Glutathione metabolism-related gene signature predicts prognosis and treatment response in low-grade glioma.

Cancer cells can induce molecular changes that reshape cellular metabolism, creating specific vulnerabilities for targeted therapeutic interventions. Given the importance of reactive oxygen species (ROS) in tumor development and drug resistance, and the abundance of reduced glutathione (GSH) as the primary cellular antioxidant, we examined an integrated panel of 56 glutathione metabolism-related genes (GMRGs) across diverse cancer types. This analysis revealed a remarkable association between GMRGs and low-grade glioma (LGG) survival. Unsupervised clustering and a GMRGs-based risk score (GS) categorized LGG patients into two groups, linking elevated glutathione metabolism to poorer prognosis and treatment outcomes. Our GS model outperformed established clinical prognostic factors, acting as an independent prognostic factor. GS also exhibited correlations with pro-tumor M2 macrophage infiltration, upregulated immunosuppressive genes, and diminished responses to various cancer therapies. Experimental validation in glioma cell lines confirmed the critical role of glutathione metabolism in glioma cell proliferation and chemoresistance. Our findings highlight the presence of a unique metabolic susceptibility in LGG and introduce a novel GS system as a highly effective tool for predicting the prognosis of LGG.

A patient-derived cell model for malignant transformation in IDH-mutant glioma.

Malignant transformation (MT) is commonly seen in IDH-mutant gliomas. There has been a growing research interest in revealing its underlying mechanisms and intervening prior to MT at the early stages of the transforming process. Here we established a unique pair of matched 3D cell models: 403L, derived from a low-grade glioma (LGG), and 403H, derived from a high-grade glioma (HGG), by utilizing IDH-mutant astrocytoma samples from the same patient when the tumor was diagnosed as WHO grade 2 (tumor mutational burden (TMB) of 3.96/Mb) and later as grade 4 (TMB of 70.07/Mb), respectively. Both cell models were authenticated to a patient's sample retaining endogenous expression of IDH1 R132H. DNA methylation profiles of the parental tumors referred to LGG and HGG IDH-mutant glioma clusters. The immunopositivity of SOX2, NESTIN, GFAP, OLIG2, and beta 3-Tubulin suggested the multilineage potential of both models. 403H was more prompt to cell invasion and developed infiltrative HGG in vivo. The differentially expressed genes (DEGs) from the RNA sequencing analysis revealed the tumor invasion and aggressiveness related genes exclusively upregulated in the 403H model. Pathway analysis showcased an enrichment of genes associated with epithelial-mesenchymal transition (EMT) and Notch signaling pathways in 403H and 403L, respectively. Mass spectrometry-based targeted metabolomics and hyperpolarized (HP) 1-13C pyruvate in-cell NMR analyses demonstrated significant alterations in the TCA cycle and fatty acid metabolism. Citrate, glutamine, and 2-HG levels were significantly higher in 403H. To our knowledge, this is the first report describing the development of a matched pair of 3D patient-derived cell models representative of MT and temozolomide (TMZ)-induced hypermutator phenotype (HMP) in IDH-mutant glioma, providing insights into genetic and metabolic changes during MT/HMP. This novel in vitro model allows further investigation of the mechanisms of MT at the cellular level.

Weighted correlation network analysis identifies multiple susceptibility loci for low-grade glioma.

BACKGROUND: The current molecular classifications cannot completely explain the polarized malignant biological behavior of low-grade gliomas (LGGs), especially for tumor recurrence. Therefore, we tried to identify suspicious hub genes related to tumor recurrence in LGGs. METHODS: In this study, we constructed a gene-miRNA-lncRNA co-expression network for LGGs by a weighted gene co-expression network analysis (WGCNA). GDCRNATools and the WGCNA R package were mainly used in data analysis. RESULTS: Sequencing data from 502 LGG patients were analyzed in this study. Compared with recurrent glioma tissues, we identified 774 differentially expressed (DE) mRNAs, 49 DE miRNAs, and 129 DE lncRNAs in primary LGGs and ultimately determined that the expression of MKLN1 was related to tumor recurrence in LGG. CONCLUSION: This study identified the potential biomarkers for the pathogenesis and recurrence of LGGs and proposed that MKLN1 could be a potential therapeutic target.

Exploration and validation of m7G-related genes as signatures in the immune microenvironment and prognostic indicators in low-grade glioma.

OBJECTIVES: Currently, an increasing number of studies are focusing on the impact of m7G modification in cancer. This study aims to investigate the prognostic value of m7G-related genes in low-grade glioma (LGG). METHODS: LGG samples were obtained from the CGGA database, and normal samples were obtained from GTEx. Differentially expressed m7G-related genes were identified, and genes highly associated with macrophage M2 in LGG patients were identified by immuno-infiltration and WGCNA analysis. The intersection of differentially expressed m7G-related genes and macrophage M2-associated genes yielded candidate genes, and hub genes were identified using 5 algorithms in CytoHubba. Enrichment analysis verified the relevant pathways of hub genes, and their performance in tumor classification was evaluated. RESULTS: A total of 3329 differentially expressed m7G-related genes were identified. 1289 genes were highly associated with macrophage M2 in LGG patients. The intersection of m7G-related genes and results in WGCNA yielded 840 candidate genes, and six hub genes (STXBP1, CPLX1, PAB3A, APBA1, RIMS1, and GRIN2B) were identified. Hub genes were enriched in synaptic transmission-related pathways and showed good performance for tumor classification. There were significant differences in survival levels between clusters. CONCLUSIONS: The identified m7G-related genes may provide new insight into the treatment and prognosis of LGG.

Identification of natural killer cell-related characteristics to predict the clinical prognosis and immune microenvironment of patients with low-grade glioma.

BACKGROUND: Individuals with low-grade glioma (LGG) have a dismal prognosis, and most patients will eventually progress to high-grade disease. Therefore, it is crucial to accurately determine their prognoses. METHODS: Seventy-nine NK cell genes were downloaded from the LM22 database and univariate Cox regression analysis was utilized to detect NK cell-related genes affecting prognosis. Molecular types were established for LGG using the "ConsensusClusterPlus" R package. The results from a functional enrichment analysis and the immune microenvironment were intensively explored to determine molecular heterogeneity and immune characteristics across distinct subtypes. Furthermore, a RiskScore model was developed and verified using expression profiles of NK cells, and a nomogram consisting of the RiskScore model and clinical traits was constructed. Moreover, pan-cancer traits of NK cells were also investigated. RESULTS: The C1 subtype included the greatest amount of immune infiltration and the poorest prognosis among well-established subtypes. The majority of enriched pathways were those involved in tumor progression, including epithelial-mesenchymal transition and cell cycle pathways. Differentially expressed genes among distinct subtypes were determined and used to develop a novel RiskScore model. This model was able to distinguish low-risk patients with LGG from those with high-risk disease. An accurate nomogram including the RiskScore, disease grade and patient's age was constructed to predict clinical outcomes of LGG patients. Finally, a pan-cancer analysis further highlighted the crucial roles of NK cell-related genes in the tumor microenvironment. CONCLUSIONS: An NK cell-related RiskScore model can accurately predict the prognoses of patients with LGG and provide valuable insights into personalized medicine.

Construction of ceRNA networks with different types of IDH1 mutation status in low-grade glioma patients.

Background: Isocitrate dehydrogenase 1 (IDH1) mutation status is related to the prognosis and immune microenvironment of glioma. Long non-coding ribonucleic acids (lncRNAs) interact with microRNAs (miRNAs), and play roles in the competitive endogenous RNA (ceRNA) network and tumor progression. Methods: Data on low-grade glioma (LGG) IDH1 mutation was acquired from The Cancer Genome Atlas (TCGA). An empirical analysis of differential gene expression was conducted to identify differentially expressed mRNAs (DEmRNAs), differentially expressed miRNAs (DEmiRNAs), and differentially expressed lncRNAs (DElncRNAs). Survival-associated genes were identified by a univariate Cox regression analysis. An enrichment analysis was conducted to explore the gene ontology and pathways of the DEmRNAs. Results: Eighty-eight DEIDH1mRNAs, 88 DEIDH1lncRNAs, and 6 DEIDH1miRNAs were identified to construct a ceRNA network of LGG patients. Validated by Chinese Glioma Genome Atlas and our LGG patients of gene expression and survival, and the colorectal neoplasia differentially expressed (CRNDE), HOXA transcript antisense RNA, myeloid-specific 1 (HOTAIRM1)/miRNA-206a/hepatocyte nuclear factor 4 (HNF4G) axis was determined. Conclusions: We established a ceRNA network by integrating the different IDH1 mutation statuses of LGG patients, and identified HNF4G, CRNDE, and HOTAIRM1 as genes related to the prognosis of and immune infiltration in LGG patients. Our findings suggest that these genes may be targets for LGG treatment, especially for patients with the wild-type IDH1 gene variants.

Autophagy related DNA methylation signature predict clinical prognosis and immune microenvironment in low-grade glioma.

Background: Low-grade glioma (LGG) is a common malignant tumor of the central nervous system. The clinical prognosis of different patients varies greatly, so exploring appropriate markers that affect the prognosis and treatment of LGG is important. The purpose of this study was to identify the potential effect of autophagy-related DNA methylation on the prognosis and immune microenvironment in LGG. Methods: The methylation profile, transcription data and corresponding clinical information of 451 patients with LGG were obtained from The Cancer Genome Atlas (TCGA). Another methylation data and clinical information of 110 patients with LGG from Chinese Glioma Genome Atlas (CGGA) were used as the validation set. Through univariate and multivariate COX regression analysis, we identified the autophagy-related genes (ARGs) associated with methylation levels and prognosis, and established a risk assessment signature. The receiver operating characteristic (ROC) and Kaplan-Meier (KM) survival curve were used to verify the model's effectiveness in predicting prognosis. Patients were divided into low- and high-risk groups based on risk scores. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were used to explore the differences in biological functions between the two groups. ESTIMATE and CIBERSORT algorithms were used to explore differences in immune infiltration and immunotherapy sites. Pearson correlation analysis was used to analyze the relative relationship between methylated cg sites and corresponding genes. Results: A total of 6 ARGs (ARSB, CFLAR, WIPI2, RB1, ERN1, RAB24) were selected that were associated with methylation levels and prognosis. The area under the curve (AUC) =0.96, and the KM survival curve P<0.0001, which proves that the risk assessment model has a good effect in predicting the prognosis of LGG. GO and KEGG enrichment analysis showed that the model mainly involved major histocompatibility complex (MHC) II receptors, antigen processing and presentation, and immune cell differentiation. In addition, we also found differences in immune infiltration and immune checkpoints between high- and low-risk groups. Conclusions: The methylation levels of these 6 ARGs have a strong predictive potential for LGG, and the methylation regulation of ARGs has an important impact on the immune microenvironment of LGGs.

Transition From Diencephalic Syndrome to Hypothalamic Obesity in Children With Suprasellar Low Grade Glioma: A Case Series.

Background: Children with suprasellar low grade glioma (LGG) frequently develop problems to maintain their body weight within the normal range, due to hypothalamic dysfunction. Hypothalamic damage may result in the diencephalic syndrome (DS), characterized by underweight or failure to thrive, but also in hypothalamic obesity (HO). Children with LGG presenting with DS at young age often develop HO later in life. The underlying pathophysiology for this change in body mass index (BMI) is not understood. Previous hypotheses have focused on the tumor or its treatment as the underlying cause. To better understand its etiology, we aimed to relate changes in BMI over time in children with suprasellar LGG presenting with DS to age, tumor progression, treatment, and endocrine function. We hypothesize that the development of HO in children with LGG presenting with DS is related to maturation status of the hypothalamus at time of injury and thus age. Methods: In this retrospective case series, all cases diagnosed in the Netherlands with suprasellar located LGG, currently treated or followed, with a history of DS developing into HO were included. Results: In total, 10 children were included. Median age at LGG diagnosis was 1.5 years (range 0.4-5.5), median BMI SDS was -2.64. The children developed overweight at a median age of 4.5 years (2.2-9.8). The median total difference in BMI SDS between underweight and obesity was +5.75 SDS (4.5-8.7). No association could be found between transition of DS to HO and onset of a pituitary disorder (present in 70.0%), surgery, chemotherapy, or tumor behavior. Two had developed central precocious puberty (CPP), both while having underweight or normal weight. Conclusion: The shift from DS to HO in children with hypothalamic LGG may be associated with age and not to tumor behavior, treatment characteristics or pituitary function. The development of CPP in these children seems not to be related to obesity. Our findings may indicate that the clinical picture of hypothalamic dysfunction reflects the maturation state of the hypothalamus at time of lesioning. Future prospective studies are needed to better understand underlying causative mechanisms of the morbid changes in body weight.

Brain Regeneration Resembles Brain Cancer at Its Early Wound Healing Stage and Diverges From Cancer Later at Its Proliferation and Differentiation Stages.

Gliomas are the most frequent type of brain cancers and characterized by continuous proliferation, inflammation, angiogenesis, invasion and dedifferentiation, which are also among the initiator and sustaining factors of brain regeneration during restoration of tissue integrity and function. Thus, brain regeneration and brain cancer should share more molecular mechanisms at early stages of regeneration where cell proliferation dominates. However, the mechanisms could diverge later when the regenerative response terminates, while cancer cells sustain proliferation. To test this hypothesis, we exploited the adult zebrafish that, in contrast to the mammals, can efficiently regenerate the brain in response to injury. By comparing transcriptome profiles of the regenerating zebrafish telencephalon at its three different stages, i.e., 1 day post-lesion (dpl)-early wound healing stage, 3 dpl-early proliferative stage and 14 dpl-differentiation stage, to those of two brain cancers, i.e., low-grade glioma (LGG) and glioblastoma (GBM), we reveal the common and distinct molecular mechanisms of brain regeneration and brain cancer. While the transcriptomes of 1 dpl and 3 dpl harbor unique gene modules and gene expression profiles that are more divergent from the control, the transcriptome of 14 dpl converges to that of the control. Next, by functional analysis of the transcriptomes of brain regeneration stages to LGG and GBM, we reveal the common and distinct molecular pathways in regeneration and cancer. 1 dpl and LGG and GBM resemble with regard to signaling pathways related to metabolism and neurogenesis, while 3 dpl and LGG and GBM share pathways that control cell proliferation and differentiation. On the other hand, 14 dpl and LGG and GBM converge with respect to developmental and morphogenetic processes. Finally, our global comparison of gene expression profiles of three brain regeneration stages, LGG and GBM exhibit that 1 dpl is the most similar stage to LGG and GBM while 14 dpl is the most distant stage to both brain cancers. Therefore, early convergence and later divergence of brain regeneration and brain cancer constitutes a key starting point in comparative understanding of cellular and molecular events between the two phenomena and development of relevant targeted therapies for brain cancers.

Recurrence- and Malignant Progression-Associated Biomarkers in Low-Grade Gliomas and Their Roles in Immunotherapy.

Despite a generally better prognosis than high-grade glioma (HGG), recurrence and malignant progression are the main causes for the poor prognosis and difficulties in the treatment of low-grade glioma (LGG). It is of great importance to learn about the risk factors and underlying mechanisms of LGG recurrence and progression. In this study, the transcriptome characteristics of four groups, namely, normal brain tissue and recurrent LGG (rLGG), normal brain tissue and secondary glioblastoma (sGBM), primary LGG (pLGG) and rLGG, and pLGG and sGBM, were compared using Chinese Glioma Genome Atlas (CGGA) and Genotype-Tissue Expression Project (GTEx) databases. In this study, 296 downregulated and 396 upregulated differentially expressed genes (DEGs) with high consensus were screened out. Univariate Cox regression analysis of data from The Cancer Genome Atlas (TCGA) yielded 86 prognostically relevant DEGs; a prognostic prediction model based on five key genes (HOXA1, KIF18A, FAM133A, HGF, and MN1) was established using the least absolute shrinkage and selection operator (LASSO) regression dimensionality reduction and multivariate Cox regression analysis. LGG was divided into high- and low-risk groups using this prediction model. Gene Set Enrichment Analysis (GSEA) revealed that signaling pathway differences in the high- and low-risk groups were mainly seen in tumor immune regulation and DNA damage-related cell cycle checkpoints. Furthermore, the infiltration of immune cells in the high- and low-risk groups was analyzed, which indicated a stronger infiltration of immune cells in the high-risk group than that in the low-risk group, suggesting that an immune microenvironment more conducive to tumor growth emerged due to the interaction between tumor and immune cells. The tumor mutational burden and tumor methylation burden in the high- and low-risk groups were also analyzed, which indicated higher gene mutation burden and lower DNA methylation level in the high-risk group, suggesting that with the accumulation of genomic mutations and epigenetic changes, tumor cells continued to evolve and led to the progression of LGG to HGG. Finally, the value of potential therapeutic targets for the five key genes was analyzed, and findings demonstrated that KIF18A was the gene most likely to be a potential therapeutic target. In conclusion, the prediction model based on these five key genes can better identify the high- and low-risk groups of LGG and lay a solid foundation for evaluating the risk of LGG recurrence and malignant progression.

Isocitrate Dehydrogenase Mutant Grade II and III Glial Neoplasms.

Mutations in isocitrate dehydrogenase (IDH) 1 or IDH2 occur in most of the adult low-grade gliomas and, less commonly, in cholangiocarcinoma, chondrosarcoma, acute myeloid leukemia, and other human malignancies. Cancer-associated mutations alter the function of the enzyme, resulting in production of R(-)-2-hydroxyglutarate and broad epigenetic dysregulation. Small molecule IDH inhibitors have received regulatory approval for the treatment of IDH mutant (mIDH) leukemia and are under development for the treatment of mIDH solid tumors. This article provides a current view of mIDH adult astrocytic and oligodendroglial tumors, including their clinical presentation and treatment, and discusses novel approaches and challenges toward improving the treatment of these tumors.

Radiation-induced contrast enhancement following proton radiotherapy for low-grade glioma depends on tumor characteristics and is rarer in children than adults.

BACKGROUND AND PURPOSE: Proton beam radiotherapy (PRT) is used in the treatment of low-grade glioma (LGG) to mitigate long-term sequelae. Following PRT, increased rates of radiation-induced contrast enhancements (RICE) are suspected but poorly understood. MATERIALS AND METHODS: We analyzed consecutive 227 patients (42 children and 185 adults) treated with PRT (54 Gy RBE) for LGG from 2010 to 2020 and followed with serial clinical exams and magnetic resonance imaging for in median 5.6 years. RESULTS: Tumors were graded WHO 1 in a minority (n = 22, 12%) of adults, but a majority of children (n = 29, 69%). In contrast, tumors were graded WHO 2 in the majority (n = 160, 87%) of adults and a minority of children (n = 10, 24%). Five-year overall survival following PRT was 81% in adults and 91% in children. The risk of RICE was 5-fold more frequent in adults (25%) vs. children (5%; p = 0.0043). In children and adults, RICE were symptomatic in 50% and 55% (n = 1 and 26) of cases with CTCAE grade 0 in 47% (n = 23), grade 1 in 25% (n = 12), 0% grade 2 (n = 0) and 29% grade 3 (n = 14), respectively. In adults, RICE risk was associated to WHO grading (8% in WHO grade 1 vs. 24% in WHO grade 2, p = 0.026), independent of age (p = 0.44) and irradiation dose (p = 0.005), but not independent of IDH mutational status. CONCLUSIONS: These data demonstrate effectiveness of PRT for LGG in both children and adults. The RICE risk is lower in children which are a main target group for PRT and differs with WHO grading.

Diffusion tensor imaging, intra-operative neurophysiological monitoring and small craniotomy: Results in a consecutive series of 103 gliomas.

Diffusion tensor imaging (DTI) allows visualization of the main white matter tracts while intraoperative neurophysiological monitoring (IONM) represents the gold standard for surgical resection of gliomas. In recent years, the use of small craniotomies has gained popularity thanks to neuronavigation and to the low morbidity rates associated with shorter surgical procedures. The aim of this study was to review a series of patients operated for glioma using DTI, IONM, and tumor-targeted craniotomies. The retrospective analysis included patients with supratentorial glioma who met the following inclusion criteria: preoperative DTI, intraoperative IONM, tumor-targeted craniotomy, pre- and postoperative MRI, and complete clinical charts. The DTI was performed on a 3T scanner. The IONM included electroencephalography (EEG), transcranial (TC) and/or cortical motor-evoked potentials (MEP), electrocorticography (ECoG), and direct electrical stimulation (DES). Outcomes included postoperative neurological deficits, volumetric extent of resection (EOR), and overall survival (OS). One hundred and three patients (61 men, 42 women; mean age 54 ± 14 years) were included and presented the following WHO histologies: 65 grade IV, 19 grade III, and 19 grade II gliomas. After 3 months, only three patients had new neurological deficits. The median postoperative volume was 0cc (IQR 3). The median OS for grade IV gliomas was 15 months, while for low-grade gliomas it was not reached. In our experience, a small craniotomy and a tumor resection supported by IONM and DTI permitted to achieve satisfactory results in terms of neurological outcomes, EOR, and OS for glioma patients.

Elevated RECQL1 expression predicts poor prognosis and associates with tumor immune infiltration in low-grade glioma.

Background: Dysregulation of RecQ protein-like 1 (RECQL1), a member of the RecQ DNA helicase, has been determined to participate in malignant process of numerous tumors such as immunosuppression and proliferation and may serve as a biomarker for certain malignancies. Nevertheless, whether there is a similar association between RECQL1 and low-grade glioma (LGG) is uncertain. We therefore turned our attention to exploring the association of RECQL1 with tumor immune infiltration and prognostic significance in LGG. Methods: The differential expression analysis of the RecQ DNA helicases was conducted through the GLIOVIS database and GSE4290 dataset, and verified by the Gene Expression Profiling Interactive Analysis 2 database. Kaplan-Meier plots, Univariate and multivariate Cox regression analysis were employed to assess the prognostic value of RECQL1 expression level and other six variables in LGG patients, and subsequently an efficient nomogram model was generated for clinical prediction. Tumor Immune Estimation Resource database and the single sample Gene Set Enrichment Analysis were used to assess the correlation between RECQL1 and immune infiltration of LGG. The biological processes that may be related to RECQL1 in LGG were learned through functional enrichment analysis by Gene Set Enrichment Analysis software. Results: Among the five RecQ DNA helicases detected, only RECQL1 was over-expression in LGG with the most convincing evidence (log2FoldChange >1.5, q value <0.01). High RECQL1 expression demonstrated worse overall survival and progression-free survival of LGG patients (P<0.05). Dysregulation of RECQL1 was an independent prognostic indicator for outcomes of LGG (HR >1.4, P<0.05). RECQL1 may participates in the carcinogenic pathways of LGG such as adherens junction and JAK-STAT signaling pathways. The transcription expression level of RECQL1, was obviously associated with tumor immune infiltrating cells and their marker genes. Conclusions: High RECQL1 expression detected in LGG not only implies adverse clinical outcome of patients, but also correlates with tumor immune infiltration and certain oncogenic pathways. Our study proposes potential novel biomarker and therapeutic target for the treatment of LGG patients.

Response and resistance to BRAFV600E inhibition in gliomas: Roadblocks ahead?

BRAFV600E represents the most common BRAF mutation in all human cancers. Among central nervous system (CNS) tumors, BRAFV600E is mostly found in pediatric low-grade gliomas (pLGG, ~20%) and, less frequently, in pediatric high-grade gliomas (pHGG, 5-15%) and adult glioblastomas (GBM, ~5%). The integration of BRAF inhibitors (BRAFi) in the treatment of patients with gliomas brought a paradigm shift to clinical care. However, not all patients benefit from treatment due to intrinsic or acquired resistance to BRAF inhibition. Defining predictors of response, as well as developing strategies to prevent resistance to BRAFi and overcome post-BRAFi tumor progression/rebound growth are some of the main challenges at present in the field. In this review, we outline current achievements and limitations of BRAF inhibition in gliomas, with a special focus on potential mechanisms of resistance. We discuss future directions of targeted therapy for BRAFV600E mutated gliomas, highlighting how insights into resistance to BRAFi could be leveraged to improve outcomes.

Characteristics of spinal dissemination in adult low-grade glioma: a retrospective cohort study at a single institute.

BACKGROUND: In adults, metastatic spinal dissemination (MSD) in low-grade glioma (LGG) is an unusual phenomenon of the central nervous system. This study sought to investigate the clinical characteristics of adult MSD in LGG and its course. METHODS: A retrospective analysis was performed from June 2011 to July 2021 in adult LGG patients with MSD treated at the Oncology Department of the Beijing Shijitan Hospital, Capital Medical University. The time to MSD and overall survival (OS) were estimated using Kaplan-Meier plotting. A univariate analysis was performed using a logarithmic-rank test, and a multivariate analysis was performed using Cox proportional hazards models. RESULTS: Thirteen adult LGG patients with MSD were enrolled in this retrospective study. Most of the primary tumors were grade II (92.3%) or astrocyte derived (7.7%). The median total OS time from the initial surgery, the time to MSD and the OS time after MSD of the 13 adult LGG patients was 45.0, 15, and 16 months, respectively. CONCLUSIONS: Adult LGG patients with MSD had a poor prognosis. Enhanced magnetic resonance imaging was highly recommended in adult LGG patients. Maybe parts of patients appeared to experience prolonged clinical benefits from systemic salvage chemotherapy and intrathecal injection after MSD. Novel modalities need to be explored to improve the outcomes of patients with MSD.

The Systematic Landscape of Nectin Family and Nectin-Like Molecules: Functions and Prognostic Value in Low Grade Glioma.

Objective: Nectin and nectin-like molecules (Necls) are molecules that are involved in cell-cell adhesion and other vital cellular processes. This study aimed to determine the expression and prognostic value of nectin and Necls in low grade glioma (LGG). Materials and Methods: Differentially expressed nectin and Necls in LGG samples and the relationship of nectin family and Necls expression with prognosis, clinicopathological parameters, and survival were explored using The Cancer Genome Atlas (TCGA), the Chinese Glioma Genome Atlas (CGGA), and Repository of Molecular Brain Neoplasia Data (REMBRANDT) databases. Univariate and multivariate Cox analysis models were performed to construct the prognosis-related gene signature. Kaplan-Meier curves and time-dependent receiver operating characteristic (ROC) curves and multivariate Cox regression analysis, were utilized to evaluate the prognostic capacity of the four-gene signature. Gene ontology (GO)enrichment analysis and Gene Set Enrichment Analyses (GSEA) were performed to further understand the underlying molecular mechanisms. The Tumor Immune Estimation Resource (TIMER) was used to explore the relationship between the four-gene signature and tumor immune infiltration. Results: Several nectin and Necls were differentially expressed in LGG. Kaplan-Meier survival analyses and Univariate Cox regression showed patients with high expression of NECTIN2 and PVR and low expression of CADM2 and NECTIN1 had worse prognosis among TCGA, CGGA, and REMBRANDT database. Then, a novel four-gene signature was built for LGG prognosis prediction. ROC curves, KM survival analyses, and multivariate COX regression indicated the new signature was an independent prognostic indicator for overall survival. Finally, GSEA and GO enrichment analyses revealed that immune-related pathways participate in the molecular mechanisms. The risk score had a strong negative correlation with tumor purity and data of TIMER showed different immune cell proportions (macrophage and myeloid dendritic cell) between high- and low-risk groups. Additionally, signature scores were positively related to multiple immune-related biomarkers (IL 2, IL8 and IFNγ). Conclusion: Our results offer an extensive analysis of nectin and Necls levels and a four-gene model for prognostic prediction in LGG, providing insights for further investigation of CADM2, NECTIN1/2, and PVR as potential clinical and immune targets in LGG.

EASE: Clinical Implementation of Automated Tumor Segmentation and Volume Quantification for Adult Low-Grade Glioma.

The growth rate of non-enhancing low-grade glioma has prognostic value for both malignant progression and survival, but quantification of growth is difficult due to the irregular shape of the tumor. Volumetric assessment could provide a reliable quantification of tumor growth, but is only feasible if fully automated. Recent advances in automated tumor segmentation have made such a volume quantification possible, and this work describes the clinical implementation of automated volume quantification in an application named EASE: Erasmus Automated SEgmentation. The visual quality control of segmentations by the radiologist is an important step in this process, as errors in the segmentation are still possible. Additionally, to ensure patient safety and quality of care, protocols were established for the usage of volume measurements in clinical diagnosis and for future updates to the algorithm. Upon the introduction of EASE into clinical practice, we evaluated the individual segmentation success rate and impact on diagnosis. In its first 3 months of usage, it was applied to a total of 55 patients, and in 36 of those the radiologist was able to make a volume-based diagnosis using three successful consecutive measurements from EASE. In all cases the volume-based diagnosis was in line with the conventional visual diagnosis. This first cautious introduction of EASE in our clinic is a valuable step in the translation of automatic segmentation methods to clinical practice.

Identification of Gender-Specific Molecular Differences in Glioblastoma (GBM) and Low-Grade Glioma (LGG) by the Analysis of Large Transcriptomic and Epigenomic Datasets.

Differences in the incidence and outcome of glioma between males and females are well known, being more striking for glioblastoma (GB) than low-grade glioma (LGG). The extensive and well-annotated data in publicly available databases enable us to analyze the molecular basis of these differences at a global level. Here, we have analyzed The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) databases to identify molecular indicators for these gender-based differences by different methods. Based on the nature of data available/accessible, the transcriptomic profile was studied in TCGA by using DeSeq2 and in CGGA by T-test, after correction based. Only IDH1 wild-type tumors were studied in CGGA. Using weighted gene co-expression network analysis (WGCNA), network analysis was done, followed by the assessment of modular differential connectivity. Differentially affected signaling pathways were identified. The gender-based effects of differentially expressed genes on survival were determined. DNA methylation was studied as an indicator of gender-based epigenetic differences. The results clearly showed gender-based differences in both GB and LGG, whatever method or database was used. While there were differences in the results obtained between databases and methods used, some major signaling pathways such as Wnt signaling and pathways involved in immune processes and the adaptive immune response were common to different assessments. There was also a differential gender-based influence of several genes on survival. Also, the autosomal genes NOX, FRG1BP, and AL354714.2 and X-linked genes such as PUDP, KDM6A, DDX3X, and SYAP1 had differential DNA methylation and expression profile in male and female GB, while for LGG, these included autosomal genes such as CNIH3 and ANKRD11 and X-linked genes such as KDM6A, MAOB, and EIF2S3. Some, such as FGF13 and DDX3X, have earlier been shown to have a role in tumor behavior, though their dimorphic effects in males and females have not been identified. Our study thus identifies several crucial differences between male and female glioma, which could be validated further. It also highlights that molecular studies without consideration of gender can obscure critical elements of biology and emphasizes the importance of parallel but separate analyses of male and female glioma.

Evaluation of magnetic resonance image segmentation in brain low-grade gliomas using support vector machine and convolutional neural network.

BACKGROUND: Image segmentation of brain low-grade glioma (LGG) magnetic resonance imaging (MRI) contributes tremendously to diagnosis, classification and treatment of the disease. A tangible, accurate, reliable and fast image segmentation technique is demanded in clinical diagnosis and research. METHODS: The emerging machine learning technique has been demonstrated its unique capability in the field of medical image processing, including medical image segmentation. Support vector machine (SVM) and convolutional neural network (CNN) are two widely used machine learning methods. In this work, image segmentation tools based on SVM and CNN are developed and evaluated for brain LGG MR image segmentation studies. The segmentation performance in terms of accuracy and cost is quantitatively analyzed and compared between the SVM and CNN techniques developed. RESULTS: Computed on the Google CoLab, each of the 109 SVM models represents an individual patient, is trained using a single image of that patient and takes a few seconds to complete. The CNN model is trained on a drastically larger dataset of 19,760 data augmented images and takes approximately 2 hours to obtain the most optimal result. The SVM models achieved an average and median accuracy of 0.937 and 0.976 respectively, precision of 0.456 and 0.535 respectively, recall of 0.878 and 0.906 respectively, and F1 score of 0.546 and 0.662 respectively. Although the CNN model required a significantly longer calculation time, it surpassed the SVM models in performance in LGG MR image segmentation, achieving an accuracy of 0.998, a precision of 0.999, a recall of 0.999 and an F1 score of 0.999. CONCLUSIONS: This study shows that SVM with appropriate filtering techniques is capable of obtaining reliable and fast segmentation of brain LGG MR images with sufficient accuracy and limited image data. CNN technique outperforms SVM in the accuracy of segmentation with requirements of significantly enlarged data set, long computation time and high-performance computer.