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Purpose: Lower-grade gliomas typically exhibit 5-aminolevulinic acid (5-ALA)-induced fluorescence in only 20–30% of cases, a rate that can be increased by doubling the administered dose of 5-ALA. Fluorescence can depict anaplastic foci, which can be precisely sampled to avoid undergrading. We aimed to analyze whether a deep learning model could predict intraoperative fluorescence based on preoperative magnetic resonance imaging (MRI). Methods: We evaluated a cohort of 163 glioma patients categorized intraoperatively as fluorescent (n = 83) or non-fluorescent (n = 80). The preoperative MR images of gliomas lacking high-grade characteristics (e.g., necrosis or irregular ring contrast-enhancement) consisted of T1, T1-post gadolinium, and FLAIR sequences. The preprocessed MRIs were fed into an encoder-decoder convolutional neural network (U-Net), pre-trained for tumor segmentation using those three MRI sequences. We used the outputs of the bottleneck layer of the U-Net in the Variational Autoencoder (VAE) as features for classification. We identified and utilized the most effective features in a Random Forest classifier using the principal component analysis (PCA) and the partial least square discriminant analysis (PLS-DA) algorithms. We evaluated the performance of the classifier using a tenfold cross-validation procedure. Results: Our proposed approach's performance was assessed using mean balanced accuracy, mean sensitivity, and mean specificity. The optimal results were obtained by employing top-performing features selected by PCA, resulting in a mean balanced accuracy of 80% and mean sensitivity and specificity of 84% and 76%, respectively. Conclusions: Our findings highlight the potential of a U-Net model, coupled with a Random Forest classifier, for pre-operative prediction of intraoperative fluorescence. We achieved high accuracy using the features extracted by the U-Net model pre-trained for brain tumor segmentation. While the model can still be improved, it has the potential for evaluating when to administer 5-ALA to gliomas lacking typical high-grade radiographic features. [ABSTRACT FROM AUTHOR]

Objective: The objective of this study was to assess the relationship between spermine synthase (SMS) expression, tumor occurrence, and prognosis in lower-grade gliomas (LGGs). Methods: A total of 523 LGG patients and 1152 normal brain tissues were included as controls. Mann-Whitney U test was performed to evaluate SMS expression in the LGG group. Functional annotation analysis was conducted to explore the biological processes associated with high SMS expression. Immune cell infiltration analysis was performed to examine the correlation between SMS expression and immune cell types. The association between SMS expression and clinical and pathological features was assessed using Spearman correlation analysis. In vitro experiments were conducted to investigate the effects of overexpressing or downregulating SMS on cell proliferation, apoptosis, migration, invasion, and key proteins in the protein kinase B (AKT)/epithelialmesenchymal transition signaling pathway. Results: The study revealed a significant upregulation of SMS expression in LGGs compared to normal brain tissues. High SMS expression was associated with certain clinical and pathological features, including older age, astrocytoma, higher World Health Organization grade, poor disease-specific survival, disease progression, non-1p/19q codeletion, and wild-type isocitrate dehydrogenase. Cox regression analysis identified SMS as a risk factor for overall survival. Bioinformatics analysis showed enrichment of eosinophils, T cells, and macrophages in LGG samples, while proportions of dendritic (DC) cells, plasmacytoid DC (pDC) cells, and CD8+ T cells were decreased. Conclusion: High SMS expression in LGGs may promote tumor occurrence through cellular proliferation and modulation of immune cell infiltration. These findings suggest the prognostic value of SMS in predicting clinical outcomes for LGG patients. [ABSTRACT FROM AUTHOR]

Background: Tractography allows the in vivo study of subcortical white matter, and it is a potential tool for providing predictive indices on post-operative outcomes. We aim at establishing whether there is a relation between cognitive outcome and the status of the inferior fronto-occipital fasciculus's (IFOF's) microstructure. Methods: The longitudinal neuropsychological data of thirty young (median age: 35 years) patients operated on for DLGG in the left temporo-insular cortex along with pre-surgery tractography data were processed. Results: A degraded integrity of the left (vs. right) IFOF (lower fractional anisotropy and length, p < 0.001; higher mean and axial diffusivity, p < 0.01) was found, with lower microstructural variables in the infiltration (vs. dislocation) group. Significant decreases immediately post-surgery vs. pre-surgery mainly occurred in lexico-semantics (p < 0.001), with significant improvements at follow-up in all the tests (p < 0.01 to p < 0.001), despite values in the range of 44% to 47.82% of patients with below cut-off scores regarding naming verbs and making visual lexical decisions. The status of left and right IFOFs is predictive of a decrease in immediate post-surgery performance for several tests (p < 0.05); similarly, it is predictive of better recovery in the follow-up performance for naming nouns, naming verbs, making phonological fluency lexical decisions, and the token test (p < −0.05). For the ROC analysis, a significant result was obtained for the verb-naming test, with a cut-off of 79%. Conclusions: This study supports the role of the predictive value of pre-operative tractography for assessing the immediate post-operative result and at follow-up the risk of developing a cognitive deficit. [ABSTRACT FROM AUTHOR]

Abstract Lower-grade gliomas (LGGs) exhibit diverse clinical behaviors and varying immune infiltration levels. Mitochondria have been implicated in numerous cancer pathogenesis and development, including LGGs. However, the precise biological functions of mitochondrial genes in shaping the immune landscape and the prognostic significance of LGGs remain elusive. Utilizing the Mito-Carta3.0 database, we curated a total of 1136 genes implicated in mitochondrial functions. By leveraging the expression profiles of 1136 genes related to mitochondria, we successfully categorized LGGs into four distinctive mitochondria-related transcriptome (MRT) subtypes. Our thorough analysis conclusively demonstrated that these subtypes exhibited marked disparities. To enable a personalized and integrated evaluation of LGG patients, we developed a prognostic signature known as MRT-related prognostic signature (MTRS). MTRS demonstrated correlation with mitochondria-related transcriptome (MRT) subtypes, allowing the assessment of patients’ prognosis and immune microenvironment. We conducted a detailed exploration of the single-cell distribution of MTRS in lower-grade gliomas and verified the core genes of MTRS within the spatial transcriptome of these tumors. Furthermore, our study pinpointed MGME1 as the pivotal gene in the model, functioning as an oncogene that exerts influence on cell proliferation and migration capabilities. Our research highlights the importance of mitochondrial transcriptomic features in LGGs, offering paths for tailored therapies.

Objective To evaluate the predictive value of T2-fluid attenuated inversion recovery (FLAIR) signal suppression rate for the short arm of chromosome 1 and long arm of chromosome 19 (1p/19q) molecular features in lower-grade gliomas (LGG), and to construct and verify the predictive model based on magnetic resonance imaging (MRI) tumor features and T2-FLAIR signal suppression rate. Methods Clincal and imaging data of the patients with pathologically confirmed supratentorial LGG (WHO grade 2~3) in our medical center from 2017 to 2021 were collected and retrospectively analyzed. According to the results of postoperative molecular pathology, they were divided into 1p/19q-codeleted (1p/19q-Codel) and 1p/19q-noncodeleted (1p/19q-Noncodel) groups. MRI tumor features were blindly assessed by 2 neuroradiologists. Five circular regions of interest were respectively delineated in the tumor area and the normal-appearing white matter in contralateral semioval center using the hot-spot method in order to calculate the T2-FLAIR signal suppression rate. The differences of clinical features, MRI tumor features and T2-FLAIR signal suppression rate were analyzed between the 2 groups. Univariate and multivariate logistic regression analyses were used to screen independent predictors and constructa predictive model and nomogram. Receiver operating characteristic (ROC) curve, calibration curve and Hosmer-Lemeshow test were applied to assess the model performance, and the model was internally validated by bootstrap method. Results A total of 146 supratentorial LGG patients were enrolled, including 68 being assigned into the 1p/19q-Codel group and 78 into the 1p/19q-Noncodel group. The T2-FLAIR signal suppression rate was 0.43 (0.28, 0.62) in the 1p/19q-Noncodel group, which was significantly higher than that in the 1p/19q-Codel group [0.29 (0.24, 0.35), P0.374 (P0.374 in predicting 1p/19q-Noncodel was 0.720, the sensitivity was 60.26% and the specificity was 83.82%. DeLong test indicated that T2-FLAIR signal suppression rate >0.374 was more effective than T2-FLAIR mismatch sign in predicting 1p/19q molecular features (P0.374 combined with cortex infiltration and calcification had good performance, with an AUC value of 0.808, and the AUC value verified internally by bootstrap method was 0.807. At the same time, the calibration and goodness of fit of the model were good. Conclusion T2-FLAIR signal suppression rate can be used as a quantitative imaging marker to predict 1p/19q-Noncodel LGG. The predictive model with T2-FLAIR signal suppression rate >0.374 combined with cortex infiltration and calcification can effectively predict 1p/19q molecular features.

Lower-grade gliomas (LGGs) exhibit diverse clinical behaviors and varying immune infiltration levels. Mitochondria have been implicated in numerous cancer pathogenesis and development, including LGGs. However, the precise biological functions of mitochondrial genes in shaping the immune landscape and the prognostic significance of LGGs remain elusive. Utilizing the Mito-Carta3.0 database, we curated a total of 1136 genes implicated in mitochondrial functions. By leveraging the expression profiles of 1136 genes related to mitochondria, we successfully categorized LGGs into four distinctive mitochondria-related transcriptome (MRT) subtypes. Our thorough analysis conclusively demonstrated that these subtypes exhibited marked disparities. To enable a personalized and integrated evaluation of LGG patients, we developed a prognostic signature known as MRT-related prognostic signature (MTRS). MTRS demonstrated correlation with mitochondria-related transcriptome (MRT) subtypes, allowing the assessment of patients' prognosis and immune microenvironment. We conducted a detailed exploration of the single-cell distribution of MTRS in lower-grade gliomas and verified the core genes of MTRS within the spatial transcriptome of these tumors. Furthermore, our study pinpointed MGME1 as the pivotal gene in the model, functioning as an oncogene that exerts influence on cell proliferation and migration capabilities. Our research highlights the importance of mitochondrial transcriptomic features in LGGs, offering paths for tailored therapies. [ABSTRACT FROM AUTHOR]

Objectives: This study aimed to explore the expression, clinical significance, and functional mechanism of FYN in lower-grade gliomas (LGG). Methods: The mRNA and protein expression of FYN in LGG tissues were detected using databases including OncoLnc, GEPIA, and Human protein atlas (HPA). The UCSC Xena browser, TIMER, STRING and Metascape databases were used to investigate Kaplan–Meier survival curves, correlations between FYN expression and various types of immune cell infiltration, protein interaction network and possible functional mechanism. Results: FYN expression in LGG, IDH mutation or 1p19q co-deletion subgroup was significantly higher than in corresponding control groups (p < 0.05). Patients with higher FYN expression had longer overall survival (p < 0.05). Male or no 1p19q co-deletion groups with higher FYN expression also had longer overall survival (p < 0.05). FYN expression had close correlation with infiltrating levels of cell purity, CD4+T cells, macrophages, and CD8+T cells (p < 0.05). Protein interaction network result showed correlation among FYN, SH2D1A, LCK, CAV1, SRC, CBL and PTK2. Functional enrichment analysis revealed that FYN and its related genes mainly participated in bacterial invasion of epithelial cells and natural killer cell mediated cytotoxicity. Peptidyl-tyrosine phosphorylation, negative regulation of anoikis, immune effector process, transmembrane receptor protein tyrosine kinase signaling pathway, epidermal growth factor receptor signaling pathway, and negative regulation of protein modification process may be the critical biological process. Conclusions: FYN is up-expressed in LGG and related to its good prognosis. It participated in tumor pathophysiological processes and may be a therapeutic target for LGG. [ABSTRACT FROM AUTHOR]

Recent researches reported that neurotrophins can promote glioma growth/invasion but the relevant model for predicting patients' survival in Lower-Grade Gliomas (LGGs) lacked. In this study, we adopted univariate Cox analysis, LASSO regression, and multivariate Cox analysis to determine a signature including five neurotrophin-related genes (NTGs), CLIC1, SULF2, TGIF1, TTF2, and WEE1. Two-sample Mendelian Randomization (MR) further explored whether these prognostic-related genes were genetic variants that increase the risk of glioma. A total of 1306 patients have been included in this study, and the results obtained from the training set can be verified by four independent validation sets. The low-risk subgroup had longer overall survival in five datasets, and its AUC values all reached above 0.7. The risk groups divided by the NTGs signature exhibited a distinct difference in targeted therapies from the copy-number variation, somatic mutation, LGG's surrounding microenvironment, and drug response. MR corroborated that TGIF1 was a potential causal target for increasing the risk of glioma. Our study identified a five-NTGs signature that presented an excellent survival prediction and potential biological function, providing new insight for the selection of LGGs therapy. [ABSTRACT FROM AUTHOR]

Introduction: The tumor microenvironment and IRGs are highly correlated with tumor occurrence, progression, and prognosis. However, their roles in grade II and III gliomas, termed LGGs in this study, remain to be fully elucidated. Our research aims to develop immune-related features for risk stratification and prognosis prediction in LGG. Methods: Using the ssGSEA method, we assessed the immune characteristics of the LGG population. We conducted differential analysis using LGG samples from the TCGA database and normal samples from GTEx, identifying 412 differentially expressed immune-related genes (DEIRGs). Subsequently, we utilized univariate Cox, LASSO, and multivariate Cox regression analyses to establish both a gene predictive model and a nomogram predictive model. Results: Here, we found that the ESTIMATE score, immune score and stromal score of high-immunity, high-grade and isocitrate dehydrogenase (IDH) wild-type glioma were higher than those of the corresponding group, and the tumor purity was lower. Higher ESTIMATE scores, stromal scores and immune scores indicated a poor prognosis in patients with LGG. Our four-gene prognostic model demonstrated superior accuracy compared to other molecular features. Validation using the CGGA as a testing set and the combined TCGA and CGGA cohort confirmed its robust prognostic value. Additionally, a nomogram integrating the prognostic model and clinical variables showed enhanced predictive capability. Discussion: Our study highlights the prognostic significance of the identified four DEIRGs (KLRC3, MR1, PDIA2, and RFXAP) in LGG patients. The predictive model and nomogram developed herein offer valuable tools for personalized treatment strategies in LGG. Future research should focus on further validating these findings and exploring the functional roles of these DEIRGs within the LGG tumor microenvironment. [ABSTRACT FROM AUTHOR]

Background: Supported self‐management can improve clinical and psychosocial outcomes in people with cancer; the considerations required to implement self‐management support (SMS) for people living with a lower‐grade glioma (LGG)—who often have complex support needs—are not known. We aimed to identify and understand these implementation considerations through the lens of normalisation process theory (NPT), from the perspectives of healthcare professionals (HCP) and people with LGG. Methods: We conducted semistructured interviews with HCPs who support adults with brain tumours (n = 25; 12 different healthcare professions), and people with LGG who had completed primary treatment (n = 28; male n = 16, mean age 54.6 years, mean time since diagnosis 8.7 years), from across the United Kingdom. Interviews were transcribed and inductive open coding conducted, before deductively mapping to constructs of NPT. We first mapped HCP data, then integrated data from people with LGG to explore alignment in experiences and perspectives. Results: We generated supporting evidence for all four NPT constructs and related subconstructs, namely: 'Coherence', 'Cognitive participation', 'Collective action' and 'Reflexive monitoring'. Data from HCPs and people with LGG clearly demonstrated that effective SMS constitutes a collective activity. Key implementation considerations included: ensuring awareness of, and access to, support; building strong HCP‐support recipient relationships; and careful inclusion of close family and friends. We identified pertinent challenges, such as identifying support needs (influenced by the extent to which those with LGG engage in help‐seeking), resistance to support (e.g., technology literacy), training for HCPs and HCP cooperation. Conclusions: This study demonstrates the collective nature of, and provides insight into the individual roles within, supported self‐management. We outline considerations to operationalise, sustain and appraise the implementation of SMS for people with LGG. Patient or Public Contribution: People with brain tumours, and informal caregivers, were involved in the development of information materials and topic guides to ensure accessibility and pertinence. They also had opportunities to comment on interview findings. [ABSTRACT FROM AUTHOR]

Background: For adult patients with grade 1–3 gliomas, identifying patients with an indication for proton therapy (PT) can be challenging due to sparse evidence supporting its benefits. In this study, we aimed to ensure national consensus and develop a decision support tool to aid clinicians in identifying patients with grade 1–3 gliomas eligible for PT. Methods: Sixty-one historic patients referred for postoperative radiotherapy for glioma grade 1–3 were included in this study and had new photon therapy and PT plans calculated. These plans along with clinical parameters were presented to neurooncologists with experience in treating brain tumours. The patients were presented at three workshops (WSs), where each neurooncologist individually had to choose between photon and proton therapy. Important parameters were selected using cross validation. Multivariable logistic regression was used to predict the neurooncologists’ treatment modality choice. Results: At the three WSs 23, 24 and 19 randomly selected patients were presented. Seventy-five percent of the neurooncologists agreed for 14 patients (61%), 16 patients (67%) and 15 patients (79%) at WS1, WS2 and WS3. Age at radiotherapy and difference in mean dose (ΔDmean) to the residual brain were significant predictors of the choice of treatment modality, p

Abstract Lower-grade gliomas (LGGs) exhibit highly variable clinical behaviors, while classic histology characteristics cannot accurately reflect the authentic biological behaviors, clinical outcomes, and prognosis of LGGs. In this study, we carried out analyses of whole exome sequencing, RNA sequencing and DNA methylation in primary vs. recurrent LGG samples, and also combined the multi-omics data to construct a prognostic prediction model. TCGA-LGG dataset was searched for LGG samples. 523 samples were used for whole exome sequencing analysis, 532 for transcriptional analysis, and 529 for DNA methylation analysis. LASSO regression was used to screen genes with significant association with LGG survival from the frequently mutated genes, differentially expressed genes, and differentially methylated genes, whereby a prediction model for prognosis of LGG was further constructed and validated. The most frequently mutated diver genes in LGGs were IDH1 (77%), TP53 (48%), ATRX (37%), etc. Top significantly up-regulated genes were C6orf15, DAO, MEOX2, etc., and top significantly down-regulated genes were DMBX1, GPR50, HMX2, etc. 2077 genes were more and 299 were less methylated in recurrent vs. primary LGG samples. Thirty-nine genes from the above analysis were included to establish a prediction model of survival, which showed that the high-score group had a very significantly shorter survival than the low-score group in both training and testing sets. ROC analysis showed that AUC was 0.817 for the training set and 0.819 for the testing set. This study will be beneficial to accurately predict the survival of LGGs to identify patients with poor prognosis to take specific treatment as early, which will help improve the treatment outcomes and prognosis of LGG.

Lower-grade (grade 2–3) gliomas (LGGs) constitutes a group of primary brain tumors with variable clinical behaviors and treatment responses. Recent advancements in molecular biology have redefined their classification, and novel imaging modalities emerged for the noninvasive diagnosis and follow-up. This review comprehensively analyses the current knowledge on molecular and imaging biomarkers in LGGs. Key molecular alterations, such as IDH mutations and 1p/19q codeletion, are discussed for their prognostic and predictive implications in guiding treatment decisions. Moreover, the authors explore theranostic biomarkers for the potential of tailored therapies. Additionally, they also describe the utility of advanced imaging modalities, including widely available techniques, as dynamic susceptibility contrast perfusion-weighted imaging and less validated, emerging approaches, for the noninvasive LGGs characterization and follow-up. The integration of molecular markers enhanced the stratification of LGGs, leading to the new concept of integrated histomolecular classification. While the IDH mutation is an established key prognostic and predictive marker, recent results from IDH inhibitors trials showed its potential value as a theranostic marker. In this setting, advanced MRI techniques such as 2-D-hydroxyglutarate spectroscopy are very promising for the noninvasive diagnosis and monitoring of LGGs. This progress offers exciting prospects for personalized medicine and improved treatment outcomes in LGGs. [ABSTRACT FROM AUTHOR]

Abstract Background As a component of membrane lipids and the precursor of oxysterols and steroid hormones, reprogrammed cholesterol metabolism contributes to the initiation and progression of multiple cancers. Thus, we aim to further investigate the significances of cholesterol metabolism in lower-grade gliomas (LGGs). Methods The present study included 413 LGG samples from TCGA RNA-seq dataset (training cohort) and 172 LGG samples from CGGA RNA-seq dataset (validation cohort). The cholesterol metabolism-related signature was identified by the LASSO regression model. Bioinformatics analyses were performed to explore the functional roles of this signature in LGGs. Kaplan-Meier and Cox regression analyses were enrolled to estimate prognostic value of the risk signature. Results Our findings suggested that cholesterol metabolism was tightly associated clinicopathologic features and genomic alterations of LGGs. Bioinformatics analyses revealed that cholesterol metabolism played a key role in immunosuppression of LGGs, mainly by promoting macrophages polarization and T cell exhaustion. Kaplan-Meier curve and Cox regression analysis showed that cholesterol metabolism was an independent prognostic indicator for LGG patients. To improve the clinical application value of the risk signature, we also constructed a nomogram model to predict the 1-, 3- and 5-year survival of LGG patients. Conclusion The cholesterol metabolism was powerful prognostic indicator and could serve as a promising target to enhance personalized treatment of LGGs.

Lower-grade gliomas (LGG) can eventually progress to glioblastoma (GBM) and death. In the context of the transfer learning approach, we aimed to train and test an MRI-based radiomics model for predicting survival in GBM patients and validate it in LGG patients. From each patient's 704 MRI-based radiomics features, we chose seventeen optimal radiomics signatures in the GBM training set (n = 71) and used these features in both the GBM testing set (n = 31) and LGG validation set (n = 107) for further analysis. Each patient's risk score, calculated based on those optimal radiomics signatures, was chosen to represent the radiomics model. We compared the radiomics model with clinical, gene status models, and combined model integrating radiomics, clinical, and gene status in predicting survival. The average iAUCs of combined models in training, testing, and validation sets were respectively 0.804, 0.878, and 0.802, and those of radiomics models were 0.798, 0.867, and 0.717. The average iAUCs of gene status and clinical models ranged from 0.522 to 0.735 in all three sets. The radiomics model trained in GBM patients can effectively predict the overall survival of GBM and LGG patients, and the combined model improved this ability. [ABSTRACT FROM AUTHOR]

Purpose: IDH-wildtype (IDH-wt) diffuse gliomas with histological features of lower-grade gliomas (LGGs) are rare and heterogeneous primary brain tumours. [11C]Methionine (MET) positron emission tomography (PET) is commonly used to evaluate glial neoplasms at diagnosis. The present study aimed to assess the prognostic value of MET PET in newly diagnosed, treatment naïve IDH-wt gliomas with histological features of LGGs. Methods: Patients with a histological diagnosis of IDH-wt LGG who underwent preoperative (< 100 days) MET PET/CT and surgery were retrospectively included. Qualitative and semi-quantitative analyses of MET PET images were performed. Progression-free survival (PFS) and overall survival (OS) were analysed by Kaplan–Meier curves. Cox proportional-hazards regression was used to test the association of imaging and clinical data to PFS and OS. Results: We included 48 patients (M:F = 25:23; median age 55). 39 lesions were positive and 9 negative at MET PET. Positive MET PET was significantly associated with shorter median PFS (15.7 months vs. not reached, p = 0.0146) and OS time (32.6 months vs. not reached, p = 0.0253). Incomplete surgical resection and higher TBRmean values were independent predictors of shorter PFS on multivariate analysis (p < 0.001 for both). Higher tumour grade and incomplete surgical resection were independent predictors of OS at multivariate analysis (p = 0.027 and p = 0.01, respectively). Conclusion: MET PET is useful for the prognostic stratification of patients with IDH-wt glial neoplasms with histological LGGs features. Considering their huge biological heterogeneity, the combination of MET PET and molecular analyses may help to improve the prognostic accuracy in these diffuse gliomas subset and influence therapeutic choices accordingly. [ABSTRACT FROM AUTHOR]

Background: As a component of membrane lipids and the precursor of oxysterols and steroid hormones, reprogrammed cholesterol metabolism contributes to the initiation and progression of multiple cancers. Thus, we aim to further investigate the significances of cholesterol metabolism in lower-grade gliomas (LGGs). Methods: The present study included 413 LGG samples from TCGA RNA-seq dataset (training cohort) and 172 LGG samples from CGGA RNA-seq dataset (validation cohort). The cholesterol metabolism-related signature was identified by the LASSO regression model. Bioinformatics analyses were performed to explore the functional roles of this signature in LGGs. Kaplan-Meier and Cox regression analyses were enrolled to estimate prognostic value of the risk signature. Results: Our findings suggested that cholesterol metabolism was tightly associated clinicopathologic features and genomic alterations of LGGs. Bioinformatics analyses revealed that cholesterol metabolism played a key role in immunosuppression of LGGs, mainly by promoting macrophages polarization and T cell exhaustion. Kaplan-Meier curve and Cox regression analysis showed that cholesterol metabolism was an independent prognostic indicator for LGG patients. To improve the clinical application value of the risk signature, we also constructed a nomogram model to predict the 1-, 3- and 5-year survival of LGG patients. Conclusion: The cholesterol metabolism was powerful prognostic indicator and could serve as a promising target to enhance personalized treatment of LGGs. [ABSTRACT FROM AUTHOR]

Aquaporin-4 (AQP4) is a significant factor in transcellular and transepithelial water movement, and abnormal expression of AQP4 has been detected in many types of tumors. The purpose of this study was to explore its role in low-grade gliomas (LGG) using freely available online bioinformatics tools. OncoLnc database was used to analyze Cox coefficients and compare AQP4 expression between various types of tumors; Tumor Immune Estimation Resource database and Gene Expression Profiling Interactive Analysis were used to compare gene expression between LGG and normal tissues; University of California Santa Cruz Xena browser generated Kaplan-Meier survival curves in the LGG cohort in The Cancer Genome Atlas and subgroups; LinkedOmics database screened the most relevant genes based on Pearson correlation coefficient; Gene Ontology Biological Process and Kyoto Encyclopedia of Genes and Genomes enrichment analysis were performed through gene set enrichment analysis to explore possible molecular mechanisms. LGG had higher AQP4 expression compared with normal tissues and ranked first among 21 different types of cancer (P < 0.05). The oligodendroglioma group had the lowest AQP4 expression and the longest overall survival (OS) (P < 0.05). LGG with astrocytoma, isocitrate dehydrogenase mutation, or 1p/19q codeletion had lower AQP4 expression and longer OS (P < 0.001). LGG with lower AQP4 expression, without 1p/19q codeletion, without chemotherapy, and with or without radiation therapy had longer OS (P < 0.05). AQP4 and coexpressed genes were involved in complex biological processes in LGG, including regulation of neurotransmitter level, peroxisome proliferator-activated receptor signaling pathway, cell adhesion molecules, and others. AQP4 is a prognostic marker in LGG and its subgroups. Patients with lower AQP4 expression may have longer OS. [ABSTRACT FROM AUTHOR]

Objective: IDH-mutant lower-grade gliomas (LGGs, grade 2 or 3) eventually transform into secondary grade 4 astrocytomas (sAIDHmut/G4). Here, we sought to describe the transformation time, risk factors, and outcomes in malignant transformation of IDH-mutant LGGs. Methods: We screened data for 108 patients with sAIDHmut/G4 in the Chinese Glioma Genome Atlas who had initial IDH-mutant LGGs and underwent reoperation during 2005–2021. We evaluated the transformation time from IDH-mutant LGGs to sAIDHmut/G4, and associated risk factors and outcomes. Malignant transformation was defined as pathological confirmation of grade 4 astrocytoma. Results: The median age of the 108 patients with IDH-mutant LGGs was 35 years (range, 19–54); the median age at transformation was 40 years (range, 25–62); and the median follow-up time for all patients was 146 months (range, 121–171). The average transformation time was 58.8 months for all patients with LGGs (range, 5.9–208.1); 63.5 and 51.9 months for grade 2 and 3 gliomas, respectively; and 58.4 and 78.1 months for IDH-mutant/1p/19q-non-codeleted astrocytomas and IDH-mutant/1p/19q-codeleted oligodendrogliomas, respectively. Univariate and multivariate analysis indicated that radiotherapy [hazard ratio (HR), 0.29; 95% confidence interval (CI), 0.137–0.595; P = 0.001] and non-A blood type (HR, 0.37; 95% CI, 0.203–0.680; P = 0.001) were protective factors against delayed malignant transformation. Radiotherapy was associated with improved survival after transformation (HR, 0.44; 95% CI, 0.241–0.803; P = 0.008), overall survival (HR, 0.50; 95% CI, 0.265–0.972; P = 0.041), and progression-free survival (HR, 0.25; 95% CI, 0.133–0.479; P < 0.0001) in patients with IDH-mutant gliomas. Conclusions: Radiotherapy is associated with delayed malignant transformation and improved survival in patients with IDH-mutant gliomas.

Abstract Background The therapeutic effects of conventional treatment on gliomas are not promising. The tumor microenvironment (TME) has a close association with the invasiveness of multiple types of tumors, including low‐grade gliomas (LGG). This study aims to validate the prognostic and immune‐related role of macrophage scavenger receptor 1 (MSR1) in LGG patients. Methods Data in this study were obtained from public databases. The differential expression of MSR1 was analyzed in LGG patients with different clinicopathological characteristics. Kaplan–Meier survival analysis, a time‐dependent receiver operating characteristic (ROC) curve, and Cox regression analysis were used to assess the prognostic value of MSR1. Differentially expressed genes (DEGs) were screened between the high and low expression groups of MSR1. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were used to annotate the function of these DEGs. Hallmark gene sets were identified based on MSR1 by Gene Set Enrichment Analysis (GSEA). Difference analysis and correlation analysis were used to study the relationship between MSR1 and TME‐related scores, tumor‐infiltrating immune cells (TIICs), immune‐related gene sets, and immune checkpoints (ICPs). The single‐cell sequencing data were processed to identify the cell types expressing MSR1. The quantification of TIICs in TME was calculated by single‐sample gene set enrichment analysis (ssGSEA). The differential expression of MSR1 in LGG and control brain tissues was verified by experiments. Results There were significant differences in the expression level of MSR1 in different types of tissues and cells. MSR1 has a high prognostic value in LGG patients and can be used as an independent prognostic factor. MSR1 is closely related to TME and may play an important role in the immunotherapy of LGG patients. Conclusions The result of our study demonstrated that MSR1 is an independent prognostic biomarker in LGG patients and may play an important role in the TME of LGGs.

This paper performed a retrospective analysis of 19 patients diagnosed with pathologically-verified WHO Ⅱ-Ⅲ grade glioma with T2/FLAIR mismatch sign. We extracted the overall characteristics of the tumor area and the image parameters of the mismatch regions from the patients' imaging data for quantitative analysis. Afterward, we investigated how T2/FLAIR mismatch sign affected the diagnostic efficacy for isocitrate dehydrogenase (IDH)-mutant and 1p/19q non-codeleted (IDHMUT/1p/19q+) lower-grade gliomas (LGG). This study showed that the overall and partial T2/FLAIR mismatch sign can be used as an imaging marker to predict IDHMUT/1p/19q+ LGG. The overall imaging characteristics of the tumor area combined with the quantitative parameters of the mismatch regions can improve the diagnostic efficacy for IDHMUT/1p/19q+ LGG.

Background: Cancer-associated fibroblasts (CAFs) are vital components of prominent cellular components in lower-grade gliomas (LGGs) that contribute to LGGs' progression, treatment resistance, and immunosuppression. Epigenetic modification and immunity have significant implications for tumorigenesis and development. Methods: We combined aberrant methylation and CAFs abundances to build a prognostic model and the impact on the biological properties of LGGs. Grouping based on the median CAFs abundances score of samples in the TCGA-LGGs dataset, differentially expressed genes and aberrantly methylated genes were combined for subsequent analysis. Results: We identified five differentially methylated and expressed genes (LAT32, SWAP70, GSAP, EMP3, and SLC2A10) and established a prognostic gene signature validated in the CGGA-LGGs dataset. Immunohistochemistry (IHC) and in vitro tests were performed to verify these expressions. The high-risk group increased in tumor-promoting immune cells and tumor mutational burden. Notably, risk stratification had different ICB sensitivities in LGGs, and there were also significant sensitivity differences for temozolomide and the other three novel chemotherapeutic agents. Conclusion: Our study reveals characteristics of CAFs in LGGs, refines the direct link between epigenetics and tumor stroma, and might provide clinical implications for guiding tailored anti-CAFs therapy in combination with immunotherapy for LGGs patients. [ABSTRACT FROM AUTHOR]

Background: Supratotal-Resection (SpTR) is a promising surgical strategy in Lower-grade gliomas (LGGs). SpTR assessment, feasibility and distinctive features, as well as clinical benefit at first and second surgery and on overall-survival must be better characterized. The critical percentage of resection exceeding FLAIR margins to obtain clinical benefit and its impact on long-term functional performance are also undefined., Methods: Included were 704 patients with primary and 439 with recurrent LGGs seen between 2010-2019, who underwent resection with Brain-Mapping-Technique (BMT) aimed at achieving a SpTR without any "a-priori" selection. Extent-Of-Resection, evaluated on 3D-FLAIR-MR and categorized according to residual tumor and cavity volume, was associated with Progression-Free-Survival (PFS) and Malignant(M)PFS at first and second surgery, and Overall-Survival by univariate, multivariate and propensity-score analysis. Functional performance was assessed by neuropsychological-NPS evaluation., Results: SpTR evaluation requires volumetric assessment enhanced by brain deformation measurement in parietal tumors; SpTR rate accounts on average for 50.2% and 35.7% at first and second surgery, is higher in grade-2, frontal and temporal locations (at expenses of Total-Resection-TR). Compared to TR, SpTR reduces and postpones first and second recurrences in all molecular subtypes and grades, delays MPFS without difference in rate and prolongs Overall-Survival. A degree of SpTR>120% associates with the lowest recurrence risk. SpTR associates with the best NPS longitudinal course., Conclusions: This study supports feasibility of SpTR in LGGs, its benefit at first and second surgery regardless of molecular subtypes, and on Overall-Survival, significantly reducing recurrence when SpTR>120%; SpTR also associates with the best patients' functional outcome., (© The Author(s) 2024. Published by Oxford University Press on behalf of the Society for Neuro-Oncology. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)

Because of the heterogeneity of lower-grade gliomas (LGGs), patients show various survival outcomes that are not reliably predicted by histological classification. The tumour microenvironment (TME) contributes to the initiation and progression of brain LGGs. Identifying potential prognostic markers based on the immune and stromal components in the TME will provide new insights into the dynamic modulation of these two components of the TME in LGGs. We applied ESTIMATE to calculate the ratio of immune and stromal components from The Cancer Genome Atlas database. After combined differential gene expression analysis, protein–protein interaction network construction and survival analysis, CD44 was screened as an independent prognostic factor and subsequently validated utilizing data from the Chinese Glioma Genome Atlas database. To decipher the association of glioma cell CD44 expression with stromal cells in the TME and tumour progression, RT–qPCR, cell viability and wound healing assays were employed to determine whether astrocytes enhance glioma cell viability and migration by upregulating CD44 expression. Surprisingly, M1 macrophages were identified as positively correlated with CD44 expression by CIBERSORT analysis. CD44+ glioma cells were further suggested to interact with microglia-derived macrophages (M1 phenotype) via osteopontin signalling on the basis of single-cell sequencing data. Overall, we found that astrocytes could elevate the CD44 expression level of glioma cells, enhancing the recruitment of M1 macrophages that may promote glioma stemness via osteopontin-CD44 signalling. Thus, glioma CD44 expression might coordinate with glial activities in the TME and serve as a potential therapeutic target and prognostic marker for LGGs.

FDX1 participates in cuproptosis, a copper-dependent cell death mode, which might influence tumor progressions like ferroptosis and pyroptosis. However, the role of FDX1 in tumors remains to be explored. This study investigated FDX1 expression features, and correlations to prognosis, tumor stages, immune microenvironment, and cuproptosis from a pan-cancer perspective based on integrated bioinformatics. FDX1 mRNA and clinical data were obtained from The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), and Broad Institute Cancer Cell Line Encyclopedia (CCLE) databases. Differential expression of FDX1 in tumor stages was performed on GEPIA2.0. Cox proportional hazard regression and survival curve were used to analyze the prognostic value of FDX1. The relationships between FDX1 expression and immune infiltration, immune cells, immune checkpoints, tumor mutation burden (TMB), microsatellite instability (MSI), mismatch repair (MMR), and DNA methyltransferase (DNMT) were explored. GSEA was utilized to find the biological function of FDX1 in LGG. Results showed that FDX1 was abnormally expressed in multiple tumor types and demonstrated variability in various tumor stages. Survival analysis revealed FDX1 predicted poor prognosis in glioma (GBMLGG), brain lower-grade glioma (LGG), and good prognosis in the pan-kidney cohort (KIPAN), and kidney renal clear cell carcinoma (KIRC). Immune correlation analysis suggested FDX1 showed positive correlations to StromalScore, ImmuneScore, ESTIMATEScore in LGG and negative correlation in KIRC. Additionally, positive correlations were observed between FDX1 and immune cells infiltration, immune checkpoints, tumor stemness, homologous recombination deficiency (HRD), and TMB in LGG in the pan-cancer analysis. Validation with CGGA suggested prognostic value and immune correlation of FDX1 in LGG. Specifically, high expression of FDX1 was accompanied by high expression of immune checkpoints such as CD276 (B7-H3), CD274 (PD-L1), PDCD1LG2 (PD-L2), CTLA4, and HAVCR2. These findings illustrated that FDX1 might be considered a potential poor prognosis biomarker and immunotherapy predictor in LGG.

Lower-grade glioma (LGG) is a common malignant primary tumour in the central nervous system, and most patients eventually develop highly aggressive gliomas despite comprehensive traditional treatment. Tumour molecular subtypes and prognostic biomarkers play a crucial role in LGG diagnosis and treatment. Therefore, the identification of novel biomarkers in LGG patients is crucial for predicting the prognosis of glioma. Immunogenic cell death (ICD) is defined as regulated cell death that is sufficient to activate the adaptive immune response of immunocompetent hosts. The combination of ICD and immunotherapy might exert a greater and more persistent antitumour effect in gliomas. In our study, we explored the expression, function, and genetic alterations of 34 ICD-related genes. Using 12 ICD-related genes, including IL17RA, IL1R1, EIF2AK3, CD4, PRF1, CXCR3, CD8A, BAX, PDIA3, CASP8, MYD88, and CASP1, we constructed and validated an ICD-related risk signature via least absolute shrinkage and selection operator (LASSO) Cox regression analysis. All the information was obtained from public databases, including The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), and the Chinese Glioma Genome Atlas (CGGA) databases. Our results revealed that ICD-high risk groups have a poor prognosis and might be more sensitive to immune checkpoint blockade (ICB) immunotherapy. In addition, ICD-high risk groups were associated with 1p19q noncodeletion, higher WHO grade, wild type IDH, and an immunosuppressive tumour microenvironment. We verified the prognostic value of 12 ICD-related genes in TCGA and CGGA databases. Immunohistochemistry was performed to verify the expression of several ICD-related genes at the protein level. Our study provides a novel and comprehensive perspective to elucidate the underlying mechanisms of LGG prognosis and direction for future individualized cancer immunotherapy. [ABSTRACT FROM AUTHOR]

Jinxing Li,1,2 Jing Huan,1 Fu Yang,1,2 Haixin Chen,2,3 Mingguang Wang,2 Xueyuan Heng2 1Guangzhou University of Chinese Medicine, Guangzhou, 510006, Guangdong, People’s Republic of China; 2Department of Neurosurgery, Linyi People’s Hospital, Linyi, 276000, Shandong, People’s Republic of China; 3Weifang Medical University, Weifang, 261053, Shandong, People’s Republic of ChinaCorrespondence: Xueyuan HengDepartment of Neurosurgery, Linyi People’s Hospital, Linyi, 276000, Shandong, People’s Republic of ChinaTel +86 13908990919Email hengxueyuan1962@126.com; hengxueyuan1962@163.comBackground: Lower-grade gliomas (LGGs) patients presented seizure-free have a worse survival than those presented with seizures. However, the current knowledge on its potential value in LGGs remains scarce.Purpose: This study aimed to identify a novel gene signature associated with seizures-free for predicting poor prognosis for LGGs patients.Materials and Methods: The RNA expression and clinical information of LGGs patients were downloaded from the Cancer Genome Atlas database. Differentially expressed genes (DEGs) were screened out between LGGs patients presented seizures-free and seizures. The novel gene signature was constructed by Lasso and multivariate regression analyses for predicting prognosis in LGGs. Its prognostic value was assessed and validated by Kaplan–Meier analyses and receiver operating characteristic (ROC) curves. Multivariate regression analysis was applied to identify the independent prognostic value of the gene signature. Furthermore, bioinformatics analysis was performed to elucidate the molecular mechanisms.Results: A total of 253 DEGs were screened out between LGG patients presented with seizures and free of seizures. A 5-gene signature (HIST1H4F, HORMAD2, LILRA3, PRSS33, and TBX20 genes) was constructed from these 253 DEGs. Kaplan–Meier analyses and ROC curves assessed and validated the good performance of the 5-gene signature in differentiating and predicting prognosis of high- and low-risk patients. Multivariate regression analysis determined the independent prognostic value of the 5-gene signature. According to bioinformatics analysis, DEGs were mainly enriched in biological processes related to positive regulation of transcription from RNA polymerase II promoter, G-protein coupled receptor signaling pathway, and pathways of cytokine–cytokine receptor interaction, chemokine signaling pathway.Conclusion: Our findings suggested that the 5-gene signature might serve as a potential prognostic biomarker and provide guidance for the personalized LGGs management.Keywords: lower-grade gliomas, seizures-free, gene signature

Background: Cuproptosis is a newly discovered unique non-apoptotic programmed cell death distinguished from known death mechanisms like ferroptosis, pyroptosis, and necroptosis. However, the prognostic value of cuproptosis and the correlation between cuproptosis and the tumor microenvironment (TME) in lower-grade gliomas (LGGs) remain unknown. Methods: In this study, we systematically investigated the genetic and transcriptional variation, prognostic value, and expression patterns of cuproptosis-related genes (CRGs). The CRG score was applied to quantify the cuproptosis subtypes. We then evaluated their values in the TME, prognostic prediction, and therapeutic responses in LGG. Lastly, we collected five paired LGG and matched normal adjacent tissue samples from Sun Yat-sen University Cancer Center (SYSUCC) to verify the expression of signature genes by quantitative real-time PCR (qRT-PCR) and Western blotting (WB). Results: Two distinct cuproptosis-related clusters were identified using consensus unsupervised clustering analysis. The correlation between multilayer CRG alterations with clinical characteristics, prognosis, and TME cell infiltration were observed. Then, a well-performed cuproptosis-related risk model (CRG score) was developed to predict LGG patients' prognosis, which was evaluated and validated in two external cohorts. We classified patients into high- and low-risk groups according to the CRG score and found that patients in the low-risk group showed significantly higher survival possibilities than those in the high-risk group (P<0.001). A high CRG score implies higher TME scores, more significant TME cell infiltration, and increased mutation burden. Meanwhile, the CRG score was significantly correlated with the cancer stem cell index, chemoradiotherapy sensitivity-related genes and immune checkpoint genes, and chemotherapeutic sensitivity, indicating the association with CRGs and treatment responses. Univariate and multivariate Cox regression analyses revealed that the CRG score was an independent prognostic predictor for LGG patients. Subsequently, a highly accurate predictive model was established for facilitating the clinical application of the CRG score, showing good predictive ability and calibration. Additionally, crucial CRGs were further validated by qRT-PCR and WB. Conclusion: Collectively, we demonstrated a comprehensive overview of CRG profiles in LGG and established a novel risk model for LGG patients' therapy status and prognosis. Our findings highlight the potential clinical implications of CRGs, suggesting that cuproptosis may be the potential therapeutic target for patients with LGG. [ABSTRACT FROM AUTHOR]

Background: The ZFP36 Ring Finger Protein Like 2 (ZFP36L2) is an RNA-binding protein that regulates gene expression at post-transcriptional level. However, the clinical significance and prognostic value of ZFP36L2 in lower-grade glioma (LGG) remain unclear. Method: ZFP36L2 expression was investigated using public datasets and the prognostic merit of ZFP36L2 with LGG patients was further evaluated. The correlation between the genetic alteration of ZFP36L2 and its mRNA expression was accessed via cBioPortal. Additionally, the prognostic value of the ZFP36L2 methylation levels in LGG was evaluated by MethSurv. The potential biological role of ZFP36L2 in LGG was identified by performing functional analyses. We also examined the correlation between ZFP36L2 expression and the immune infiltration. Finally, the predictive value of ZFP36L2 to immunotherapy was assessed. Result: ZFP36L2 was highly expressed in LGG patients and overexpressed ZFP36L2 predicted poor clinical outcomes. We further identified ZFP36L2 as an independent prognostic factor. The methylation level of ZFP36L2 negatively correlated with the ZFP36L2 expression, and patients with low ZFP36L2 methylation had worse overall survival. The results of functional analysis indicated that ZFP36L2 was involved in multiple immune response-related pathways in LGG. Furthermore, high expression of ZFP36L2 was significantly and positively correlated with immune infiltration. Finally, we found that ZFP36L2 expression was positively correlated with the immune checkpoint PD-L1, and ZFP36L2 low expression cohort gained better benefit from immunotherapy. Conclusion: Our findings demonstrate that ZFP36L2 is a potential biomarker for LGG, highlighting its potential as a therapeutic target in immunotherapy. [ABSTRACT FROM AUTHOR]

Glycosylation alterations, a key driver throughout tumorigenesis and tumor progression, could regulate the microenvironment and immune response as well as lead to harmful metabolism and cell signaling. In this study, we first comprehensively evaluated the glycosylation modification patterns of LGGs based on glycosyltransferase family genes and systematically integrated these modification patterns with tumor metabolism and immune microenvironment characteristics. Glycosylation score was also developed to quantify glycosylation modification patterns of individuals. As a result, two glycosylation modification patterns were identified, with distinct prognosis, metabolism, and immune microenvironment features. The glycosylation subtype A and cluster A were characterized by higher carbohydrates and amino acid metabolism activity, higher levels of infiltrating cells, and poor prognosis, whereas an opposite modification pattern was observed in glycosylation subtype B and cluster B. In addition, a high glycosylation score is closer to a microenvironment characterized by chronic inflammation, immunosuppression, and tumor promotion. Following analysis and validation, the glycosylation score was a reliable and independent prognostic index. More importantly, the glycosylation score influenced the response to immunotherapy, chemotherapy, or targeted therapy, which provided a novel insight into promoting personalized therapy in the future and may contribute to developing novel therapeutic drugs or exploring promising drug combination therapy strategies.

Epithelial-mesenchymal transition (EMT) confers high invasive and migratory capacity to cancer cells, which limits the effectiveness of tumor therapy. Long non-coding RNAs (lncRNAs) can regulate the dynamic process of EMT at different levels through various complex regulatory networks. We aimed to comprehensively analyze and screen EMT-related lncRNAs to characterize lower-grade glioma (LGG) tumor biology and provide new ideas for current therapeutic approaches. We retrieved 1065 LGG samples from the Cancer Genome Atlas and Chinese Glioma Genome Atlas by machine learning algorithms, identified three hub lncRNAs including CRNDE, LINC00665, and NEAT1, and established an EMT-related lncRNA signature (EMTrLS). This novel signature had strong prognostic value and potential clinical significance. EMTrLS described LGG genomic alterations and clinical features including gene mutations, tumor mutational burden, World Health Organization (WHO) grade, IDH status, and 1p/19q status. Notably, stratified analysis revealed activation of malignancy-related and metabolic pathways in the EMTrLS-high cohort. Moreover, the population with increased EMTrLS scores had increased cells with immune killing function. However, this antitumor immune function may be suppressed by increased Tregs and macrophages. Meanwhile, the relatively high expression of immune checkpoints explained the immunosuppressive state of patients with high EMTrLS scores. Importantly, we validated this result by quantifying the course of antitumor immunity. In particular, EMTrLS stratification enabled assessment of the responsiveness of LGG to chemotherapeutic drug efficacy and PD1 blockade. In conclusion, our findings complement the foundation of molecular studies of LGG, provide valuable insight into our understanding of EMT-related lncRNAs, and offer new strategies for LGG therapy.

Background: The therapeutic effects of conventional treatment on gliomas are not promising. The tumor microenvironment (TME) has a close association with the invasiveness of multiple types of tumors, including low‐grade gliomas (LGG). This study aims to validate the prognostic and immune‐related role of macrophage scavenger receptor 1 (MSR1) in LGG patients. Methods: Data in this study were obtained from public databases. The differential expression of MSR1 was analyzed in LGG patients with different clinicopathological characteristics. Kaplan–Meier survival analysis, a time‐dependent receiver operating characteristic (ROC) curve, and Cox regression analysis were used to assess the prognostic value of MSR1. Differentially expressed genes (DEGs) were screened between the high and low expression groups of MSR1. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were used to annotate the function of these DEGs. Hallmark gene sets were identified based on MSR1 by Gene Set Enrichment Analysis (GSEA). Difference analysis and correlation analysis were used to study the relationship between MSR1 and TME‐related scores, tumor‐infiltrating immune cells (TIICs), immune‐related gene sets, and immune checkpoints (ICPs). The single‐cell sequencing data were processed to identify the cell types expressing MSR1. The quantification of TIICs in TME was calculated by single‐sample gene set enrichment analysis (ssGSEA). The differential expression of MSR1 in LGG and control brain tissues was verified by experiments. Results: There were significant differences in the expression level of MSR1 in different types of tissues and cells. MSR1 has a high prognostic value in LGG patients and can be used as an independent prognostic factor. MSR1 is closely related to TME and may play an important role in the immunotherapy of LGG patients. Conclusions: The result of our study demonstrated that MSR1 is an independent prognostic biomarker in LGG patients and may play an important role in the TME of LGGs. [ABSTRACT FROM AUTHOR]

5-Methylcytosine (m5C) methylation is an important RNA modification pattern that can participate in oncogenesis and progression of cancers by affecting RNA stability, expression of oncogenes, and the activity of cancer signaling pathways. Alterations in the expression pattern of long non-coding RNAs (lncRNAs) are potentially correlated with abnormalities in the m5C regulation features of cancers. Our aim was to reveal the mechanisms by which lncRNAs regulated the m5C process, to explore the impact of aberrant regulation of m5C on the biological properties of lower-grade gliomas (LGG), and to optimize current therapeutic. By searching 1017 LGG samples from the Cancer Genome Atlas and Chinese Glioma Genome Atlas, we first clarified the potential impact of m5C regulators on LGG prognosis in this study and used univariate Cox analysis and least absolute shrinkage and selection operator regression to explore clinically meaningful lncRNAs. Consequently, we identified four lncRNAs, including LINC00265, CIRBP-AS1, GDNF-AS1, and ZBTB20-AS4, and established a novel m5C-related lncRNAs signature (m5CrLS) that was effective in predicting prognosis. Notably, mutation rate, WHO class II, IDH mutation, 1p/19q co-deletion and MGMT promoter methylation were increased in the low m5CrLS score group. Patients with increased m5CrLS scores mostly showed activation of tumor malignancy-related pathways, increased immune infiltrating cells, and decreased anti-tumor immune function. Besides, the relatively high expression of immune checkpoints also revealed the immunosuppressed state of patients with high m5CrLS scores. In particular, m5CrLS stratification was sensitive to assess the efficacy of LGG to temozolomide and the responsiveness of immune checkpoint blockade. In conclusion, our results revealed the molecular basis of LGG, provided valuable clues for our understanding of m5C-related lncRNAs, and filled a gap between epigenetics and tumor microenvironment. [ABSTRACT FROM AUTHOR]

Abstract Background To investigate associations between lower-grade glioma (LGG) mRNA-based subtypes (R1-R4) and MR features. Methods mRNA-based subtyping was obtained from the LGG dataset in The Cancer Genome Atlas (TCGA). We identified matching patients (n = 145) in The Cancer Imaging Archive (TCIA) who underwent MR imaging. The associations between mRNA-based subtypes and MR features were assessed. Results In the TCGA-LGG dataset, patients with the R2 subtype had the shortest median OS months (P 5%, OR: 14.733; P 5%, OR: 0.14; P

Summary: Background: Virtually few accurate and robust prediction models of lower-grade gliomas (LGG) survival exist that may aid physicians in making clinical decisions. We aimed to develop a prognostic prediction model of LGG by incorporating demographic, clinical and transcriptional biomarkers with either main effects or gene-gene interactions. Methods: Based on gene expression profiles of 1,420 LGG patients from six independent cohorts comprising both European and Asian populations, we proposed a 3-D analysis strategy to develop and validate an Accurate Prediction mOdel of Lower-grade gLiomas Overall survival (APOLLO). We further conducted decision curve analysis to assess the net benefit (NB) of identifying true positives and the net reduction (NR) of unnecessary interventions. Finally, we compared the performance of APOLLO and the existing prediction models by the first systematic review. Findings: APOLLO possessed an excellent discriminative ability to identify patients at high mortality risk. Compared to those with less than the 20th percentile of APOLLO risk score, patients with more than the 90th percentile of APOLLO risk score had significantly worse overall survival (HR=54·18, 95% CI: 34·73-84·52, P=2·66 × 10−69). Further, APOLLO can accurately predict both 36- and 60-month survival in six independent cohorts with a pooled AUC36-month=0·901 (95% CI: 0·879-0·923), AUC60-month=0·843 (95% CI: 0·815-0·871) and C-index=0·818 (95% CI: 0·800-0·835). Moreover, APOLLO offered an effective screening strategy for detecting LGG patients susceptible to death (NB36-month=0·166, NR36-month=40·1% and NB60-month=0·258, NR60-month=19·2%). The systematic comparisons revealed APOLLO outperformed the existing models in accuracy and robustness. Interpretation: APOLLO has the demonstrated feasibility and utility of predicting LGG survival (http://bigdata.njmu.edu.cn/APOLLO). Funding: National Key Research and Development Program of China (2016YFE0204900); Natural Science Foundation of Jiangsu Province (BK20191354); National Natural Science Foundation of China (81973142 and 82103946); China Postdoctoral Science Foundation (2020M681671); National Institutes of Health (CA209414, CA249096, CA092824 and ES000002).

5-Methylcytosine (m5C) methylation is an important RNA modification pattern that can participate in oncogenesis and progression of cancers by affecting RNA stability, expression of oncogenes, and the activity of cancer signaling pathways. Alterations in the expression pattern of long non-coding RNAs (lncRNAs) are potentially correlated with abnormalities in the m5C regulation features of cancers. Our aim was to reveal the mechanisms by which lncRNAs regulated the m5C process, to explore the impact of aberrant regulation of m5C on the biological properties of lower-grade gliomas (LGG), and to optimize current therapeutic. By searching 1017 LGG samples from the Cancer Genome Atlas and Chinese Glioma Genome Atlas, we first clarified the potential impact of m5C regulators on LGG prognosis in this study and used univariate Cox analysis and least absolute shrinkage and selection operator regression to explore clinically meaningful lncRNAs. Consequently, we identified four lncRNAs, including LINC00265, CIRBP-AS1, GDNF-AS1, and ZBTB20-AS4, and established a novel m5C-related lncRNAs signature (m5CrLS) that was effective in predicting prognosis. Notably, mutation rate, WHO class II, IDH mutation, 1p/19q co-deletion and MGMT promoter methylation were increased in the low m5CrLS score group. Patients with increased m5CrLS scores mostly showed activation of tumor malignancy-related pathways, increased immune infiltrating cells, and decreased anti-tumor immune function. Besides, the relatively high expression of immune checkpoints also revealed the immunosuppressed state of patients with high m5CrLS scores. In particular, m5CrLS stratification was sensitive to assess the efficacy of LGG to temozolomide and the responsiveness of immune checkpoint blockade. In conclusion, our results revealed the molecular basis of LGG, provided valuable clues for our understanding of m5C-related lncRNAs, and filled a gap between epigenetics and tumor microenvironment.

Glioma is a relatively low aggressive brain tumor. Although the median survival time of patients for lower-grade glioma (LGG) was longer than that of patients for glioblastoma, the overall survival was still short. Therefore, it is urgent to find out more effective molecular prognostic markers. The role of the Fam20 kinase family in different tumors was an emerging research field. However, the biological function of Fam20C and its prognostic value in brain tumors have rarely been reported. This study aimed to evaluate the value of Fam20C as a potential prognostic marker for LGG. A total of 761 LGG samples (our cohort, TCGA and CGGA) were included to investigate the expression and role of Fam20C in LGG. We found that Fam20C was drastically overexpressed in LGG and was positively associated with its clinical progression. Kaplan-Meier analysis and a Cox regression model were employed to evaluate its prognostic value, and Fam20C was found as an independent risk factor in LGG patients. Gene set enrichment analysis also revealed the potential signaling pathways associated with Fam20C gene expression in LGG; these pathways were mainly enriched in extracellular matrix receptor interactions, cell adhesion, cell apoptosis, NOTCH signaling, cell cycle, etc. In summary, our findings provide insights for understanding the potential role of Fam20C and its application as a new prognostic biomarker for LGG. [ABSTRACT FROM AUTHOR]

Background: Isocitrate dehydrogenase (IDH)-mutant lower-grade gliomas (LGGs) are further classified into two classes: with and without 1p/19q codeletion. IDH-mutant and 1p/19q codeleted LGGs have better prognosis compared with IDH-mutant and 1p/19q non-codeleted LGGs. Purpose: To evaluate conventional magnetic resonance imaging (cMRI), diffusion-weighted imaging (DWI), susceptibility-weighted imaging (SWI), and dynamic susceptibility contrast perfusion-weighted imaging (DSC-PWI) for predicting 1p/19q codeletion status of IDH-mutant LGGs. Material and Methods: We retrospectively reviewed cMRI, DWI, SWI, and DSC-PWI in 142 cases of IDH mutant LGGs with known 1p/19q codeletion status. Features of cMRI, relative ADC (rADC), intratumoral susceptibility signals (ITSSs), and the value of relative cerebral blood volume (rCBV) were compared between IDH-mutant LGGs with and without 1p/19q codeletion. Receiver operating characteristic curve and logistic regression were used to determine diagnostic performances. Results: IDH-mutant and 1p/19q non-codeleted LGGs tended to present with the T2/FLAIR mismatch sign and distinct borders (P < 0.001 and P = 0.038, respectively). Parameters of rADC, ITSSs, and rCBVmax were significantly different between the 1p/19q codeleted and 1p/19q non-codeleted groups (P < 0.001, P = 0.017, and P < 0.001, respectively). A combination of cMRI, SWI, DWI, and DSC-PWI for predicting 1p/19q codeletion status in IDH-mutant LGGs resulted in a sensitivity, specificity, positive predictive value, negative predictive value, and an AUC of 80.36%, 78.57%, 83.30%, 75.00%, and 0.88, respectively. Conclusion: 1p/19q codeletion status of IDH-mutant LGGs can be stratified using cMRI and advanced MRI techniques, including DWI, SWI, and DSC-PWI. A combination of cMRI, rADC, ITSSs, and rCBVmax may improve the diagnostic performance for predicting 1p/19q codeletion status. [ABSTRACT FROM AUTHOR]

Gliomas are the most common tumors of the central nervous system and are classified into grades I-IV based on their histological characteristics. Lower-grade gliomas (LGG) can be divided into grade II diffuse low-grade gliomas and grade III moderate gliomas and have a relatively good prognosis. However, LGG often develops into high-grade glioma within a few years. This study aimed to construct and identify the prognostic value of an inflammatory signature and discover potential drug targets for primary LGG. We first screened differentially expressed genes in primary LGG (TCGA) compared with normal brain tissue (GTEx) that overlapped with inflammation-related genes from MSigDB. After survival analysis, nine genes were selected to construct an inflammatory signature. LGG patients with a high inflammatory signature score had a poor prognosis, and the inflammatory signature was a strong independent prognostic factor in both the training cohort (TCGA) and validation cohort (CGGA). Compared with the low-inflammatory signature group, differentially expressed genes in the high-inflammatory signature group were mainly enriched in immune-related signaling pathways, which is consistent with the distribution of immune cells in the high- and low-inflammatory signature groups. Integrating driver genes, upregulated genes and drug targets data, bromodomain and PHD finger-containing protein 1 (BRPF1) was selected as a potential drug target. Inhibition of BRPF1 function or knockdown of BRPF1 expression attenuated glioma cell proliferation and colony formation. [ABSTRACT FROM AUTHOR]

Glioma is a relatively low aggressive brain tumor. Although the median survival time of patients for lower-grade glioma (LGG) was longer than that of patients for glioblastoma, the overall survival was still short. Therefore, it is urgent to find out more effective molecular prognostic markers. The role of the Fam20 kinase family in different tumors was an emerging research field. However, the biological function of Fam20C and its prognostic value in brain tumors have rarely been reported. This study aimed to evaluate the value of Fam20C as a potential prognostic marker for LGG. A total of 761 LGG samples (our cohort, TCGA and CGGA) were included to investigate the expression and role of Fam20C in LGG. We found that Fam20C was drastically overexpressed in LGG and was positively associated with its clinical progression. Kaplan-Meier analysis and a Cox regression model were employed to evaluate its prognostic value, and Fam20C was found as an independent risk factor in LGG patients. Gene set enrichment analysis also revealed the potential signaling pathways associated with Fam20C gene expression in LGG; these pathways were mainly enriched in extracellular matrix receptor interactions, cell adhesion, cell apoptosis, NOTCH signaling, cell cycle, etc. In summary, our findings provide insights for understanding the potential role of Fam20C and its application as a new prognostic biomarker for LGG.

Gliomas are the most common tumors of the central nervous system and are classified into grades I-IV based on their histological characteristics. Lower-grade gliomas (LGG) can be divided into grade II diffuse low-grade gliomas and grade III moderate gliomas and have a relatively good prognosis. However, LGG often develops into high-grade glioma within a few years. This study aimed to construct and identify the prognostic value of an inflammatory signature and discover potential drug targets for primary LGG. We first screened differentially expressed genes in primary LGG (TCGA) compared with normal brain tissue (GTEx) that overlapped with inflammation-related genes from MSigDB. After survival analysis, nine genes were selected to construct an inflammatory signature. LGG patients with a high inflammatory signature score had a poor prognosis, and the inflammatory signature was a strong independent prognostic factor in both the training cohort (TCGA) and validation cohort (CGGA). Compared with the low-inflammatory signature group, differentially expressed genes in the high-inflammatory signature group were mainly enriched in immune-related signaling pathways, which is consistent with the distribution of immune cells in the high- and low-inflammatory signature groups. Integrating driver genes, upregulated genes and drug targets data, bromodomain and PHD finger-containing protein 1 (BRPF1) was selected as a potential drug target. Inhibition of BRPF1 function or knockdown of BRPF1 expression attenuated glioma cell proliferation and colony formation.

Lower-grade gliomas (LGGs) have a good prognosis with a wide range of overall survival (OS) outcomes. An accurate prognostic system can better predict survival time. An RNA-Sequencing (RNA-seq) prognostic signature showed a better predictive power than clinical predictor models. A signature constructed using gene pairs can transcend changes from biological heterogeneity, technical biases, and different measurement platforms. RNA-seq coupled with corresponding clinical information were extracted from The Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA). Immune-related gene pairs (IRGPs) were used to establish a prognostic signature through univariate and multivariate Cox proportional hazards regression. Weighted gene co-expression network analysis (WGCNA) was used to evaluate module eigengenes correlating with immune cell infiltration and to construct gene co-expression networks. Samples in the training and testing cohorts were dichotomized into high- and low-risk groups. Risk score was identified as an independent predictor, and exhibited a closed relationship with prognosis. WGCNA presented a gene set that was positively correlated with age, WHO grade, isocitrate dehydrogenase (IDH) mutation status, 1p/19 codeletion, risk score, and immune cell infiltrations (CD4 T cells, B cells, dendritic cells, and macrophages). A nomogram comprising of age, WHO grade, 1p/19q codeletion, and three gene pairs (BIRC5|SSTR2, BMP2|TNFRSF12A, and NRG3|TGFB2) was established as a tool for predicting OS. The IPGPs signature, which is associated with immune cell infiltration, is a novel tailored tool for individual-level prediction.

Rationale and Objectives: To explore associations between MR imaging features, DNA methylation subtyping, and survival in lower-grade gliomas (LGG).Materials and Methods: The MR data from 170 patients generated with the Cancer Imaging Archive were reviewed. The correlation was evaluated by Fisher's Exact Test, Pearson Chi-Square and binary regression analysis. Survival analysis was conducted by using time-dependent ROC analysis and the Kaplan-Meier method (the worst prognosis subgroup).Results: Identified were 9 (5.3%) M1-subtype, 18 (10.6%) M2-subtype, 48 (28.2%) M3-subtype, 31 (18.2%) M4-subtype and 64 (37.6%) M5-subtype. Patients with M4-subtype had the shortest median OS (49.3 vs. 28.4) months(p < 0.05). The time-dependent ROC for the M4-subtype was 0.83 (95% confidence interval 0.72-0.95) for survival at 12 months, 0.82 (95% confidence interval 0.70-0.94) for survival at 24 months, and 0.74 (95% confidence interval 0.62-0.86) for survival at 36 months. After uni- and multivariate analysis, a nomogram was built based on proportion contrast-enhanced (CE) tumor, extranodular growth, volume_cutoff_median, and location. For the prediction of M4-subtype, the nomogram showed good discrimination, with an area under the curve (AUC) of 0.886 (95% CI: 0.820-952) and was well calibrated. On multivariate logistic regression analysis, volume ≥60cm3 (OR: 0.200; p < 0.001; 95%CI: 0.048-0.834) was associated with M1-subtype (AUC: 0.690). Hemorrhage (OR: 5.443; p = 0.002; 95%CI: 1.844-16.069) and volume > median (OR: 3.256; p = 0.05; 95%CI: 0.992-10.686) were associated with M2-subtype (AUC: 0.733). Proportion CE tumor<=5% (OR: 3.968; P=0.002; 95%CI: 1.634-9.635) was associated with M3-subtype (AUC: 0.632). Poorly-defined (OR: 2.258; p = 0.05; 95%CI: 1.000-5.101) and volume > median (OR: 2.447; p = 0.01; 95%CI: 1.244-4.813) were associated with M5-subtype (AUC: 0.645). Decision curve analysis indicated predictions for all models were clinically useful.Conclusion: This preliminary radiogenomics analysis of lower-grade gliomas demonstrated associations between MR features and DNA methylation subtyping. The shortest survival was observed in patients with M4-subtype. And we have constructed nomogram that enables more accurate predictions of M4-subtype. [ABSTRACT FROM AUTHOR]

Introduction: Glioma is the most common malignant primary brain tumor with survival outcome for patients with lower‐grade gliomas (LGGs) being quite variable. Epigenetic modifications in LGGs appear tightly linked to patient clinical outcomes but are not commonly used as clinical tools. Aims: We aimed to derive an epigenetic enzyme gene signature for LGGs that would allow for improved clinical risk stratification. Results: The study employed transcriptomic data of 711 lower‐grade gliomas from three publically available data sets. Based on least absolute shrinkage and selection operator (LASSO) Cox regression analysis, we discovered a 13‐gene epigenetic signature that strongly predicts poor overall survival in LGGs. The robust prediction ability for survival was further verified in two independent validation cohorts. The signature was also significantly associated with malignant molecular signatures including wild‐type IDH, unmethylated MGMT promoter, and non‐codeletion of 1p19q together with linkage to multiple oncogenic pathways. Interestingly, our results showed that immune infiltration of MDSCs together with mRNA expression of immune inhibition biomarkers was also positively correlated with the epigenetic signature. Lastly, we confirmed the oncogenic role of SMYD2 in glioma tumor cells in functional assays. Conclusions: We report a novel epigenetic gene signature that harbors robust survival prediction value for LGG patients that is tightly linked to activation of multiple oncogenic pathways. [ABSTRACT FROM AUTHOR]

Ferroptosis is a newly discovered form of programmed cell death, which has unique biological effects on metabolism and redox biology. In this study, the prognostic value of ferroptosis‐related genes was investigated in lower‐grade gliomas (LGG). We downloaded the ferroptosis‐related genes from the FerrDb dataset. Univariate Cox and LASSO regression analyses were applied to identify genes correlated with overall survival (OS). Subsequently, 12 ferroptosis‐related genes were screened to establish the prognostic signature using stepwise multivariate Cox regression. According to the median value of risk scores, patients were divided into low‐ and high‐risk subgroups. The Kaplan‐Meier curves showed the high‐risk group had a lower OS. The predictive power of the risk model was validated using the CGGA. Functional analysis revealed that the terms associated with plasma membrane receptor complex, immune response and glutamate metabolic process were primarily related to the risk model. Moreover, we established a nomogram that had a strong forecasting ability for the 1‐, 3‐ and 5‐year OS. In addition, we compared the risk scores between different clinical features. We also detected infiltration of macrophages and monocytes in different subgroups. Overall, our study identified the prognostic signature of 12 ferroptosis‐related genes, which has the potential to predict the prognosis of LGG. [ABSTRACT FROM AUTHOR]

BackgroundLower-grade gliomas (LGGs) have more favorable outcomes than glioblastomas; however, LGGs often progress to process glioblastomas within a few years. Numerous studies have proven that the tumor microenvironment (TME) is correlated with the prognosis of glioma.MethodsLGG RNA-Sequencing (RNA-seq) data from The Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA) were extracted and then divided into training and testing cohorts, respectively. Immune-related differentially expressed genes (DEGs) were screened to establish a prognostic signature by a multivariate Cox proportional hazards regression model. The immune-related risk score and clinical information, such as age, sex, World Health Organization (WHO) grade, and isocitrate dehydrogenase 1 (IDH1) mutation, were used to independently validate and develop a prognostic nomogram. GO and KEGG pathway analyses to DEGs between immune-related high-risk and low-risk groups were performed.ResultsSixteen immune-related genes were screened for establishing a prognostic signature. The risk score had a negative correlation with prognosis, with an area under the receiver operating characteristic (ROC) curve of 0.941. The risk score, age, grade, and IDH1 mutation were identified as independent prognostic factors in patients with LGGs. The hazard ratios (HRs) of the high-risk score were 5.247 [95% confidence interval (CI) = 3.060–8.996] in the multivariate analysis. A prognostic nomogram of 1-, 3-, and 5-year survival was established and validated internally and externally. Go and KEGG pathway analyses implied that immune-related biological function and pathways were involved in the TME.ConclusionThe immune-related prognostic signature and the prognostic nomogram could accurately predict survival.

Background: Lower-grade gliomas (LGGs) have more favorable outcomes than glioblastomas; however, LGGs often progress to process glioblastomas within a few years. Numerous studies have proven that the tumor microenvironment (TME) is correlated with the prognosis of glioma. Methods: LGG RNA-Sequencing (RNA-seq) data from The Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA) were extracted and then divided into training and testing cohorts, respectively. Immune-related differentially expressed genes (DEGs) were screened to establish a prognostic signature by a multivariate Cox proportional hazards regression model. The immune-related risk score and clinical information, such as age, sex, World Health Organization (WHO) grade, and isocitrate dehydrogenase 1 (IDH1) mutation, were used to independently validate and develop a prognostic nomogram. GO and KEGG pathway analyses to DEGs between immune-related high-risk and low-risk groups were performed. Results: Sixteen immune-related genes were screened for establishing a prognostic signature. The risk score had a negative correlation with prognosis, with an area under the receiver operating characteristic (ROC) curve of 0.941. The risk score, age, grade, and IDH1 mutation were identified as independent prognostic factors in patients with LGGs. The hazard ratios (HRs) of the high-risk score were 5.247 [95% confidence interval (CI) = 3.060–8.996] in the multivariate analysis. A prognostic nomogram of 1-, 3-, and 5-year survival was established and validated internally and externally. Go and KEGG pathway analyses implied that immune-related biological function and pathways were involved in the TME. Conclusion: The immune-related prognostic signature and the prognostic nomogram could accurately predict survival. [ABSTRACT FROM AUTHOR]

Purpose: To extrapolate the infiltration levels of immune cells in patients with lower-grade gliomas (LGGs) using magnetic resonance imaging (MRI)-based radiomic features. Methods: A retrospective dataset of 516 patients with LGGs from The Cancer Genome Atlas (TCGA) database was analysed for the infiltration levels of six types of immune cells using Tumor IMmune Estimation Resource (TIMER) based on RNA sequencing data. Radiomic features were extracted from 107 patients whose pre-operative MRI data are available in The Cancer Imaging Archive; 85 and 22 of these patients were assigned to the training and testing cohort, respectively. The least absolute shrinkage and selection operator (LASSO) was applied to select optimal radiomic features to build the radiomic signatures for extrapolating the infiltration levels of immune cells in the training cohort. The developed radiomic signatures were examined in the testing cohort using Pearson's correlation. Results: The infiltration levels of B cells, CD4+ T cells, CD8+ T cells, macrophages, neutrophils and dendritic cells negatively correlated with overall survival in the 516 patient cohort when using univariate Cox's regression. Age, Karnofsky Performance Scale, WHO grade, isocitrate dehydrogenase mutant status and the infiltration of neutrophils correlated with survival using multivariate Cox's regression analysis. The infiltration levels of the 6 cell types could be estimated by radiomic features in the training cohort, and their corresponding radiomic signatures were built. The infiltration levels of B cells, CD8+ T cells, neutrophils and macrophages estimated by radiomics correlated with those estimated by TIMER in the testing cohort. Combining clinical/genomic features with the radiomic signatures only slightly improved the prediction of immune cell infiltrations. Conclusion: We developed MRI-based radiomic models for extrapolating the infiltration levels of immune cells in LGGs. Our results may have implications for treatment planning. [ABSTRACT FROM AUTHOR]