Screening and Identification of Key Biomarkers in Lower Grade Glioma via Bioinformatical Analysis

Lower-grade glioma (LGG) is a common type of central nervous system tumor. Due to its complicated pathogenesis, the choice and timing of adjuvant therapy after tumor treatment are controversial. This study explored and identified potential therapeutic targets for lower-grade. The bioinformatics method was employed to identify potential biomarkers and LGG molecular mechanisms. Firstly, we selected and downloaded GSE15824, GSE50161, and GSE86574 from the GEO database, which included 40 LGG tissue and 28 normal brain tissue samples. GEO and VENN software identified of 206 codifference expressed genes (DEGs). Secondly, we applied the DAVID online software to investigate the DEG biological function and KEGG pathway enrichment, as well as to build the protein interaction visualization network through Cytoscape and STRING website. Then, the MCODE plug is used in the analysis of 22 core genes. Thirdly, the 22 core genes were analyzed with UNCLA software, of which 18 genes were associated with a worse prognosis. Fourthly, GEPIA was used to analyze the 18 selected genes, and 14 genes were found to be a significantly different expression between LGGs and normal brain tumor samples. Fifthly, hierarchical gene clustering was used to examine the 14 important gene expression differences in different histologies, as well as analysis of the KEGG pathway. Five of these genes were shown to be abundant in the natural killer cell-mediated cytokines (NKCC) and phagosome pathways. The five key genes that may be affected by the immune microenvironment play a crucial role in LGG development.

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Identification and Validation of an Energy Metabolism-Related lncRNA-mRNA Signature for Lower-Grade Glioma

BioMed Research International ◽

10.1155/2020/3708231 ◽

2020 ◽

Vol 2020 ◽

pp. 1-14

Author(s):

Jingwei Zhao ◽

Le Wang ◽

Bo Wei

Keyword(s):

Energy Metabolism ◽

Risk Score ◽

Cox Regression ◽

The Cancer Genome Atlas ◽

Coexpression Network ◽

Normal Brain ◽

Lower Grade ◽

Prognostic Signature ◽

Lower Grade Glioma ◽

Genome Atlas

Energy metabolic processes play important roles for tumor malignancy, indicating that related protein-coding genes and regulatory upstream genes (such as long noncoding RNAs (lncRNAs)) may represent potential biomarkers for prognostic prediction. This study will develop a new energy metabolism-related lncRNA-mRNA prognostic signature for lower-grade glioma (LGG) patients. A GSE4290 dataset obtained from Gene Expression Omnibus was used for screening the differentially expressed genes (DEGs) and lncRNAs (DELs). The Cancer Genome Atlas (TCGA) dataset was used as the prognosis training set, while the Chinese Glioma Genome Atlas (CGGA) was for the validation set. Energy metabolism-related genes were collected from the Molecular Signatures Database (MsigDB), and a coexpression network was established between energy metabolism-related DEGs and DELs to identify energy metabolism-related DELs. Least absolute shrinkage and selection operator (LASSO) analysis was performed to filter the prognostic signature which underwent survival analysis and nomogram construction. A total of 1613 DEGs and 37 DELs were identified between LGG and normal brain tissues. One hundred and ten DEGs were overlapped with energy metabolism-related genes. Twenty-seven DELs could coexpress with 67 metabolism-related DEGs. LASSO regression analysis showed that 9 genes in the coexpression network were the optimal signature and used to construct the risk score. Kaplan-Meier curve analysis showed that patients with a high risk score had significantly worse OS than those with a low risk score (TCGA: HR=3.192, 95%CI=2.182‐4.670; CGGA: HR=1.922, 95%CI=1.431‐2.583). The predictive accuracy of the risk score was also high according to the AUC of the ROC curve (TCGA: 0.827; CGGA: 0.806). Multivariate Cox regression analyses revealed age, IDH1 mutation, and risk score as independent prognostic factors, and thus, a prognostic nomogram was established based on these three variables. The excellent prognostic performance of the nomogram was confirmed by calibration and discrimination analyses. In conclusion, our findings provided a new biomarker for the stratification of LGG patients with poor prognosis.

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NI-06 MOLECULAR DIAGNOSIS, 11C-METHIONINE UPTAKE AND PROGNOSIS IN GRADE 2 AND 3 GLIOMAS

Neuro-Oncology Advances ◽

10.1093/noajnl/vdz039.119 ◽

2019 ◽

Vol 1 (Supplement_2) ◽

pp. ii26-ii26

Author(s):

Kaoru Tamura ◽

Yaeko Furuhashi ◽

Motoki Inaji ◽

Daisuke Kobayashi ◽

Takahiro Ogishima ◽

...

Keyword(s):

Who Classification ◽

Genetic Alterations ◽

Normal Brain ◽

Lower Grade ◽

Who Grade ◽

Oligodendroglial Tumors ◽

Integrated Diagnosis ◽

Lower Grade Glioma ◽

Methionine Uptake ◽

Grade 3

Abstract OBJECT The revised 2016 WHO Classification of Tumours of the Central Nervous System incorporates genetic alterations into the classification system, with the goal of creating more homogenous disease categories with greater prognostic value. In this study, we reclassified 103 consecutive lower grade gliomas using the revised 2016 WHO classification and examined for 11C-methionine uptake and prognosis. METHODS 103 consecutive lower grade glioma patients (Grade 2 in 41 patients, Grade 3 in 62 patients) treated at Tokyo Medical and Dental University Hospital from 2000 to 2018 were included in this study. The IDH1/2, ATRX and 1p19q status were analyzed using tumor samples. The tumor-to-normal ratio (T/N) of 11 C-methionine uptake was calculated by dividing the maximum standardized uptake value (SUV) for the tumor by the mean SUV of the normal brain. RESULT In the integrated diagnosis, 11 astrocytomas and 17 anaplastic astrocytomas were diagnosed as “IDH-mutant”, while 14 astrocytomas and 29 anaplastic astrocytomas were diagnosed as “IDH-wild”. In the 32 oligodendroglial tumors, 12 oligodendrogliomas and 9 anaplastic oligodendrogliomas were diagnosed as “IDH-mutant and 1p/19q-codeleted”. The concordance rate with 1p19q co-deletion and ATRX retention was 94.7%. The median T/N ratios in oligodendroglial tumors with “IDH-mutant and 1p/19q-codeleted” were 1.83 in Grade 2 and 2.83 in grade 3, which were significantly higher than those in astrocytic tumors with “IDH-mutant” (G2: 1.38, G3:1.62). Kaplan-Meier survival analysis revealed that oligodendroglial tumors with “IDH-mutant and 1p/19q-codeleted” had significantly better outcomes regardless of WHO grade. Overall survival was 90.9% at 5 years and 77.9% at 10 years in oligodendroglial tumors with “IDH-mutant and 1p/19q-codeleted”. CONCLUSIONS The results indicated that lower grade glioma categories reclassified with molecular classification correlate with the T/N ratio of methionine and the prognosis.

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Identification of key candidate genes and biological pathways in bladder cancer

PeerJ ◽

10.7717/peerj.6036 ◽

2018 ◽

Vol 6 ◽

pp. e6036 ◽

Cited By ~ 22

Author(s):

Xin Gao ◽

Yinyi Chen ◽

Mei Chen ◽

Shunlan Wang ◽

Xiaohong Wen ◽

...

Keyword(s):

Bladder Cancer ◽

Pathway Analysis ◽

Molecular Mechanisms ◽

Kegg Pathway ◽

Aurora Kinase ◽

Smooth Muscle Contraction ◽

Ppi Network ◽

Tissue Samples ◽

Kegg Pathway Analysis ◽

Key Genes

Background Bladder cancer is a malignant tumor in the urinary system with high mortality and recurrence rates. However, the causes and recurrence mechanism of bladder cancer are not fully understood. In this study, we used integrated bioinformatics to screen for key genes associated with the development of bladder cancer and reveal their potential molecular mechanisms. Methods The GSE7476, GSE13507, GSE37815 and GSE65635 expression profiles were downloaded from the Gene Expression Omnibus database, and these datasets contain 304 tissue samples, including 81 normal bladder tissue samples and 223 bladder cancer samples. The RobustRankAggreg (RRA) method was utilized to integrate and analyze the four datasets to obtain integrated differentially expressed genes (DEGs), and the gene ontology (GO) functional annotation and Kyoto encyclopedia of genes and genomes (KEGG) pathway analysis were performed. Protein-protein interaction (PPI) network and module analyses were performed using Cytoscape software. The OncoLnc online tool was utilized to analyze the relationship between the expression of hub genes and the prognosis of bladder cancer. Results In total, 343 DEGs, including 111 upregulated and 232 downregulated genes, were identified from the four datasets. GO analysis showed that the upregulated genes were mainly involved in mitotic nuclear division, the spindle and protein binding. The downregulated genes were mainly involved in cell adhesion, extracellular exosomes and calcium ion binding. The top five enriched pathways obtained in the KEGG pathway analysis were focal adhesion (FA), PI3K-Akt signaling pathway, proteoglycans in cancer, extracellular matrix (ECM)-receptor interaction and vascular smooth muscle contraction. The top 10 hub genes identified from the PPI network were vascular endothelial growth factor A (VEGFA), TOP2A, CCNB1, Cell division cycle 20 (CDC20), aurora kinase B, ACTA2, Aurora kinase A, UBE2C, CEP55 and CCNB2. Survival analysis revealed that the expression levels of ACTA2, CCNB1, CDC20 and VEGFA were related to the prognosis of patients with bladder cancer. In addition, a KEGG pathway analysis of the top 2 modules identified from the PPI network revealed that Module 1 mainly involved the cell cycle and oocyte meiosis, while the analysis in Module 2 mainly involved the complement and coagulation cascades, vascular smooth muscle contraction and FA. Conclusions This study identified key genes and pathways in bladder cancer, which will improve our understanding of the molecular mechanisms underlying the development and progression of bladder cancer. These key genes might be potential therapeutic targets and biomarkers for the treatment of bladder cancer.

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Identification of Hub Gene GRIN1 Correlated with Histological Grade and Prognosis of Glioma by Weighted Gene Coexpression Network Analysis

BioMed Research International ◽

10.1155/2021/4542995 ◽

2021 ◽

Vol 2021 ◽

pp. 1-22

Author(s):

Aoran Yang ◽

Xinhuan Wang ◽

Yaofeng Hu ◽

Chao Shang ◽

Yang Hong

Keyword(s):

Gene Expression ◽

Network Analysis ◽

Molecular Mechanisms ◽

The Cancer Genome Atlas ◽

Coexpression Network ◽

Normal Brain ◽

Gene Coexpression Network ◽

Gene Coexpression ◽

Coexpression Network Analysis ◽

Core Genes

The function of glutamate ionotropic receptor NMDA type subunit 1 (GRIN1) in neurodegenerative diseases has been widely reported; however, its role in the occurrence of glioma remains less explored. We obtained clinical data and transcriptome data from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA). Hub gene’s expression differential analysis and survival analysis were conducted by browsing the Gene Expression Profiling Interactive Analysis (GEPIA) database, Human Protein Atlas database, and LOGpc database. We conducted a variation analysis of datasets obtained from GEO and TCGA and performed a weighted gene coexpression network analysis (WGCNA) using the R programming language (3.6.3). Kaplan-Meier (KM) analysis was used to calculate the prognostic value of GRIN1. Finally, we conducted Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Using STRING, we constructed a protein–protein interaction (PPI) network. Cytoscape software, a prerequisite of visualizing core genes, was installed, and CytoHubba detected the 10 most tumor-related core genes. We identified 185 differentially expressed genes (DEGs). GO and KEGG enrichment analyses illustrated that the identified DEGs are imperative in different biological functions and ascertained the potential pathways in which the DEGs may be enriched. The overall survival calculated by KM analysis showed that patients with lower expression of GRIN1 had worse prognoses than patients with higher expression of GRIN1 ( p = 0.004 ). The GEPIA and LOGpc databases were used to verify the expression difference of GRIN1 among GBM, LGG, and normal brain tissues. Ultimately, immunohistochemical assay results showed that GRIN1 was detected in normal tissue and not in the tumor specimens. Our results highlight a potential target for glioma treatment and will further our understanding of the molecular mechanisms underlying the treatment of glioma.

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NI-13 Prediction of outcome at the time of discontinuing TMZ-adjuvant therapy in IDH-mutant lower grade glioma using 11C-methinine PET

Neuro-Oncology Advances ◽

10.1093/noajnl/vdab159.075 ◽

2021 ◽

Vol 3 (Supplement_6) ◽

pp. vi20-vi20

Author(s):

Takaaki Beppu ◽

Yuichi Sato ◽

Toshiaki Sasaki ◽

Kazunori Terasaki ◽

Takeshi Iwaya ◽

...

Keyword(s):

Adjuvant Chemotherapy ◽

Univariate Analysis ◽

Methylation Status ◽

Normal Brain ◽

Lower Grade ◽

Diffuse Astrocytoma ◽

Cox Regression Analysis ◽

Cutoff Values ◽

Lower Grade Glioma ◽

Met Pet

Abstract Purpose: This study aimed to clarify whether positron emission tomography with 11C-methyl-L-methionine (11C-met PET) can predict consequential outcomes at the time of discontinuing temozolomide (TMZ)-adjuvant chemotherapy in patients with residual isocitrate dehydrogenase gene (IDH)-mutant lower-grade glioma.Methods: In 30 patients showing residual lesions of IDH-mutant lower grade glioma (16 with diffuse astrocytoma and 14 with anaplastic astrocytoma), we performed 11C-met PET, and calculated the tumor-to-normal brain tissue ratio of standardized uptake values (SUVT/N) at the time of discontinuing TMZ-adjuvant chemotherapy. We determined cutoff values to predict tumor relapse using the receiver operating characteristic curve for various prognostic factors including age, Karnofsky performance scale, number of courses of therapy, residual tumor size, and SUVT/N. The promotor methylation status of O6-methylguanine-DNA methyl-transferase gene (MGMT) was assessed using methylation-specific polymerase chain reaction. Progression-free survival (PFS) was compared between groups divided by cutoff values. Uni- and multivariate analyses were conducted using log-rank testing and Cox regression analysis, respectively.Results: Univariate analysis identified MGMT methylation status (p = 0.04) and an SUVT/N of 1.27 (p = 0.02) as predictors of PFS after TMZ discontinuation. In multivariate analysis, both unmethylated MGMT and SUVT/N ≥ 1.27 remained as strong predictors of unfavorable outcome. Conclusion: The present study suggested that 11C-met PET allows prediction of outcomes comparable to MGMT promotor methylation status at the time of discontinuing TMZ-adjuvant chemotherapy in patients with residual IDH-mutant lower-grade glioma.

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The Solute Carrier Family 7 Genes Are Potential Diagnostic and Prognostic Biomarkers in Lower Grade Glioma

10.21203/rs.3.rs-198130/v1 ◽

2021 ◽

Author(s):

Wentao Liu ◽

Yu Ji ◽

Rijun Ren ◽

Gentang Zhang

Keyword(s):

Gene Expression ◽

Amino Acid ◽

The Cancer Genome Atlas ◽

Normal Brain ◽

Lower Grade ◽

Lower Grade Glioma ◽

Family Gene ◽

Solute Carrier ◽

Genome Atlas ◽

Brain Tissues

Abstract Background: The solute carrier (SLC) 7 family genes are a group of cationic amino acid/glycoprotein transporters and of importance to the maintenance of amino acid nutrition and survival of tumour cells. This study was to investigate the diagnostic values of SLC7 family genes and their associations with overall survival (OS) and relapse-free survival (RFS) in Lower grade glioma (LGG). Methods: SLC7 family gene expression and clinical data were retrieved from The Cancer Genome Atlas and the Chinese Glioma Genome Atlas database. The expression difference of SLC7 family genes was compared between 523 LGG and 1141 normal brain tissues. The associations between gene expression, clinicopathologic factors, patients’ OS and RFS were analysed by various statistical methods in the two datasets. Results: As compared to normal brain tissues, SLC7A10 expression was significantly down-regulated, while SLC7A5, SLC7A7 expression was significantly up-regulated in LGG tissues. Multivariate analysis and validation analysis confirmed that increased SLC7A7 expression was associated with increased mortality (P≤0.001, Odd ratio [OR]:2.66, 95% Confidence interval [CI]: 1.56–4.6). While, increased SLC7A4 and SLC7A14 expression was associated with reduced mortality (P=0.02, OR:0.38, 95% CI: 0.16–0.81; P≤0.001, OR:0.38, 95% CI: 0.21–0.67; respectively). Increased SLC7A11 expression was associated with decreased RFS (P=0.01, OR:0.61, 95% CI: 0.43–0.88). Conclusion: SLC7A5, SLC7A7, SLC7A10 might serve as diagnostic biomarkers in LGG. High SLC7A4, SLC7A7 and SLC7A14 expression is significantly associated with OS. SLC7 family gene expression represents a potentially diagnostic and prognostic biomarker to predict survival in LGG.

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Construction of a ceRNA network in glioma and analysis of its clinical significance

BMC Genomics ◽

10.1186/s12864-021-08035-w ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Guangdong Liu ◽

Haihong Li ◽

Wenyang Ji ◽

Haidong Gong ◽

Yan Jiang ◽

...

Keyword(s):

Differential Expression ◽

Expression Analysis ◽

Molecular Mechanisms ◽

Cox Regression ◽

Differential Expression Analysis ◽

Gene Set Enrichment Analysis ◽

Normal Brain ◽

Central Nervous System Tumor ◽

Cox Regression Analysis ◽

Cerna Network

Abstract Background Glioma is the most common central nervous system tumor with a poor survival rate and prognosis. Previous studies have found that long non-coding RNA (lncRNA) and competitive endogenous RNA (ceRNA) play important roles in regulating various tumor mechanisms. We obtained RNA-Seq data of glioma and normal brain tissue samples from TCGA and GTEx databases and extracted the lncRNA and mRNA expression data. Further, we analyzed these data using weighted gene co-expression network analysis and differential expression analysis, respectively. Differential expression analysis was also carried out on the mRNA data from the GEO database. Further, we predicted the interactions between lncRNA, miRNA, and targeted mRNA. Using the CGGA data to perform univariate and multivariate Cox regression analysis on mRNA. Results We constructed a Cox proportional hazard regression model containing four mRNAs and performed immune infiltration analysis. Moreover, we also constructed a ceRNA network including 21 lncRNAs, two miRNAs, and four mRNAs, and identified seven lncRNAs related to survival that have not been previously studied in gliomas. Through the gene set enrichment analysis, we found four lncRNAs that may have a significant role in tumors and should be explored further in the context of gliomas. Conclusions In short, we identified four lncRNAs with research value for gliomas, constructed a ceRNA network in gliomas, and developed a prognostic prediction model. Our research enhances our understanding of the molecular mechanisms underlying gliomas, providing new insights for developing targeted therapies and efficiently evaluating the prognosis of gliomas.

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Identification of Key Pathways and Genes in the Orai2 Mediated Classical and Mesenchymal Subtype of Glioblastoma by Bioinformatic Analyses

Disease Markers ◽

10.1155/2019/7049294 ◽

2019 ◽

Vol 2019 ◽

pp. 1-13 ◽

Cited By ~ 2

Author(s):

Feng Yuan ◽

Li Yi ◽

Long Hai ◽

Yingshuai Wang ◽

Yihan Yang ◽

...

Keyword(s):

Pathway Analysis ◽

Survival Data ◽

Kegg Pathway ◽

Normal Brain ◽

Lower Grade ◽

Jnk Pathway ◽

Mesenchymal Transition ◽

Crac Channels ◽

Kegg Pathway Analysis ◽

Key Pathways

Background. Ca2+ release-activated Ca2+ channels (CRAC) are the main Ca2+ entry pathway regulating intracellular Ca2+ concentration in a variety of cancer types. Orai2 is the main pore-forming subunit of CRAC channels in central neurons. To explore the role of Orai2 in glioblastoma (GBM), we investigated the key pathways and genes in Orai2-mediated GBM by bioinformatic analyses. Methods. Via The Cancer Genome Atlas (TCGA), French, Sun, and Gene Expression Omnibus (GEO) (GDS3885) datasets, we collected 1231 cases with RNA-seq data and analyzed the functional annotation of Orai2 by gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. Univariate and multivariate survival analyses were applied to 823 patients with survival data. Results. We discovered that Orai2 was markedly upregulated in GBM compared to normal brain samples and lower-grade gliomas (LGG). Survival analysis found that higher expression of Orai2 was independently associated with a worse prognosis of patients with the classical and mesenchymal subtypes of GBM. Simultaneously, Orai2 expression was higher in tumors of the classical and mesenchymal subtypes than other subtypes and was significantly correlated with classical- and mesenchymal-related genes. GO and KEGG pathway analysis revealed that genes significantly correlated with Orai2 were involved in the JNK pathway. Through screening transcriptomic data, we found a strong association between Orai2 and apoptosis, stemness, and an epithelial-mesenchymal transition- (EMT-) like phenotype. Conclusion. As a prognostic factor, Orai2 is obviously activated in the classical and mesenchymal subtypes of GBM and promotes glioma cell self-renewal, apoptosis, and EMT-like by the JNK pathway. These findings indicate that Orai2 could be a candidate prognostic and therapeutic target, especially for the classical and mesenchymal subtypes of GBM.

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Construction of Immune-related lncRNA Prognosis Models and Development Related Networks and Drugs in LGG

10.21203/rs.3.rs-757554/v1 ◽

2021 ◽

Author(s):

Xiaoyu Zhang ◽

Xiaoqin He ◽

Lin Xiong ◽

Yangtao Xu ◽

Wenliang Chen ◽

...

Keyword(s):

Immune Cells ◽

Prognostic Significance ◽

Clinical Staging ◽

Lower Grade ◽

Lasso Regression ◽

Malignant Tumours ◽

Lower Grade Glioma ◽

Patient Prognosis ◽

Core Genes

Abstract Glioma is the most important tumor of the nervous system. LncRNA plays an important role in the occurrence, development, and metastasis of glioma. The immune status of glioma plays an important role in its treatment and prognosis. In lower-grade glioma (LGG), based on the results of the Weighted correlation network analysis (WCGNA) of lncRNA, we used lasso regression, random forest model, and stepwise regression to establish the lncRNA prognostic model: 0.58349*CYTOR + 0. 47804*LINC01831 + 0. 24933*HOTAIRM1 + 0. 73600*AC022034 + 0. 62351*AC104407 = lncRNA model prognosis score (LMPS). Concomitantly, we explored the correlation of LMPS with the Estimation of STromal and Immune cells in MAlignant Tumours using Expression data (ESTIMATE), Tumor IMmune Estimation Resource (TIMER) infiltrating immune cells. We also established the reciprocal network of the model and verified the significant prognostic significance of the hub genes of the network and the significant impact on immunity. Finally, by connectivity Map (cMap) analysis, we obtained the drugs about the WCGNA module where the model is. Our study highlights the close role of the lncRNA model with patient prognosis, immunity, and clinical staging, and can guide immunotherapy. The network is constructed with the model as the core. Its core genes also have a guiding role in patient prognosis, immunity, and treatment. We identified loratadine and fidaxomicin as possible drugs.

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NIMG-47. METABOLIC CHARACTERIZATION OF IDH MUTANT LOWER GRADE GLIOMA USING SPECTROSCOPIC IMAGING OPTIMIZED FOR 2-HYDROXYGLUTARATE DETECTION

Neuro-Oncology ◽

10.1093/neuonc/noaa215.660 ◽

2020 ◽

Vol 22 (Supplement_2) ◽

pp. ii158-ii158

Author(s):

Marisa LaFontaine ◽

Adam Autry ◽

Elizabeth Philips ◽

Llewellyn Jalbert ◽

Javier Villanueva-Meyer ◽

...

Keyword(s):

Tumor Biology ◽

Progression Free Survival ◽

Relevant Information ◽

Metabolic Parameters ◽

Lower Grade ◽

Newly Diagnosed ◽

Tissue Samples ◽

Lower Grade Glioma ◽

Idh Mutations ◽

Significant Difference

Abstract INTRODUCTION Mutations in isocitrate dehydrogenase (IDH1/2) genes are both diagnostic and prognostic biomarkers for lower grade gliomas, which can be noninvasively evaluated via MRS detection of 2-hydroxyglutarate (2HG). A high percentage of lower grade gliomas harbor IDH mutations that have a major impact on tumor biology. METHODS Forty IDH-mutant lower grade glioma patients were recruited and scanned using 2HG-optimized 97ms PRESS spectroscopy prior to surgical resection, whereupon image-guided tissue samples were acquired and analyzed via histopathology. Spectra were processed and quantified using LCModel. Wilcoxon ranksum tests were used to compare the difference in metabolic parameters between patient subgroups. Cox proportional hazard models were used to evaluate the influence of metabolic parameters on progression free survival (PFS). Pearson and Kendall Tau rank correlations were used for metabolites and histopathology parameters. RESULTS Levels of 2HG were quantifiable in 37/40 (92.5%) IDH+ patients, yielding a detection rate of 92.5%. A significant difference in myoinositol/total choline was found between astrocytoma and oligodendroglioma in the newly diagnosed grade II population (N=10/13, p=0.042) and also in the overall population (N=25/15, p=0.022). 2HG/creatine was the only significant prognostic indicator on PFS in newly diagnosed patients (p=0.036, HR=4.435; N=33), who had a median PFS of 1472 days [95%CI 994 1950 days] (15 censored). Significantly decreased glutamate/creatine and increased (2HG+GABA)/creatine (p< 0.0001) were found in the T2 lesion compared to those in the normal appearing white matter. At the location where the biopsy samples were taken, there was a significant relationship between 2HG/creatine and glycine/creatine (0.93±0.01; mean±SD Tau; p=0.0005) as well as 2HG/creatine and glutathione/creatine (0.96±0.02; mean±SD Tau; p=0.001) but no significance between the metabolites and pathology parameters were found. CONCLUSIONS The non-invasive detection of 2HG can provide clinically relevant information for patient management and our study demonstrated both the feasibility of 2HG detection and its relationship with PFS.

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