncRNAs were screened from these 3 cohorts. Univariate Cox regression analysis was then carried out to determine the prognostic PRlncRNAs. A pyroptosis-related lncRNAs signature (PRLS) was then built by least absolute shrinkage and selection operator and multivariate Cox analyses. We systematically evaluated the correlation of the PRLS with the prognosis, immune features, and tumor mutation burden in glioma. A total of 14 prognostic PRlncRNAs overlapped in all cohorts and were selected as candidate lncRNAs. Based on The Cancer Genome Atlas cohort, a PRLS containing 7 PRlncRNAs was built. In all cohorts, the PRLS was proved to be a good predictor of glioma prognosis, with a higher risk score related to a poorer prognosis. We observed obvious differences in the immune microenvironment, immune cell infiltration level, and immune checkpoint expression in low- and high-risk subgroups. Compared with low-risk cases, high-risk cases had lower Tumor Immune Dysfunction and Exclusion scores and greater tumor mutation burden, indicating that high-risk cases can be more sensitive to immunotherapy. A nomogram combining PRLS and clinical parameters was constructed, which showed more robust and accurate predictive power. In conclusion, the PRLS is a potentially useful indicator for predicting prognosis and response to immunotherapy in glioma. Our findings may provide a useful insight into clinically individualized treatment strategies for patients.

Tumor Microenvironment in Gliomas: A Treatment Hurdle or an Opportunity to Grab?

Gliomas are the most frequent central nervous system (CNS) primary tumors. The prognosis and clinical outcomes of these malignancies strongly diverge according to their molecular alterations and range from a few months to decades. The tumor-associated microenvironment involves all cells and connective tissues surrounding tumor cells. The composition of the microenvironment as well as the interactions with associated neoplastic mass, are both variables assuming an increasing interest in these last years. This is mainly because the microenvironment can mediate progression, invasion, dedifferentiation, resistance to treatment, and relapse of primary gliomas. In particular, the tumor microenvironment strongly diverges from isocitrate dehydrogenase (IDH) mutated and wild-type (wt) tumors. Indeed, IDH mutated gliomas often show a lower infiltration of immune cells with reduced angiogenesis as compared to IDH wt gliomas. On the other hand, IDH wt tumors exhibit a strong immune infiltration mediated by several cytokines and chemokines, including CCL2, CCL7, GDNF, CSF-1, GM-CSF, etc. The presence of several factors, including Sox2, Oct4, PD-L1, FAS-L, and TGF β2, also mediate an immune switch toward a regulatory inhibited immune system. Other important interactions are described between IDH wt glioblastoma cells and astrocytes, neurons, and stem cells, while these interactions are less elucidated in IDH-mutated tumors. The possibility of targeting the microenvironment is an intriguing perspective in terms of therapeutic drug development. In this review, we summarized available evidence related to the glioma microenvironment, focusing on differences within different glioma subtypes and on possible therapeutic development.

Improving Noninvasive Classification of Molecular Subtypes of Adult Gliomas With Diffusion-Weighted MR Imaging: An Externally Validated Machine Learning Algorithm.

Genetic testing for molecular markers of gliomas sometimes is unavailable because of time-consuming and expensive, even limited tumor specimens or nonsurgery cases. To train a three-class radiomic model classifying three molecular subtypes including isocitrate dehydrogenase (IDH) mutations and 1p/19q-noncodeleted (IDHmut-noncodel), IDH wild-type (IDHwt), IDH-mutant and 1p/19q-codeleted (IDHmut-codel) of adult gliomas and investigate whether radiomic features from diffusion-weighted imaging (DWI) could bring additive value. Retrospective. A total of 755 patients including 111 IDHmut-noncodel, 571 IDHwt, and 73 IDHmut-codel cases were divided into training (n=480) and internal validation set (n=275); 139 patients including 21 IDHmut-noncodel, 104 IDHwt, and 14 IDHmut-codel cases were utilized as external validation set. A 1.5T or 3.0 T/multiparametric MRI, including T1-weighted (T1), T1-weighted gadolinium contrast-enhanced (T1c), T2-weighted (T2), fluid attenuated inversion recovery (FLAIR), and DWI. The performance of multiparametric radiomic model (random-forest model) using 22 selected features from T1, T2, FLAIR, T1c images and apparent diffusion coefficient (ADC) maps, and conventional radiomic model using 20 selected features from T1, T2, FLAIR, and T1c images was assessed in internal and external validation sets by comparing probability values and actual incidence. Mann-Whitney U test, Chi-Squared test, Wilcoxon test, receiver operating curve (ROC), and area under the curve (AUC); DeLong analysis. P < 0.05 was statistically significant. The multiparametric radiomic model achieved AUC values for IDHmut-noncodel, IDHwt, and IDHmut-codel of 0.8181, 0.8524, and 0.8502 in internal validation set and 0.7571, 0.7779, and 0.7491 in external validation set, respectively. Multiparametric radiomic model showed significantly better diagnostic performance after DeLong analysis, especially in classifying IDHwt and IDHmut-noncodel subtypes. Radiomic features from DWI could bring additive value and improve the performance of conventional MRI-based radiomic model for classifying the molecular subtypes especially IDHmut-noncodel and IDHwt of adult gliomas. Stage 2.

Proteogenomics of diffuse gliomas reveal molecular subtypes associated with specific therapeutic targets and immune-evasion mechanisms

Diffuse gliomas are devastating brain tumors. Here, we perform a proteogenomic profiling of 213 retrospectively collected glioma tumors. Proteogenomic analysis reveals the downstream biological events leading by EGFR-, IDH1-, TP53-mutations. The comparative analysis illustrates the distinctive features of GBMs and LGGs, indicating CDK2 inhibitor might serve as a promising drug target for GBMs. Further proteogenomic integrative analysis combined with functional experiments highlight the cis-effect of EGFR alterations might lead to glioma tumor cell proliferation through ERK5 medicates nucleotide synthesis process. Proteome-based stratification of gliomas defines 3 proteomic subgroups (S-Ne, S-Pf, S-Im), which could serve as a complement to WHO subtypes, and would provide the essential framework for the utilization of specific targeted therapies for particular glioma subtypes. Immune clustering identifies three immune subtypes with distinctive immune cell types. Further analysis reveals higher EGFR alteration frequencies accounts for elevation of immune check point protein: PD-L1 and CD70 in T-cell infiltrated tumors.

Cellular migration in IDH-wildtype glioblastoma: a case series on the genomics of amoeboid movement and its prognostic significance

Glioblastoma is an aggressive, fatal form of adult diffuse glioma. The ability of glioblastoma cells to switch to an amoeboid-like mode of invasion contributes greatly to tumour aggressiveness,1 though the prognostic value of genes related to amoeboid migration are not well characterised. Aims: To explore genes involved in amoeboid-related events (AREs) and determine differences in the frequency of genomic alterations between different adult glioma subgroups. Methods: An extensive literature review was conducted to identify genes involved in AREs. The genomic alteration frequency of identified ARE-associated genes were compared between low and high-grade glioma cohorts (LGG and HGG, respectively), stratified by their IDH1/2 mutation status. The prognostic significance of ARE-genes was also analysed using the HGG cohort. Results: Overall, 273 genes related to AREs were identified and subtyped. ARE-associated genomic alterations were significantly more common in IDH-mutant HGG, relative to IDH-wildtype HGG (p=0.048). Survival analyses indicated that novel ARE-associated genes, PIK3R6, PLA2G2F, PLA2G4B, PLA2G4F, PNPLA3, ARPC4 and WNT8A, may have prognostic value in HGG patients. Of interest, MAP2K2, was associated with longer overall survival in HGG patients. Conclusion: ARE-associated genes show varying genomic alterations between different adult glioma subtypes and represent an exciting opportunity for further investigation. Reference 1. Koh I, Cha J, Park J, et al. The mode and dynamics of glioblastoma cell invasion into a decellularized tissue-derived extracellular matrix-based three-dimensional tumor model. Sci Rep 2018; 8: 4608.

Anti-Vascular Endothelial Growth Factor Therapy Abolishes Glioma-Associated Endothelial Cell-Induced Tumor Invasion.

Tumor-remodeled endothelial cells not only facilitate the formation of tumor angiogenesis but also promote tumorigenesis. In this study, we aimed to explore the interaction between glioma-associated endothelial cells (GAEs) and glioma cells. We found that different subtypes of glioma owned distinct GAE abundance. Glioma patients with high GAE abundance exhibited poor prognosis. Both the results of the bioinformatics analysis and the in vitro co-culture system assay revealed that GAE promoted glioma cell invasion. Besides, anti-vascular endothelial growth factor (VEGF) therapy partially abolished the effects of GAE on gliomas. Moreover, anti-VEGF therapy upregulated IL-2 expression in GAE, and exogenous IL-2 administration inhibits GAE-induced glioma cell invasion. Collectively, our present study provides a novel outstanding of the interaction between GAE and glioma cells.

Correlation between ITGB2 expression and clinical characterization of glioma and the prognostic significance of its methylation in low-grade glioma(LGG).

Glioma is the most common primary tumor in the brain.Integrin beta 2(ITGB2) is a member of the leukocyte integrin family (leukocyte integrin), participating in lymphocyte recycling and homing, cell adhesion, and cell surface-mediated signal transduction. However, few studies on ITGB2 in gliomas have been reported yet.This study first discussed the relationship between ITGB2 expression and clinical characterization of glioma and the prognostic significance of its methylation in low-grade glioma. We collected Clinical data and transcription of glioma patients from TCGA, CGGA, and Rembrant datasets to analyze the differential expression of ITGB2 mRNA in glioma tissues and normal tissues. The box polts to evaluated the expression patterns of ITGB2 in different molecular subtypes. Receiver operating characteristic curve (ROC) were used to evaluate and verify the reliability of the model. Kaplan-Meier survival curves to evaluated the relationship between the level of ITGB2 mRNA expression and overall survival (OS). Using cox regression analysis to verify the ability of ITGB2 as an independent predictor of OS in glioma patients. We use TIMER to analyze and visualize the association between immune infiltration levels and a range of variables. The methylation of GBMLGG patients were obtained from the TCGA database through the biological portal. ITGB2 can be a potential marker for mesenchymal molecular subtype gliomas. COX regression analysis shows that ITGB2 is an independent predictive marker of OS in malignant glioma patients. Biological processes show that ITGB2 has involved glioma immune-related activities, especially closely related to B cells, CD4+Tcells, macrophages, neutrophils, and dendritic cells. ITGB2 is negatively regulated by ITGB2 methylation, resulting in low expression in LGG tissues. Low expression of ITGB2 and high methylation indicate good OS in patients with LGG. The ITGB2 methylation risk score (ITMRS) obtained from the ITGB2 methylation CpG site can better predict the OS of LGG patients. We used univariate and multivariate cox regression analysis of methylationsites, used the R language predict function to obtain the risk score of these ITGB2 methylation sites(ITMRS). ITGB2 can be used as a potential marker of mesenchymal molecular subtypes of gliomas and as an independent predictive marker of OS in patients with malignant gliomas. The ITMRS we established can be used as an independent prognostic factor for LGG and provide a new idea for the diagnosis and treatment of LGG.

Combining Multi-Shell Diffusion with Conventional MRI Improves Molecular Diagnosis of Diffuse Gliomas with Deep Learning.

The WHO classification since 2016 confirms the importance of integrating molecular diagnosis for prognosis and treatment decisions of adult-type diffuse gliomas. This motivates the development of non-invasive diagnostic methods, in particular MRI, to predict molecular subtypes of gliomas before surgery. At present, this development has been focused on deep-learning (DL)-based predictive models, mainly with conventional MRI (cMRI), despite recent studies suggesting multi-shell diffusion MRI (dMRI) offers complementary information to cMRI for molecular subtyping. The aim of this work is to evaluate the potential benefit of combining cMRI and multi-shell dMRI in DL-based models. A model implemented with deep residual neural networks was chosen as an illustrative example. Using a dataset of 146 patients with gliomas (from grade 2 to 4), the model was trained and evaluated, with nested cross-validation, on pre-operative cMRI, multi-shell dMRI, and a combination of the two for the following classification tasks: (i) IDH-mutation; (ii) 1p/19q-codeletion; and (iii) three molecular subtypes according to WHO 2021. The results from a subset of 100 patients with lower grades gliomas (2 and 3 according to WHO 2016) demonstrated that combining cMRI and multi-shell dMRI enabled the best performance in predicting IDH mutation and 1p/19q codeletion, achieving an accuracy of 75 ± 9% in predicting the IDH-mutation status, higher than using cMRI and multi-shell dMRI separately (both 70 ± 7%). Similar findings were observed for predicting the 1p/19q-codeletion status, with the accuracy from combining cMRI and multi-shell dMRI (72 ± 4%) higher than from each modality used alone (cMRI: 65 ± 6%; multi-shell dMRI: 66 ± 9%). These findings remain when we considered all 146 patients for predicting the IDH status (combined: 81 ± 5% accuracy; cMRI: 74 ± 5%; multi-shell dMRI: 73 ± 6%) and for the diagnosis of the three molecular subtypes according to WHO 2021 (combined: 60 ± 5%; cMRI: 57 ± 8%; multi-shell dMRI: 56 ± 7%). Together, these findings suggest that combining cMRI and multi-shell dMRI can offer higher accuracy than using each modality alone for predicting the IDH and 1p/19q status and in diagnosing the three molecular subtypes with DL-based models.

Olig1/2-Expressing Intermediate Lineage Progenitors Are Predisposed to PTEN/p53-Loss-Induced Gliomagenesis and Harbor Specific Therapeutic Vulnerabilities.

Multiple progenitor-state mutagenesis reveal that Olig1/2-expressing intermediate precursors are highly susceptible to PTEN/p53-loss-mediated transformation and impart differential drug sensitivity, indicating tumor-initiating cell states and genetic drivers dictate glioma phenotypes and drug responses. See related commentary by Zamler and Hu, p. 807.

Deep Learning-Assisted Quantitative Susceptibility Mapping as a Tool for Grading and Molecular Subtyping of Gliomas.

The online version contains supplementary material available at 10.1007/s43657-022-00087-6.

Implications of BRAF V600E mutation in gliomas: Molecular considerations, prognostic value and treatment evolution.

Gliomas are molecularly heterogeneous brain tumors responsible for the most years of life lost by any cancer. High-grade gliomas have a poor prognosis and despite multimodal treatment including surgery, radiotherapy, and chemotherapy, exhibit a high recurrence rate. There is a need for new therapeutic approaches based on precision medicine informed by biomarker assessment and BRAF, a key regulator of MAPK signaling pathway, influencing cell differentiation, proliferation, migration and pro-tumorigenic activity, is emerging as a promising molecular target. V600E, is the most frequent BRAF alteration in gliomas, especially in pediatric low-grade astrocytomas, pleomorphic xanthoastrocytoma, papillary craniopharyngioma, epithelioid glioblastoma and ganglioglioma. The possible application of BRAF-targeted therapy in gliomas is continuously growing and there is preliminary evidence of prolonged disease control obtained by BRAF inhibitors in tumors harboring BRAF V600E mutation. The possibility of introducing targeted therapies into the treatment algorithm represents a paradigm shift for patients with BRAF V600E mutant recurrent high-grade and low-grade glioma and BRAF routine testing should be considered in clinical practice. The focus of this review is to summarize the molecular landscape of BRAF across glioma subtypes and the novel therapeutic strategies for BRAF V600E mutated tumors.

Epigenetically regulated lncRNAs dissect the intratumoural heterogeneity and facilitate immune evasion of glioblastomas.

Background: Glioblastomas are the most common and malignant central nervous system (CNS) tumors that occupied a highly heterogeneous tumor microenvironment (TIME). Long noncoding RNAs (lncRNAs), whose expression can be modified by DNA methylation, are emerging as critical regulators in the immune system. However, knowledge about the epigenetic changes in lncRNAs and their contribution to the immune heterogeneity of glioma is still lacking. Methods: In this study, we integrated paired methylome and transcriptome datasets of glioblastomas and identified 2 robust immune subtypes based on lncRNA methylation features. The immune characteristics of glioma subtypes were compared. Furthermore, immune-related lncRNAs were identified and their relationships with immune evasion were evaluated. Results: Glioma immunophenotypes exhibited distinct immune-related characteristics as well as clinical and epigenetic features. 149 epigenetically regulated (ER) lncRNAs were recognized that possessed inverse variation in epigenetic and transcriptional levels between glioma subtypes. Immune-related lncRNAs were further identified through the investigation of their correlation with immune cell infiltrations and immune-related pathways. In particular, the 'Hot' glioma subtype with higher immunoactivity while a worse survival outcome was found to character immune evasion features. We finally prioritized candidate ER lncRNAs associated with immune evasion markers and response to glioma immunotherapy. Among them, CD109-AS1 and LINC02447 were validated as novel immunoevasive biomarkers for glioma through in vitro experiments. Conclusion: In summary, our study systematically reveals the crosstalk among DNA methylation, lncRNA, and immune regulation in glioblastomas, and will facilitate the development of epigenetic immunotherapy approaches.

Leptomeningeal disease in histone-mutant gliomas.

The 2016 WHO classification described a subtype of midline gliomas harboring histone 3 (H3) K27M alterations, and the 2021 edition added a new subtype of hemispheric diffuse gliomas with H3 G34R/V mutations. The incidence and clinical behavior of leptomeningeal disease (LMD) in these patients is not well defined. Retrospective study of patients with H3-altered gliomas diagnosed from 01/2012 to 08/2021; histone mutations were identified through next-generation sequencing (NGS) of tumor biopsy and/or cerebrospinal fluid (CSF). We identified 42 patients harboring H3 mutations (K27M mutations in 33 patients, G34R/V in 8, and both in one). Median age was 21 (4-70); 27 were male. LMD was diagnosed in 21/42 (50%) patients, corresponding to a 3-year cumulative incidence of 44.7% (95% confidence interval (CI): 26.1%-63.4%) for the K27-mutant group and a 1-year cumulative incidence of 37.5% in the G34-mutant group (95% CI: 0.01%-74.4%; no events after 1 year). Median time from tumor diagnosis to LMD was 12.9 months for H3-K27 patients and 5.6 months for H3-G34 patients. H3 mutation was detected in CSF in all patients with LMD who had NGS (8 H3-K27-mutant patients). In the H3-K27-mutant group, modeled risk of death was increased in patients who developed LMD (hazard ratio: 7.37, 95% CI: 2.98-18.23, P < .0001). In our cohort, 50% of patients developed LMD. Although further studies are needed, CSF ctDNA characterization may aid in identifying molecular tumor profiles in glioma patients with LMD, and neuroaxis imaging and CSF NGS should be considered for early LMD detection.

Analysis of complement system gene expression and outcome across the subtypes of glioma

Glioma is a malignant and poorly understood cancer that occurs in the glial cells of the brain. Gliomas consist of two clinical subcategories: low-grade glioma (LGG) and glioblastoma (GBM). Based on gene expression profile analysis, gliomas are also divided into four transcriptional subtypes and two mutational subclasses based on IDH expression. These subtypes are associated with differing prognoses, histology, and gene profiles. The complement system, a branch of the innate immune system traditionally associated with inflammation and opsonization, has various pro-tumor effects, including immunosuppression, maintenance of glial stem cells, and hypoxic signaling. We analyzed the expression of complement system genes across the transcriptional and IDH-mutational subtypes of LGG and GBM to determine whether these genes are differentially expressed concerning mutation status and transcriptional subclasses. We performed differential gene expression analysis and analyzed the results for gene set enrichments and correlations with outcome status. The results showed that complement system genes are differentially expressed with varying outcomes across different glioma subtypes. Within the transcriptional subtypes, the complement system genes tended to be overexpressed in subtypes with poor response to treatment or increased tumor malignancy. These results suggest that dysregulation of the complement system may significantly contribute to the categorization of transcriptional subtypes and further, may play a role in treatment response and/or overall patient outcome, although more research is needed to confirm this. These findings could help elucidate the interplay between the immune system, gene expression, and glioma pathogenesis.

The E3 ubiquitin ligase RBCK1: Implications in the tumor immune microenvironment and antiangiogenic therapy of glioma

E3 ubiquitin ligases (E3s) play a pivotal role in regulating the specificity of protein ubiquitination, and their significant functions as regulators of immune responses against tumors are attracting considerable interest. RBCK1—an RBR E3 ligase—is involved in immune regulation and tumor development. However, the potential effect of RBCK1 on glioma remains enigmatic. In the present study, we performed comprehensive analyses of multilevel data, which disclosed distribution characteristics of RBCK1 in pan-cancer, especially in glioma. Functional roles of RBCK1 were further confirmed using immunohistochemistry, cell biological assays, and xenograft experiments. Aberrant ascending of RBCK1 in multiple types of cancer was found to remodel the immunosuppressive microenvironment of glioma by regulating immunomodulators, cancer immunity cycles, and immune cell infiltration. Notably, the MES-like/RBCK1High cell population, a unique subset of cells in the microenvironment, suppressed T cell-mediated cell killing in glioma. Elevated expression levels of RBCK1 suggested a glioma subtype characterized by immunosuppression and hypo-responsiveness to immunotherapy but manifesting surprisingly increased responses to anti-angiogenic therapy. In conclusion, anti-RBCK1 target therapy might be beneficial for glioma treatment. Moreover, RBCK1 assisted in predicting molecular subtypes of glioma and response rates of patients to different clinical treatments, which could guide personalized therapy.

Retracted: Identification of Four Metabolic Subtypes of Glioma Based on Glycolysis-Cholesterol Synthesis Genes.

[This retracts the article DOI: 10.1155/2022/9448144.].

Classification and biomarker selection in lower-grade glioma using robust sparse logistic regression applied to RNA-seq data

Effective diagnosis and treatment in cancer is a barrier for the development of personalized medicine, mostly due to tumor heterogeneity. In the particular case of gliomas, highly heterogeneous brain tumors at the histological, cellular and molecular levels, and exhibiting poor prognosis, the mechanisms behind tumor heterogeneity and progression remain poorly understood. The recent advances in biomedical high-throughput technologies have allowed the generation of large amounts of molecular information from the patients that combined with statistical and machine learning techniques can be used for the definition of glioma subtypes and targeted therapies, an invaluable contribution to disease understanding and effective management.In this work sparse and robust sparse logistic regression models with the elastic net penalty were applied to glioma RNA-seq data from The Cancer Genome Atlas (TCGA), to identify relevant transcriptomic features in the separation between lower-grade glioma (LGG) subtypes and identify putative outlying observations. In general, all classification models yielded good accuracies, selecting different sets of genes. Among the genes selected by the models, TXNDC12, TOMM20, PKIA, CARD8 and TAF12 have been reported as genes with relevant role in glioma development and progression. This highlights the suitability of the present approach to disclose relevant genes and fosters the biological validation of non-reported genes.

Intraoperative detection of IDH-mutant glioma using fluorescence lifetime imaging.

Identifying isocitrate dehydrogenase (IDH)-mutation and glioma subtype during surgery instead of days later can aid in modifying tumor resection strategies for better survival outcomes. We report intraoperative identification of IDH-mutant glioma (N=12 patients) with a clinically compatible fluorescence lifetime imaging (FLIm) device (excitation: 355 nm; emission spectral bands: 390/40 nm, 470/28 nm, 542/50 nm). The fluorescence-derived parameters were analyzed to study the optical contrast between IDH-mutant tumors and surrounding brain tissue. IDH-mutant oligodendrogliomas exhibited shorter lifetimes (3.3 ± 0.1ns) than IDH-mutant astrocytomas (4.1 ± 0.1ns). Both IDH-mutant glioma subtypes had shorter lifetimes than white matter (4.6 ± 0.4ns) but had comparable lifetimes to cortex. Lifetimes also increased with malignancy grade within IDH-mutant oligodendrogliomas (grade 2: 2.96 ± 0.08 ns, grade 3: 3.4 ± 0.3ns) but not within IDH-mutant astrocytomas. The current results support the feasibility of FLIm as a surgical adjuvant for identifying IDH-mutant glioma tissue.

Classification of low- and high-grade gliomas using radiomic analysis of multiple sequences of MRI brain.

Gliomas are frequent tumors of brain parenchyma, which have histology similar to that of glial cells. Accurate glioma grading is required for determining clinical management. The background of this study is to investigate the accuracy of magnetic resonance imaging (MRI)-based radiomic features extracted from multiple MRI sequences in differentiating low and high-grade gliomas. This is a retrospective study. It includes two groups. Group A includes patients with confirmed histopathological diagnosis of low (23) and high-grade (58) gliomas from 2012 to 2020 were included. The MRI images were acquired using a Signa HDxt 1.5 Tesla MRI (GE Healthcare, Milwaukee, USA). Group B includes an external test set consisting of low- (20) and high-grade gliomas (20) obtained from The Cancer Genome Atlas (TCGA). The radiomic features were extracted from axial T2, apparent diffusion coefficient map, axial T2 fluid-attenuated inversion recovery, and axial T1 post-contrast sequences for both the groups. The Mann - Whitney U test was performed to assess the significant radiomic features useful for distinguishing the glioma grades for Group A. To determine the accuracy of radiomic features for differentiating gliomas, AUC was calculated from receiver operating characteristic curve analysis for both groups. Our study noticed in Group A, fourteen MRI-based radiomic features from four MRI sequences showed a significant difference (p < 0.001) in differentiating gliomas. In Group A, we noticed T1 post-contrast radiomic features such as first-order variance (FOV) (sensitivity - 94.56%, specificity - 97.51%, AUC - 0.969) and GLRLM long-run gray-level emphasis (sensitivity - 97.54%), specificity - 96.53%, AUC - 0.972) had the highest discriminative power for distinguishing the histological subtypes of gliomas. Our study noticed no statistical significant difference between ROC curves of significant radiomic features for both groups. In Group B, the T1 post-contrast radiomic features such as FOV (AUC-0.933) and GLRLM long-run gray-level emphasis (AUC-0.981) had also shown high discriminative power for distinguishing the gliomas. Our study concludes that MRI-based radiomic features extracted from multiple MRI sequences provide a non-invasive diagnosis of low- and high-grade gliomas and can be implemented in clinical settings for diagnosing the glioma grades.

Australian genome-wide association study confirms higher female risk for adult glioma associated with variants in the region of CCDC26.

Glioma accounts for approximately 80% of malignant adult brain cancer and its most common subtype, glioblastoma, has one of the lowest 5-year cancer survivals. Fifty risk-associated variants within 34 glioma genetic risk regions have been found by genome-wide association studies (GWAS) with a sex difference reported for 8q24.21 region. We conducted an Australian GWAS by glioma subtype and sex. We analyzed genome-wide data from the Australian Genomics and Clinical Outcomes of Glioma (AGOG) consortium for 7573692 single nucleotide polymorphisms (SNPs) for 560 glioma cases and 2237 controls of European ancestry. Cases were classified as glioblastoma, non-glioblastoma, astrocytoma or oligodendroglioma. Logistic regression analysis was used to assess the associations of SNPs with glioma risk by subtype and by sex. We replicated the previously reported glioma risk associations in the regions of 2q33.3 C2orf80, 2q37.3 D2HGDH, 5p15.33 TERT, 7p11.2 EGFR, 8q24.21 CCDC26, 9p21.3 CDKN2BAS, 11q21 MAML2, 11q23.3 PHLDB1, 15q24.2 ETFA, 16p13.3 RHBDF1, 16p13.3 LMF1, 17p13.1 TP53, 20q13.33 RTEL, and 20q13.33 GMEB2 (P < .05). We also replicated the previously reported sex difference at 8q24.21 CCDC26 (P=.0024) with the association being nominally significant for both sexes (P < .05). Our study supports a stronger female risk association for the region 8q24.21 CCDC26 and highlights the importance of analyzing glioma GWAS by sex. Abetter understanding of sex differences could provide biological insight into the cause of glioma with implications for prevention, risk prediction and treatment.