Localizing seizure-susceptible brain regions associated with low-grade gliomas using voxel-based lesion-symptom mapping

Ferroptosis-related gene signature predicts prognosis and immunotherapy in glioma

Aims

Glioma is a highly invasive brain tumor, which makes prognosis challenging and renders patients resistant to various treatments. Induction of cell death is promising in cancer therapy. Ferroptosis, a recently discovered regulated cell death, can be induced for killing glioma cells. However, the prognostic prediction of ferroptosis‐related genes (FRGs) in glioma remains elusive.

Methods

The mRNA expression profiles and gene variation and corresponding clinical data of glioma patients and NON‐TUMOR control were downloaded from public databases. Risk score based on a FRGs signature was constructed in REMBRANDT cohort and validated in other datasets including CGGA‐693, CGGA‐325, and TCGA.

Results

Our results demonstrated that the majority of FRGs was differentially expressed among GBM, LGG, and NON‐TUMOR groups (96.6%). Furthermore, the glioma patients with low‐risk score exhibited a more satisfactory clinical outcome. The better prognosis was also validated in the glioma patients with low‐risk score no matter to which grade they were affiliated. Functional analysis revealed that the high‐risk score group was positively correlated with the enrichment scores for immune checkpoint blockade‐related positive signatures, indicating the critical role of glioma immunotherapy via risk score.

Conclusion

A novel FRGs‐related risk score can predict prognosis and immunotherapy in glioma patients.

Integrated microenvironment-associated genomic profiles identify LRRC15 mediating recurrent glioblastoma-associated macrophages infiltration

Glioblastoma (GBM) is the most common malignant intracranial tumour with intrinsic infiltrative characteristics, which could lead to most patients eventually relapse. The prognosis of recurrent GBM patients remains unsatisfactory. Cancer cell infiltration and their interaction with the tumour microenvironment (TME) could promote tumour recurrence and treatment resistance. In our study, we aimed to identify potential tumour target correlated with rGBM microenvironment based on the gene expression profiles and clinical information of rGBM patients from The Cancer Genome Atlas (TCGA) database. LRRC15 gene with prognostic value was screened by univariate and multivariate analysis, and the correlation between macrophages and LRRC15 was identified as well. Furthermore, the prognosis correlation and immune characteristics of LRRC15 were validated using the Chinese Glioma Genome Atlas (CGGA) database and our clinical tissues by immunochemistry assay. Additionally, we utilized the transwell assay and carboxy fluorescein succinimidyl ester (CFSE) tracking to further confirm the effects of LRRC15 on attracting microglia/macrophages and tumour cell proliferation in the TME. Gene profiles‐based rGBM microenvironment identified that LRRC15 could act in collusion with microglia/macrophages in the rGBM microenvironment to promote the poor prognosis, especially in mesenchymal subtype, indicating the strategies of targeting LRRC15 to improve macrophages‐based immunosuppressive effects could be promising for rGBM treatments.

Association of IDH mutation and 1p19q co-deletion with tumor immune microenvironment in lower-grade glioma

Although the successful clinical trials of immunotherapy show promising strategies for many cancers, its application in glioma has lagged in comparison with the progress seen in other cancers. Both isocitrate dehydrogenase (IDH) mutations and 1p/19q codeletions are critical molecular alterations affecting therapeutic response in lower-grade glioma (LGG). The systematic and comprehensive characterization of the immunological phenotypes with different molecular subtypes is key to improving our understanding and application of immunotherapies in LGG. Here, we collected the RNA-sequencing, somatic mutation, and clinical data from 1,052 patients from The Cancer Genome Atlas and Chinese Glioma Genome Atlas and stratified patients into three genetic subgroups: IDH mutations with 1p/19q codeletions (IDH mut-codel), IDH mutations without 1p/19q codeletions (IDH mut-noncodel), and IDH wild-type. Our evaluations revealed that IDH mutations and 1p/19q codeletions were associated with distinct immunological tumor microenvironments in LGG. In addition, immune cell infiltration, the expression of immune checkpoint and human leukocyte antigen (HLA) gene, and the activity of immune signaling pathways shared gradual increase from IDH mut-codel to IDH wild-type. We further constructed and validated an immune-related prognostic signature that presented high value in predicting the overall survival time in LGG. In conclusion, our study may provide valuable information for immunotherapy strategies in LGG patients.

An Immune-Related Gene Pairs Signature Predicts Prognosis and Immune Heterogeneity in Glioblastoma

Purpose

Glioblastoma is one of the most aggressive nervous system neoplasms. Immunotherapy represents a hot spot and has not been included in standard treatments of glioblastoma. So in this study, we aim to filtrate an immune-related gene pairs (IRGPs) signature for predicting survival and immune heterogeneity.

Methods

We used gene expression profiles and clinical information of glioblastoma patients in the TCGA and CGGA datasets, dividing into discovery and validation cohorts. IRGPs significantly correlative with prognosis were selected to conduct an IRGPs signature. Low and high risk groups were separated by this IRGPs signature. Univariate and multivariate cox analysis were adopted to check whether risk can be a independent prognostic factor. Immune heterogeneity between different risk groups was analyzed via immune infiltration and gene set enrichment analysis (GSEA). Some different expressed genes between groups were selected to determine their relationship with immune cells and immune checkpoints.

Results

We found an IRGPs signature consisting of 5 IRGPs. Different risk based on IRGPs signature is a independent prognostic factor both in the discovery and validation cohorts. High risk group has some immune positive cells and more immune repressive cells than low risk group by means of immune infiltration. We discovered some pathways are more active in the high risk group, leading to immune suppression, drug resistance and tumor evasion. In two specific signaling, some genes are over expressed in high risk group and positive related to immune repressive cells and immune checkpoints, which indicate aggression and immunotherapy resistance.

Conclusion

We identified a robust IRGPs signature to predict prognosis and immune heterogeneity in glioblastoma patients. Some potential targets and pathways need to be further researched to make different patients benefit from personalized immunotherapy.

Comprehensive exploration of tumor mutational burden and immune infiltration in diffuse glioma

BACKGROUND

Immune checkpoint inhibitors (ICIs) have been used as a novel treatment for diffuse gliomas, but the efficacy varies with patients, which may be associated with the tumor mutational burden (TMB) and immune infiltration. We aimed to explore the relationship between the two and their impacts on the prognosis.

METHODS

The data of the training set were downloaded from The Cancer Genome Atlas (TCGA). "DESeq2" R package was used for differential analysis and identification of differentially expressed genes (DEGs). A gene risk score model was constructed based on DEGs, and a nomogram was developed combined with clinical features. With the CIBERSORT algorithm, the relationship between TMB and immune infiltration was analyzed, and an immune risk score model was constructed. Two models were verification in the validation set downloaded from the Chinese Glioma Genome Atlas (CGGA).

RESULTS

Higher TMB was related to worse prognosis, older age, higher grade, and higher immune checkpoint expression. The gene risk score model was constructed based on BIRC5, SAA1, and TNFRSF11B, and their expressions were all negatively correlated with prognosis. The nomogram was developed combined with age and grade. The immune risk score model was constructed based on M0 macrophages, neutrophils, naïve CD4⁺ T cells, and activated mast cells. The proportions of the first two were higher in the high-TMB group and correlated with worse prognosis, while the latter two were precisely opposite.

CONCLUSIONS

In diffuse gliomas, TMB was negatively correlated with prognosis. The association of immune infiltration with TMB and prognosis varied with the type of immune cells. The nomogram and risk score models can accurately predict prognosis. The results can help identify patients suitable for ICIs and potential therapeutic targets, thus improve the treatment of diffuse gliomas.

Oncogene or tumor suppressor? Long noncoding RNAs role in patient's prognosis varies depending on disease type

Functional studies of long noncoding RNAs (lncRNAs) are often performed in the context of only a single cancer type. However, the tissue-specific expression patterns of lncRNAs raise the question of whether lncRNA associations identified in one cancer type are relevant to other cancer types. Here, we examine the relationships between the expression levels of 50 cancer-related lncRNAs and survival data from 24 types of cancer in The Cancer Genome Atlas (TCGA) with the goal of identifying prognosis related lncRNAs. Our results suggest that high expression levels of certain lncRNAs are consistently associated with worse/better survival in a number of cancers, while other lncRNAs have different prognostic roles in different types of cancer. Our analysis also identifies 20 novel unadjusted associations that have not been reported before. In addition, in low-grade glioma (LGG), prognostic-related lncRNAs are identified after conditioning on known clinical biomarker and common therapy, revealing that 2 lncRNAs, FOXP4-AS1, and NEAT1, are associated with temozolomide response-a standard-of-care in LGG. Pathway analysis suggests NF-kB/STAT3 signaling pathway enrichment in LGG patients with high NEAT1 expression and DNA repair/myc gene set enrichment in LGG patients with high expression of FOXP4-AS1. Our work demonstrates the context dependency of lncRNAs across cancer types and highlights a number of lncRNAs as potential novel cancer prognosis markers.

Construction of a Prognostic Gene Signature Associated with Immune Infiltration in Glioma: A Comprehensive Analysis Based on the CGGA

Background

Tumor microenvironment (TME) is closely related to the progression of glioma and the therapeutic effect of drugs on this cancer. The aim of this study was to develop a signature associated with the tumor immune microenvironment using machine learning.

Methods

We downloaded the transcriptomic and clinical data of glioma patients from the Chinese Glioma Genome Atlas (CGGA) databases (mRNAseq_693). The single-sample Gene Set Enrichment Analysis (ssGSEA) database was used to quantify the relative abundance of immune cells. We divided patients into two different infiltration groups via unsupervised clustering analysis of immune cells and then selected differentially expressed genes (DEGs) between the two groups. Survival-related genes were determined using Cox regression analysis. We next randomly divided patients into a training set and a testing set at a ratio of 7 : 3. By integrating the DEGs into least absolute shrinkage and selection operator (LASSO) regression analysis in the training set, we were able to construct a 15-gene signature, which was validated in the testing and total sets. We further validated the signature in the mRNAseq_325 dataset of CGGA.

Results

We identified 74 DEGs associated with tumor immune infiltration, 70 of which were significantly associated with overall survival (OS). An immune-related gene signature was established, consisting of 15 key genes: adenosine triphosphate (ATP)-binding cassette subfamily C member 3 (ABCC3), collagen type IV alpha 1 chain (COL4A1), podoplanin (PDPN), annexin A1 (ANXA1), COL4A2, insulin-like growth factor binding protein 2 (IGFBP2), serpin family A member 3 (SERPINA3), CXXC-type zinc finger protein 11 (CXXC11), junctophilin 3 (JPH3), secretogranin III (SCG3), secreted protein acidic and rich in cysteine (SPARC)-related modular calcium-binding protein 1 (SMOC1), Cluster of Differentiation 14 (CD14), COL1A1, S100 calcium-binding protein A4 (S100A4), and transforming growth factor beta 1 (TGF-β1). The OS of patients in the high-risk group was worse than that of patients in the low-risk group. GSEA showed that interleukin-6 (IL-6)/Janus kinase (JAK)/signal transducer and activator of transcription (STAT3) signaling, interferon gamma (IFN-γ) response, angiogenesis, and coagulation were more highly enriched in the high-risk group and that oxidative phosphorylation was more highly enriched in the low-risk group.

Conclusion

We constructed a stable gene signature associated with immune infiltration to predict the survival rates of glioma patients.

Developing a lncRNA Signature to Predict the Radiotherapy Response of Lower-Grade Gliomas Using Co-expression and ceRNA Network Analysis

BACKGROUND

Lower-grade glioma (LGG) is a type of central nervous system tumor that includes WHO grade II and grade III gliomas. Despite developments in medical science and technology and the availability of several treatment options, the management of LGG warrants further research. Surgical treatment for LGG treatment poses a challenge owing to its often inaccessible locations in the brain. Although radiation therapy (RT) is the most important approach in this condition and offers more advantages compared to surgery and chemotherapy, it is associated with certain limitations. Responses can vary from individual to individual based on genetic differences. The relationship between non-coding RNA and the response to radiation therapy, especially at the molecular level, is still undefined.

METHODS

In this study, using The Cancer Genome Atlas dataset and bioinformatics, the gene co-expression network that is involved in the response to radiation therapy in lower-grade gliomas was determined, and the ceRNA network of radiotherapy response was constructed based on three databases of RNA interaction. Next, survival analysis was performed for hub genes in the co-expression network, and the high-efficiency biomarkers that could predict the prognosis of patients with LGG undergoing radiotherapy was identified.

RESULTS

We found that some modules in the co-expression network were related to the radiotherapy responses in patients with LGG. Based on the genes in those modules and the three databases, we constructed a ceRNA network for the regulation of radiotherapy responses in LGG. We identified the hub genes and found that the long non-coding RNA, DRAIC, is a potential molecular biomarker to predict the prognosis of radiotherapy in LGG.

Multivariate machine learning-based language mapping in glioma patients based on lesion topography

Diffusive and progressive tumor infiltration within language-related areas of the brain induces functional reorganization. However, the macrostructural basis of subsequent language deficits is less clear. To address this issue, lesion topography data from 137 preoperative patients with left cerebral language-network gliomas (81 low-grade gliomas and 56 high-grade gliomas), were adopted for multivariate machine-learning-based lesion-language mapping analysis. We found that tumor location in the left posterior middle temporal gyrus-a bottleneck where both dorsal and ventral language pathways travel-predicted deficits of spontaneous speech (cluster size = 1356 mm³, false discovery rate corrected P < 0.05) and naming scores (cluster size = 1491 mm³, false discovery rate corrected P < 0.05) in the high-grade glioma group. In contrast, no significant lesion-language mapping results were observed in the low-grade glioma group, suggesting a large functional reorganization. These findings suggest that in patients with gliomas, the macrostructural plasticity mechanisms that modulate brain-behavior relationships depend on glioma grade.

Classification of brain arteriovenous malformations located in motor-related areas based on location and anterior choroidal artery feeding

Objective

Surgical management of arteriovenous malformations (AVMs) involving motor cortex or fibre tracts (M-AVMs) is challenging. This study aimed to construct a classification system based on nidus locations and anterior choroidal artery (AChA) feeding to pre-surgically evaluate motor-related and seizure-related outcomes in patients undergoing resection of M-AVMs.

Methods and materials

A total of 125 patients who underwent microsurgical resection of M-AVMs were retrospectively reviewed. Four subtypes were identified based on nidus location: (I) nidus involving the premotor area and/or supplementary motor areas; (II) nidus involving the precentral gyrus; (III) nidus involving the corticospinal tract (CST) and superior to the posterior limb of the internal capsule; (IV) nidus involving the CST at or inferior to the level of posterior limb of the internal capsule. In addition, we divided type IV into type IVa and type IVb according to the AChA feeding. Surgical-related motor deficit (MD) evaluations were performed 1 week (short-term) and 6 months (long-term) after surgery.

Results

The type I patients exhibited the highest incidence (62.0%) of pre-surgical epilepsy among the four subtypes. Multivariate analysis showed that motor-related area subtypes (p=0.004) and diffuse nidus (p=0.014) were significantly associated with long-term MDs. Long-term MDs were significantly less frequent in type I than in the other types. Type IV patients acquired the highest proportion (four patients, 25.0%) of long-term poor outcomes (mRS >2). Type IVb patients showed a significantly higher incidence of post-surgical MDs than type IVa patients (p=0.041). The MDs of type III or IV patients required more recovery time. Of the 62 patients who had pre-surgical seizures, 90.3% (56/62) controlled their seizures well and reached Engel class I after surgery.

Conclusions

Combining the consideration of location and AChA feeding, the classification for M-AVMs is a useful approach for predicting post-surgical motor function and decision-making.

Identification of a Glycolysis-Related LncRNA Signature to Predict Survival in Diffuse Glioma Patients

Glycolysis refers to one of the critical phenotypes of tumor cells, regulating tumor cell phenotypes and generating sufficient energy for glioma cells. A range of noticeable genes [such as isocitrate dehydrogenase (IDH), phosphatase, and tensin homolog (PTEN), or Ras] overall impact cell proliferation, invasion, cell cycle, and metastasis through glycolysis. Moreover, long non-coding RNAs (LncRNAs) are increasingly critical to disease progression. Accordingly, this study aimed to identify whether glycolysis-related LncRNAs have potential prognostic value for glioma patients. First, co-expression network between glycolysis-related protein-coding RNAs and LncRNAs was established according to Pearson correlation (Filter: |r| > 0.5 & P < 0.001). Furthermore, based on univariate Cox regression, the Least Absolute Shrinkage and Selection Operator (LASSO) analysis and multivariate Cox regression, a predictive model were built; vital glycolysis-related LncRNAs were identified; the risk score of every single patient was calculated. Moreover, receiver operating characteristic (ROC) curve analysis, gene set enrichment analysis (GSEA), GO and KEGG enrichment analysis were performed to assess the effect of risk score among glioma patients. 685 cases (including RNA sequences and clinical information) from two different cohorts of the Chinese Glioma Genome Atlas (CGGA) database were acquired. Based on the mentioned methods, the risk score calculation formula was yielded as follows: Risk score = (0.19 × EXP_FOXD2-AS1) + (−0.27 × EXP_AC062021.1) + (−0.16 × EXP_AF131216.5) + (−0.05 × EXP_LINC00844) + (0.11 × EXP_CRNDE) + (0.35 × EXP_LINC00665). The risk score was independently related to prognosis, and every single mentioned LncRNAs was significantly related to the overall survival of patients. Moreover, functional enrichment analysis indicated that the biologic process of the high-risk score was mainly involved in the cell cycle and DNA replication signaling pathway. This study confirmed that glycolysis-related LncRNAs significantly impact poor prognosis and short overall survival and may act as therapeutic targets in the future.

A Novel 10-Gene Signature Predicts Poor Prognosis in Low Grade Glioma

AIM

Low grade glioma (LGG) is a lethal brain cancer with relatively poor prognosis in young adults. Thus, this study was performed to develop novel molecular biomarkers to effectively predict the prognosis of LGG patients and finally guide treatment decisions.

METHODS

survival-related genes were determined by Kaplan-Meier survival analysis and multivariate Cox regression analysis using the expression and clinical data of 506 LGG patients from The Cancer Genome Atlas (TCGA) database and independently validated in a Chinese Glioma Genome Atlas (CGGA) dataset. A prognostic risk score was established based on a linear combination of 10 gene expression levels using the regression coefficients of the multivariate Cox regression models. GSEA was performed to analyze the altered signaling pathways between the high and low risk groups stratified by median risk score.

RESULTS

We identified a total of 1489 genes significantly correlated with patients' prognosis in LGG. The top 5 protective genes were DISP2, CKMT1B, AQP7, GPR162 and CHGB, the top 5 risk genes were SP1, EYA3, ZSCAN20, ITPRIPL1 and ZNF217 in LGG. The risk score was predictive of poor overall survival and relapse-free survival in LGG patients. Pathways of small cell lung cancer, pathways in cancer, chronic myeloid leukemia, colorectal cancer were the top 4 most enriched pathways in the high risk group. SP1, EYA3, ZSCAN20, ITPRIPL1, ZNF217 and GPR162 were significantly up-regulated, while DISP2, CKMT1B, AQP7 were down-regulated in 523 LGG tissues as compared to 1141 normal brain controls.

CONCLUSIONS

The 10-gene signature may become novel prognostic and diagnostic biomarkers to considerably improve the prognostic prediction in LGG.

A machine learning analysis of a "normal-like" IDH-WT diffuse glioma transcriptomic subgroup associated with prolonged survival reveals novel immune and neurotransmitter-related actionable targets

BACKGROUND

Classification of primary central nervous system tumors according to the World Health Organization guidelines follows the integration of histologic interpretation with molecular information and aims at providing the most precise prognosis and optimal patient management. According to the cIMPACT-NOW update 3, diffuse isocitrate dehydrogenase-wild type (IDH-WT) gliomas should be graded as grade IV glioblastomas (GBM) if they possess one or more of the following molecular markers that predict aggressive clinical course: EGFR amplification, TERT promoter mutation, and whole-chromosome 7 gain combined with chromosome 10 loss.

METHODS

The Cancer Genome Atlas (TCGA) glioma expression datasets were reanalyzed in order to identify novel tumor subcategories which would be considered as GBM-equivalents with the current diagnostic algorithm. Unsupervised clustering allowed the identification of previously unrecognized transcriptomic subcategories. A supervised machine learning algorithm (k-nearest neighbor model) was also used to identify gene signatures specific to some of these subcategories.

RESULTS

We identified 14 IDH-WT infiltrating gliomas displaying a "normal-like" (NL) transcriptomic profile associated with a longer survival. Genes such as C5AR1 (complement receptor), SLC32A1 (vesicular gamma-aminobutyric acid transporter), MSR1 (or CD204, scavenger receptor A), and SYT5 (synaptotagmin 5) were differentially expressed and comprised in gene signatures specific to NL IDH-WT gliomas which were validated further using the Chinese Glioma Genome Atlas datasets. These gene signatures showed high discriminative power and correlation with survival.

CONCLUSION

NL IDH-WT gliomas represent an infiltrating glioma subcategory with a superior prognosis which can only be detected using genome-wide analysis. Differential expression of genes potentially involved in immune checkpoint and amino acid signaling pathways is providing insight into mechanisms of gliomagenesis and could pave the way to novel treatment targets for infiltrating gliomas.

LTBP1 plays a potential bridge between depressive disorder and glioblastoma

Background

Glioblastoma multiforme (GBM) is the most malignant tumor in human brain. Diagnosis and treatment of GBM may lead to psychological disorders such as depressive and anxiety disorders. There was no research focusing on the correlation between depressive/anxiety disorder and the outcome of GBM. Thus, the aim of this study was to investigate the possibility of depressive/anxiety disorder correlated with the outcome of GBM patients, as well as the overlapped mechanism bridge which could link depressive/anxiety disorders and GBM.

Methods

Patient Health Questionnaire (PHQ-9) and Generalized Anxiety Disorder (GAD-7) were used to investigate the psychological condition of GBM patients in our department. To further explore the potential mechanism, bioinformatic methods were used to screen out genes that could be indicators of outcome in GBM, followed by gene ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, and protein–protein interaction (PPI) analysis. Further, cellular experiments were conducted to evaluate the proliferation, migration capacity of primary GBM cells from the patients.

Results

It was revealed that patients with higher PHQ-9 and GAD-7 scores had significantly worse prognosis than their lower-scored counterparts. Bioinformatic mining revealed that LTBP1 could be a potential genetic mechanism in both depressive/anxiety disorder and GBM. Primary GBM cells with different expression level of LTBP1 should significantly different proliferation and migration capacity. GO, KEGG analysis confirmed that extracellular matrix (ECM) was the most enriched function of LTBP1. PPI network showed the interaction of proteins altered by LTBP1. Hub genes COL1A2, COL5A1 and COL10A1, as well as mesenchymal marker CD44 and Vimentin were statistically higher expressed in LTBP1 high group; while proneural marker E-cadherin was significantly higher expressed in low LTBP1 group.

Conclusion

There is closely correlation between depressive/anxiety disorders and GBM. LTBP1 could be a potential bridge linking the two diseases through the regulation of ECM.

Aging-related tumor associated fibroblasts changes could worsen the prognosis of GBM patients

Background

Glioblastoma multiforme (GBM) is the most malignant tumor in human brain, with highly heterogeneity among different patients. Age could function as an incidence and prognosis risk factor for many tumors.

Method

A series of bioinformatic experiments were conducted to evaluate the differences of incidence, differential expressed genes, enriched pathways with the data from Surveillance, Epidemiology, and End Results (SEER) program, the cancer genome atlas (TCGA) and Chinese glioma genome atlas (CGGA) project.

Results

We discovered in our present study that distinct difference of incidence and prognosis of different aged GBM patients. By a series of bioinformatic method, we found that the tumor associated fibroblasts (TAFs) was the most crucial tumor microenvironment (TME) component that led to this phenomenon. Epithelial-mesenchymal transition (EMT) could be the mechanism by which TAFs regulate the progression of GBM.

Conclusion

We have proposed a close correlation between age and GBM incidence and prognosis, and propose the underlying mechanism behind this correlation by mining different databases, which laid the foundation for future research.

Integrated regulatory models for inference of subtype-specific susceptibilities in glioblastoma

Glioblastoma multiforme (GBM) is a highly malignant form of cancer that lacks effective treatment options or well-defined strategies for personalized cancer therapy. The disease has been stratified into distinct molecular subtypes; however, the underlying regulatory circuitry that gives rise to such heterogeneity and its implications for therapy remain unclear. We developed a modular computational pipeline, Integrative Modeling of Transcription Regulatory Interactions for Systematic Inference of Susceptibility in Cancer (inTRINSiC), to dissect subtype-specific regulatory programs and predict genetic dependencies in individual patient tumors. Using a multilayer network consisting of 518 transcription factors (TFs), 10,733 target genes, and a signaling layer of 3,132 proteins, we were able to accurately identify differential regulatory activity of TFs that shape subtype-specific expression landscapes. Our models also allowed inference of mechanisms for altered TF behavior in different GBM subtypes. Most importantly, we were able to use the multilayer models to perform an in silico perturbation analysis to infer differential genetic vulnerabilities across GBM subtypes and pinpoint the MYB family member MYBL2 as a drug target specific for the Proneural subtype.

Radiomics Analysis of Postoperative Epilepsy Seizures in Low-Grade Gliomas Using Preoperative MR Images

Purpose: The present study aimed to evaluate the performance of radiomics features in the preoperative prediction of epileptic seizure following surgery in patients with LGG. Methods: This retrospective study collected 130 patients with LGG. Radiomics features were extracted from the T2-weighted MR images obtained before surgery. Multivariable Cox-regression with two nested leave-one-out cross validation (LOOCV) loops was applied to predict the prognosis, and elastic net was used in each LOOCV loop to select the predictive features. Logistic models were then built with the selected features to predict epileptic seizures at two time points. Student's t-tests were then used to compare the logistic model predicted probabilities of developing epilepsy in the epilepsy and non-epilepsy groups. The t-test was used to identify features that differentiated patients with early-onset epilepsy from their late-onset counterparts. Results: Seventeen features were selected with the two nested LOOCV loops. The index of concordance (C-index) of the Cox model was 0.683, and the logistic model predicted probabilities of seizure were significantly different between the epilepsy and non-epilepsy groups at each time point. Moreover, one feature was found to be significantly different between the patients with early- or late-onset epilepsy. Conclusion: A total of 17 radiomics features were correlated with postoperative epileptic seizures in patients with LGG and one feature was a significant predictor of the time of epilepsy onset.

Accuracy and practical aspects of semi- and fully automatic segmentation methods for resected brain areas

Purpose

Precise segmentation of brain lesions is essential for neurological research. Specifically, resection volume estimates can aid in the assessment of residual postoperative tissue, e.g. following surgery for glioma. Furthermore, behavioral lesion-symptom mapping in epilepsy relies on accurate delineation of surgical lesions. We sought to determine whether semi- and fully automatic segmentation methods can be applied to resected brain areas and which approach provides the most accurate and cost-efficient results.

Methods

We compared a semi-automatic (ITK-SNAP) with a fully automatic (lesion_GNB) method for segmentation of resected brain areas in terms of accuracy with manual segmentation serving as reference. Additionally, we evaluated processing times of all three methods. We used T1w, MRI-data of epilepsy patients (n = 27; 11 m; mean age 39 years, range 16–69) who underwent temporal lobe resections (17 left).

Results

The semi-automatic approach yielded superior accuracy (p < 0.001) with a median Dice similarity coefficient (mDSC) of 0.78 and a median average Hausdorff distance (maHD) of 0.44 compared with the fully automatic approach (mDSC 0.58, maHD 1.32). There was no significant difference between the median percent volume difference of the two approaches (p > 0.05). Manual segmentation required more human input (30.41 min/subject) and therefore inferring significantly higher costs than semi- (3.27 min/subject) or fully automatic approaches (labor and cost approaching zero).

Conclusion

Semi-automatic segmentation offers the most accurate results in resected brain areas with a moderate amount of human input, thus representing a viable alternative compared with manual segmentation, especially for studies with large patient cohorts.

Downregulation of LUZP2 Is Correlated with Poor Prognosis of Low-Grade Glioma

Background

LUZP2 is a protein limitedly expressed in the brain and spinal cord, while there are few studies on it in brain tumors. Low-grade glioma (LGG) is one of the most common brain tumors. However, the biological behavior of LGG is not very clear at present. This study was aimed at exploring the role of LUZP2 in LGG.

Methods

By data mining in The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA), the expression, clinical characteristics, and potential regulatory mechanism of LUZP2 in LGG were assessed. The regulatory miRNAs of LUZP2 were predicted using miRDB, TargetScan, and miRTarBase. Meanwhile, the potential biological function of coexpressed genes was investigated by GO and KEGG analyses.

Results

LUZP2 expression was downregulated with the increase of tumor grade (p < 0.05). Low LUZP2 expression independently predicted poor OS in LGG in TCGA cohort and the CGGA part B and part C cohorts (all p < 0.001). Additionally, LUZP2 was targeted by miR-142-5p according to 2 prediction databases and 1 validated database, which was negatively related to LUZP2 mRNA expression (p < 0.001). Kaplan-Meier analyses demonstrated that low miR-142-5p expression was significantly associated with poor OS (p < 0.001). Furthermore, coexpression genes of LUZP2 were significantly involved in nervous system development and metabolic pathways.

Conclusions

LUZP2 may be crucial for nervous system extracellular matrix development and serve as an important clinical biomarker for LGG patients. miR-142-5p upregulation could be the upstream regulator that contributed to LUZP2 downregulation.

IDH mutation-specific radiomic signature in lower-grade gliomas

Unravelling the heterogeneity is the central challenge for glioma precession oncology. In this study, we extracted quantitative image features from T2-weighted MR images and revealed that the isocitrate dehydrogenase (IDH) wild type and mutant lower grade gliomas (LGGs) differed in their expression of 146 radiomic descriptors. The logistic regression model algorithm further reduced these to 86 features. The classification model could discriminate the two types in both the training and validation sets with area under the curve values of 1.0000 and 0.9932, respectively. The transcriptome-radiomic analysis revealed that these features were associated with the immune response, biological adhesion, and several malignant behaviors, all of which are consistent with biological processes that are differentially expressed in IDH wild type and IDH mutant LGGs. Finally, a prognostic signature showed an ability to stratify IDH mutant LGGs into high and low risk groups with distinctive outcomes. By extracting a large number of radiomic features, we identified an IDH mutation-specific radiomic signature with prognostic implications. This radiomic signature may provide a way to non-invasively discriminate lower-grade gliomas as with or without the IDH mutation.

Impact of AVM location on language cortex right-hemisphere reorganization: A voxel-based lesion-symptom mapping study

OBJECTIVES

Cerebral arteriovenous malformations (AVMs) are congenital malformations, and right-sided dominance of the language cortex is not a rare phenomenon for patients with AVM involving language area. We tried to use voxel-based lesion-symptom mapping (VLSM) method to depict the location of AVM nidus and to demonstrate the relationship between AVM location and the pattern of language cortex reorganization.

PATIENTS AND METHODS

The authors retrospectively reviewed clinical and imaging data of 70 adult patients with unruptured cerebral AVMs who underwent blood oxygen level-dependent (BOLD) functional magnetic resonance imaging (fMRI) of language task. All patients were right handed, and all lesions were located in the left cerebral hemisphere. Lateralization indexes (LI) of the BOLD signals were calculated for Broca and Wernicke areas separately and were used to reflect the degree of right-sided dominance of the two language areas. VLSM method was applied to study the relationship between AVM location and LI of language task activations.

RESULTS

Statistical analysis revealed that the change of LI of Broca area was significantly associated with lesions located in the inferior frontal gyrus, pre- and post-central gyrus, supramarginal gyrus and middle frontal gyrus. The change of LI of Wernicke area was significantly associated with lesions located in the left superior, middle, inferior and transverse temporal gyrus.

CONCLUSION

These findings provide new evidence that the language cortex reorganization patterns in AVM patients have anatomic specificity.

Radiogenomics of lower-grade gliomas: a radiomic signature as a biological surrogate for survival prediction

Objective: We aimed to identify a radiomic signature to be used as a noninvasive biomarker of prognosis in patients with lower-grade gliomas (LGGs) and to reveal underlying biological processes through comprehensive radiogenomic investigation. Methods: We extracted 55 radiomic features from T2-weighted images of 233 patients with LGGs (training cohort: n = 85; validation cohort: n = 148). Univariate Cox regression and linear risk score formula were applied to generate a radiomic-based signature. Gene ontology analysis of highly expressed genes in the high-risk score group was conducted to establish a radiogenomic map. A nomogram was constructed for individualized survival prediction.

Results: The six-feature radiomic signature stratified patients in the training cohort into low- or high-risk groups for overall survival (P = 0.0018). This result was successfully verified in the validation cohort (P = 0.0396). Radiogenomic analysis revealed that the prognostic radiomic signature was associated with hypoxia, angiogenesis, apoptosis, and cell proliferation. The nomogram resulted in high prognostic accuracy (C-index: 0.92, C-index: 0.70) and favorable calibration for individualized survival prediction in the training and validation cohorts.

Conclusions: Our results suggest a great potential for the use of radiomic signature as a biological surrogate in providing prognostic information for patients with LGGs.

Seizures in glioma patients: An overview of incidence, etiology, and therapies

Gliomas are fatal brain tumors, and even low-grade gliomas (LGGs) have an average survival of less than a decade. Seizures are a common presentation of gliomas, particularly LGGs, and substantially impact quality of life. Glioma-related seizures differ from other focal epilepsies in their pathogenesis and in the likelihood of refractory epilepsy. We review factors that predict seizure activity and response to treatment, optimal pharmacologic and surgical management of glioma-related epilepsy, and the benefit of using newer anti-seizure medications in patients with gliomas. As surgery is so often beneficial with seizure reduction, we discuss oncologic and epilepsy surgery perspectives. Treatment of gliomas has the potential to ameliorate seizures and increase rates of seizure freedom. Prospective, well-powered studies are needed to provide more definitive answers for practitioners taking care of glioma patients with seizures.

Clinical practice guidelines for the diagnosis and treatment of adult diffuse glioma-related epilepsy

BACKGROUND

Glioma-related epilepsy (GRE) is defined as symptomatic epileptic seizures secondary to gliomas, it brings both heavy financial and psychosocial burdens to patients with diffuse glioma and significantly decreases their quality of life. To date, there have been no clinical guidelines that provide recommendations for the optimal diagnostic and therapeutic procedures for GRE patients.

METHODS

In March 2017, the Joint Task Force for GRE of China Association Against Epilepsy and Society for Neuro-Oncology of China launched the guideline committee for the diagnosis and treatment of GRE. The guideline committee conducted a comprehensive review of relevant domestic and international literatures that were evaluated and graded based on the Oxford Centre for Evidence-Based Medicine Levels of Evidence, and then held three consensus meetings to discuss relevant recommendations. The recommendations were eventually given according to those relevant literatures, together with the experiences in the diagnosis and treatment of over 3000 GRE cases from 24 tertiary level hospitals that specialize in clinical research of epilepsy, glioma, and GRE in China.

RESULTS

The manuscript presented the current standard recommendations for the diagnostic and therapeutic procedures of GRE.

CONCLUSIONS

The current work will provide a framework and assurance for the diagnosis and treatment strategy of GRE to reduce complications and costs caused by unnecessary treatment. Additionally, it can serve as a reference for all professionals involved in the management of patients with GRE.

Inter-rater agreement in glioma segmentations on longitudinal MRI

Background

Tumor segmentation of glioma on MRI is a technique to monitor, quantify and report disease progression. Manual MRI segmentation is the gold standard but very labor intensive. At present the quality of this gold standard is not known for different stages of the disease, and prior work has mainly focused on treatment-naive glioblastoma. In this paper we studied the inter-rater agreement of manual MRI segmentation of glioblastoma and WHO grade II-III glioma for novices and experts at three stages of disease. We also studied the impact of inter-observer variation on extent of resection and growth rate.

Methods

In 20 patients with WHO grade IV glioblastoma and 20 patients with WHO grade II-III glioma (defined as non-glioblastoma) both the enhancing and non-enhancing tumor elements were segmented on MRI, using specialized software, by four novices and four experts before surgery, after surgery and at time of tumor progression. We used the generalized conformity index (GCI) and the intra-class correlation coefficient (ICC) of tumor volume as main outcome measures for inter-rater agreement.

Results

For glioblastoma, segmentations by experts and novices were comparable. The inter-rater agreement of enhancing tumor elements was excellent before surgery (GCI 0.79, ICC 0.99) poor after surgery (GCI 0.32, ICC 0.92), and good at progression (GCI 0.65, ICC 0.91). For non-glioblastoma, the inter-rater agreement was generally higher between experts than between novices. The inter-rater agreement was excellent between experts before surgery (GCI 0.77, ICC 0.92), was reasonable after surgery (GCI 0.48, ICC 0.84), and good at progression (GCI 0.60, ICC 0.80). The inter-rater agreement was good between novices before surgery (GCI 0.66, ICC 0.73), was poor after surgery (GCI 0.33, ICC 0.55), and poor at progression (GCI 0.36, ICC 0.73). Further analysis showed that the lower inter-rater agreement of segmentation on postoperative MRI could only partly be explained by the smaller volumes and fragmentation of residual tumor. The median interquartile range of extent of resection between raters was 8.3% and of growth rate was 0.22 mm/year.

Conclusion

Manual tumor segmentations on MRI have reasonable agreement for use in spatial and volumetric analysis. Agreement in spatial overlap is of concern with segmentation after surgery for glioblastoma and with segmentation of non-glioblastoma by non-experts.

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Inter-rater agreement for longitudinal glioma segmentation was determined.

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Agreement between 4 experts was higher than between 4 novices.

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Three time-points of glioblastoma (WHO IV) and diffuse glioma (WHO II-III) are studied.

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Impact on extent of resection and growth rate measurements was determined.

Predictors of Epilepsy Presentation in Unruptured Brain Arteriovenous Malformations: A Quantitative Evaluation of Location and Radiomics Features on T2-Weighted Imaging

OBJECTIVE

To explore predictors of epilepsy presentation in unruptured brain arteriovenous malformations (bAVMs) with quantitative evaluation of location and radiomics features on T2-weighted imaging.

METHODS

This retrospective study identified 117 patients with unruptured bAVMs. Cases were randomly split into training dataset (n = 90) and test dataset (n = 27). On the training dataset, we applied atlas-based analysis to identify epilepsy-susceptible brain regions of bAVMs, and then applied the radiomics technique to explore shape, intensity, and textural features that were correlated with epilepsy presentation. Informative radiomics predictors were selected by least absolute shrinkage and selection operator with 3-fold cross-validation. A linear classification score was then constructed, and we tested if we could precisely identify epilepsy-susceptible bAVMs with the location and radiomics predictors.

RESULTS

Two brain regions and 4 radiomics features were screened out as predictors for epilepsy. The percent of damage of the right precentral gyrus and the right superior longitudinal fasciculus was associated with epilepsy presentation. The 4 radiomics features were Original_firstorder_Median, Wavelet-LHL_firstorder_InterquartileRange, Wavelet-HHL_firstorder_InterquartileRange, and Wavelet-HHH_glrlm_RunVariance. Epileptogenic bAVMs had larger variance of run lengths, larger median value, and interquartile range of voxel intensities. On the training dataset, these 6 predictors were able to classify epilepsy-susceptible bAVMs with accuracy at 0.822, and the area under the curve was 0.866 (95% confidence interval, 0.791-0.940). On the test dataset, sensitivity, specificity, and accuracy of classification reached 0.786, 0.769, and 0.778, respectively.

CONCLUSIONS

Epilepsy-susceptible bAVMs had distinct locations and radiomics features on T2-weighted imaging.

The Impacts of Tumor and Tumor Associated Epilepsy on Subcortical Brain Structures and Long Distance Connectivity in Patients With Low Grade Glioma

Low grade gliomas in cerebral cortex often cause symptoms related to higher cerebral functions such as attention, memory and executive function before treatment is initiated. Interestingly, focal tumors residing in one cortical region can lead to a diverse range of symptoms, indicating that the impact of a tumor is extended to multiple brain regions. We hypothesize that the presence of focal glioma in the cerebral cortex leads to alterations of distant subcortical areas and essential white matter tracts. In this study, we analyzed diffusion tensor imaging scans in glioma patients to study the effect of glioma on subcortical gray matter nuclei and long-distance connectivity. We found that the caudate nucleus, putamen and thalamus were affected by cortical glioma, displaying both volumetric and diffusion alterations. The cerebellar cortex contralateral to the tumor side also showed significant volume decrease. Additionally, tractography of the cortico-striatal and cortico-thalamic projections shows similar diffusion alterations. Tumor associated epilepsy might be an important contributing factor to the found alterations. Our findings indeed confirm concurrent structural and connectivity abrasions of brain areas distant from brain tumor, and provide insights into the pathogenesis of diverse neurological symptoms in glioma patients.

Molecular profiles of tumor contrast enhancement: A radiogenomic analysis in anaplastic gliomas

The presence of contrast enhancement (CE) on magnetic resonance (MR) imaging is conventionally regarded as an indicator for tumor malignancy. However, the biological behaviors and molecular mechanism of enhanced tumor are not well illustrated. The aim of this study was to investigate the molecular profiles associated with anaplastic gliomas (AGs) presenting CE on postcontrast T1‐weighted MR imaging. In this retrospective database study, RNA sequencing and MR imaging data of 91 AGs from the Cancer Genome Atlas (TCGA) and 64 from the Chinese Glioma Genome Atlas (CGGA) were collected. Gene set enrichment analysis (GSEA), significant analysis of microarray, generalized linear models, and Least absolute shrinkage and selection operator algorithm were used to explore radiogenomic and prognostic signatures of AG patients. GSEA indicated that angiogenesis and epithelial‐mesenchymal transition were significantly associated with post‐CE. Genes driving immune system response, cell proliferation, and focal adhesions were also significantly enriched. Gene ontology of 237 differential genes indicated consistent results. A 48‐gene signature for CE was identified in TCGA and validated in CGGA dataset (area under the curve = 0.9787). Furthermore, seven genes derived from the CE‐specific signature could stratify AG patients into two subgroups based on overall survival time according to corresponding risk score. Comprehensive analysis of post‐CE and genomic characteristics leads to a better understanding of radiology‐pathology correlations. Our gene signature helps interpret the occurrence of radiological traits and predict clinical outcomes. Additionally, we found nine prognostic quantitative radiomic features of CE and investigated the underlying biological processes of them.

Correlation between glioma location and preoperative seizures: a systematic review and meta-analysis

Epilepsy is a common manifestation of glioma patients and negatively impacts on quality of life and neurocognitive function. The risk of preoperative seizures in patients with glioma is currently under discussion. We aimed to evaluate the relationship between tumor locations in the cerebrum and preoperative seizures in patients with glioma. PubMed, EMBASE, Web of Science, China Biology Medicine, and the Cochrane Library were systematically searched from inception to July 15, 2017, for original studies including reports of preoperative seizures in patients with gliomas in different brain regions. The pooled odds ratio (OR) and 95% confidence interval (CI) of the meta-analysis for preoperative seizure risk stratified by cerebrum regions were calculated. The quality of evidence was assessed per outcome, using the approach of the Grades of Recommendation, Assessment, Development and Evaluation. Overall, 4323 participants in 16 population-based studies were included in this meta-analysis. The meta-analysis indicated that gliomas in the frontal lobe (OR = 1.51, 95% CI = 1.09-2.09, P = 0.013) were associated with a higher risk for preoperative seizure compared to occipital lobe involved (OR = 0.53, 95% CI = 0.32-0.88, P = 0.014). Regarding the other three lobe involved gliomas, no difference was found between the incidence of preoperative seizures and tumor location. Current limited data suggest that frontal gliomas were associated with a higher risk of preoperative seizures, while gliomas in the occipital lobe were associated with a lower seizure risk. Further RCT studies recruiting larger sample sizes are required to validate these results and guide clinical practice.

Tumor-related epilepsy: epidemiology, pathogenesis and management

INTRODUCTION

Seizure is a common comorbidity in patients with brain tumor. It may be the presenting symptom or develop after the tumor diagnosis. The underlying pathophysiology of brain tumor-related epilepsy remains poorly understood.

METHODS

A comprehensive literature review of Pubmed English articles from 1980-2017 was performed to summarize current knowledge and treatment options of brain tumor-related epilepsy.

RESULTS

Multiple factors have been found to contribute to tumor-related epilepsy, including tumor type, speed of tumor growth, location, and tumor burden. The underlying pathogenesis of epilepsy is not clear but perturbations in the peri-tumoral regions, both structural and cellular communications, have been implicated.

CONCLUSIONS

Surgical and medical treatments of tumor-related epilepsy remain challenging as additional factors such as the extent of surgical resection, interactions with tumor-related oncological treatments and anti-epileptic medication related side effects need to be considered.

Radiomics analysis allows for precise prediction of epilepsy in patients with low-grade gliomas

Purpose

To investigate the association between imaging features and low-grade gliomas (LGG) related epilepsy, and to propose a radiomics-based model for the prediction of LGG-associated epilepsy.

Methods

This retrospective study consecutively enrolled 286 patients with LGGs (194 in the primary cohort and 92 in the validation cohort). T2-weighted MR images (T2WI) were used to characterize risk factors for LGG-related epilepsy: Tumor location features and 3-D imaging features were determined, following which the interactions between these two kinds of features were analyzed. Elastic net was applied to generate a radiomics signature combining key imaging features associated with the LGG-related epilepsy with the primary cohort, and then a nomogram incorporating radiomics signature and clinical characteristics was developed. The radiomics signature and nomogram were validated in the validation cohort.

Results

A total of 475 features associated with LGG-related epilepsy were obtained for each patient. A radiomics signature with eleven selected features allowed for discriminating patients with epilepsy or not was detected, which performed better than location and 3-D imaging features. The nomogram incorporating radiomics signature and clinical characteristics achieved a high degree of discrimination with area under receiver operating characteristic (ROC) curve (AUC) at 0.8769 in the primary cohort and 0.8152 in the validation cohort. The nomogram also allowed for good calibration in the primary cohort.

Conclusion

We developed and validated an effective prediction model for LGG-related epilepsy. Our results suggested that radiomics analysis may enable more precise and individualized prediction of LGG-related epilepsy.

Surgery guided with intraoperative electrocorticography in patients with low-grade glioma and refractory seizures

OBJECTIVE Using intraoperative electrocorticography (ECoG) to identify epileptogenic areas and improve postoperative seizure control in patients with low-grade gliomas (LGGs) remains inconclusive. In this study the authors retrospectively report on a surgery strategy that is based on intraoperative ECoG monitoring. METHODS A total of 108 patients with LGGs presenting at the onset of refractory seizures were included. Patients were divided into 2 groups. In Group I, all patients underwent gross-total resection (GTR) combined with resection of epilepsy areas guided by intraoperative ECoG, while patients in Group II underwent only GTR. Tumor location, tumor side, tumor size, seizure-onset features, seizure frequency, seizure duration, preoperative antiepileptic drug therapy, intraoperative electrophysiological monitoring, postoperative Engel class, and histological tumor type were compared between the 2 groups. RESULTS Univariate analysis demonstrated that tumor location and intraoperative ECoG monitoring correlated with seizure control. There were 30 temporal lobe tumors, 22 frontal lobe tumors, and 2 parietal lobe tumors in Group I, with 18, 24, and 12 tumors in those same lobes, respectively, in Group II (p < 0.05). In Group I, 74.07% of patients were completely seizure free (Engel Class I), while 38.89% in Group II (p < 0.05). In Group I, 96.30% of the patients achieved satisfactory postoperative seizure control (Engel Class I or II), compared with 77.78% in Group II (p < 0.05). Intraoperative ECoG monitoring indicated that in patients with temporal lobe tumors, most of the epileptic discharges (86.7%) were detected at the anterior part of the temporal lobe. In these patients with epilepsy discharges located at the anterior part of the temporal lobe, satisfactory postoperative seizure control (93.3%) was achieved after resection of the tumor and the anterior part of the temporal lobe. CONCLUSIONS Intraoperative ECoG monitoring provided the exact location of epileptogenic areas and significantly improved postoperative seizure control of LGGs. In patients with temporal lobe LGGs, resection of the anterior temporal lobe with epileptic discharges was sufficient to control seizures.

[Clinical characteristics of epileptic seizures in insular gliomas]

AIM

To study the characteristics of epileptic seizures in insular gliomas.

MATERIAL AND METHODS

Forty-five patients with insular gliomas were examined. The spread of a tumor was established by MRI results and intraoperational findings. A tumor within the insular only was found in 9 out of 45 patients (7 left-sided and 2 right-sided). In 36 patients, a tumor slightly spread into temporal lobe pole and medial-basal regions of the frontal lobe (27 left-sided and 18 right-sided). The control group consisted of 50 patients with tumors of temporal and frontal lobes.

RESULTS

Paroxysmal symptoms were similar in patients with tumors of the insular and patients with tumors of temporal lobes. Seizures in patients with frontal lobe tumors differed significantly from insular and temporal tumors, with the exception of a tumor localized in the opercula area. The following quantitative differences were identified: different forms of unconsciousness were significantly less frequent in symptomatic epilepsy in patients with insular tumor than in epilepsy caused by temporal lobe tumors (36% of patients vs 84% in temporal tumors (p<0.0001)). In patients with insular tumors, olfactory and taste hallucinations occur more often compared to temporal lobe tumors (51% vs 16% (p<0.003). The frequency of paroxysmal seizures of fear and anxiety in patients with those tumors was similar (20% with insular tumors and 14 with temporal tumors). An autonomic component of episeizures did not differ between tumors of both localizations. Olfactory and taste hallucinations were qualitatively similar in insular and temporal lobe tumors: smell and taste were unpleasant or associated with a danger: smell of burning, gas, something spoiled, sour, tart chemistry, taste of somethong metallic, chemical, sour. No pleasant smell or taste were reported.

CONCLUSION

Epileptic seizures in insular tumors had similarities and certain differences compared with temporal seizures that well reflect function of the insula and its links, in the first turn, with limbic system structures.

Activation of the mTOR signaling pathway in peritumoral tissues can cause glioma-associated seizures

Epileptic seizures, the most common symptom accompanying glioma, are closely associated with tumor growth and patient quality of life. However, the association between glioma and glioma-related epilepsy is poorly understood. In fact, findings related to the location of epileptogenicity have been inconsistent in previous studies. We investigated seizure foci in patients with glioma and the corresponding association between glioma-related epilepsy and the tumoral and peritumoral microenvironment. Clinical characteristics, extracellular electrophysiology, immunohistochemistry, and western blots were conducted on 12 patients with glioma; nine patients had histories of preoperative seizures while three did not. Samples from included patients were used to identify seizure foci and mTOR pathway status. Electrophysiological recordings were conducted on 36 samples (tumor, peritumoral, and normal brain tissues) from 12 patients. Interictal-like discharges (ILDs) were observed in seven of nine peritumoral tissues obtained from patients with glioma that had experienced perioperative seizures. No ILDs were observed in any other sample groups. Western blots and immunohistochemistry for mTOR pathway proteins (mTOR and S6k) suggested that the mTOR pathway was activated in peritumoral tissues of patients with seizure history, but inactivated in patients without seizure history. Our results suggest that mTOR pathway expression in peritumoral tissues is associated with tumor-related seizures, thus providing a potential target for therapeutics aimed at simultaneously controlling gliomas and seizures.

Putamen involvement and survival outcomes in patients with insular low-grade gliomas

OBJECTIVE Insular glioma has a unique origin and biological behavior; however, the associations between its anatomical features and prognosis have not been well established. The object of this study was to propose a classification system of insular low-grade gliomas based on preoperative MRI findings and to assess the system's association with survival outcome. METHODS A total of 211 consecutively collected patients diagnosed with low-grade insular gliomas was analyzed. All patients were classified according to whether tumor involved the putamen on MR images. The prognostic role of this novel putaminal classification, as well as that of Yaşargil's classification, was examined using multivariate analyses. RESULTS Ninety-nine cases (46.9%) of insular gliomas involved the putamen. Those tumors involving the putamen, as compared with nonputaminal tumors, were larger (p < 0.001), less likely to be associated with a history of seizures (p = 0.04), more likely to have wild-type IDH1 (p = 0.003), and less likely to be totally removed (p = 0.02). Significant favorable predictors of overall survival on univariate analysis included a high preoperative Karnofsky Performance Scale score (p = 0.02), a history of seizures (p = 0.04), gross-total resection (p = 0.006), nonputaminal tumors (p < 0.001), and an IDH1 mutation (p < 0.001). On multivariate analysis, extent of resection (p = 0.035), putamen classification (p = 0.014), and IDH1 mutation (p = 0.026) were independent predictors of overall survival. No prognostic role was found for Yaşargil's classification. CONCLUSIONS The current study's findings suggest that the putamen classification is an independent predictor of survival outcome in patients with insular low-grade gliomas. This newly proposed classification allows preoperative survival prediction for patients with insular gliomas.

Dysregulation of microRNA-128 expression in WHO grades 2 glioma is associated with glioma-associated epilepsy: Down-regulation of miR-128 induces glioma-associated seizure

OBJECTIVE

Approximately 80% of glioma patients will experience at least one seizure activity during the course of the disease, and because the etiology of glioma-related seizure is most likely multifactorial and complex, it remains poorly understood. MicroRNAs are a class of small noncoding RNAs that function as critical gene regulators. MicroRNA-128 was found to be decreased in glioblastoma, and knockout of the microRNA-128a gene could induce epilepsy in mice. Based on the Chinese Glioma Genome Atlas and previous study, we hypothesized that dysregulation of miR-128 expression may play a role in the pathogenesis of TAE in low-grade glioma.

METHODS

Fifty-three low-grade glioma samples were analyzed for the expression levels of miR-128 using qRT-PCR, and candidate targets of miR-128 (Cacnge2, GRIK3, and GRIN2D) were detected by the 3'-UTR luciferase reporter assay. Four other miRs (miR-9, miR-192a, miR-92a, and miR-451) that showed dysregulation of glioblastoma in the CGGA data were also analyzed.

RESULTS

The microRNA-128 expression levels were down-regulated in low-grade glioma tissue (t-test; p=0.009). Dysregulation of miR-128 expression in low-grade glioma is associated with glioma-associated epilepsy (p=0.006). No statistical significance of miR-9, miR-192a, miR-92a, and miR-451 was found to be associated with LGG.

CONCLUSION

Our results here, together with other recent lines of evidence, indicate that miR-128 is an extremely attractive target for therapy in glioma patients with seizure.

Age-associated brain regions in gliomas: a volumetric analysis

Both age of patients and tumor location are associated with tumor origin, genetic characteristics, and prognosis. The objective of this study was to investigate the relationship between tumor location and age at diagnosis in a large cohort of patients with a primary diagnosis of glioma. We consecutively enrolled a cohort of 200 adults with glioblastoma and another cohort of 200 adults with diffuse low-grade gliomas. The magnetic resonance images of all tumors were manually segmented and then registered to a standard brain space. By using voxel-by-voxel regression analysis, specific brains regions associated with advanced age at tumor diagnosis were localized. In the low-grade gliomas cohort, the brain regions associated with advanced age at tumor diagnosis were mainly located in the right middle frontal region, while a region in the left temporal lobe, particularly at the subgranular zone, was associated with lower age at tumor diagnosis. In the glioblastoma cohort, the brain regions associated with advanced age at tumor diagnosis were mainly located in the temporal lobe, particularly at the posterior region of the subventricular zones. A region in the left inferior frontal region was associated with lower age at tumor diagnosis. Significant differences in the age of patients were found between tumors located in the identified regions and those located elsewhere in both cohorts. The current study demonstrated the correlation between tumor location and age at diagnosis, which implies differences in the origin of gliomas in young and older patients.

Deficiency of very large G-protein-coupled receptor-1 is a risk factor of tumor-related epilepsy: a whole transcriptome sequencing analysis

The majority of patients with low-grade glioma (LGG) experience epileptic seizures as their initial symptom, while the underlying mechanisms of tumor-related seizures are still far from being fully understood. In addition to tumor type and location, genetic changes of LGGs are considered to be influential factors in causing epileptic seizures. Nevertheless, the molecular biomarkers associated with tumor-related epilepsy have rarely been identified. RNA sequence data from 80 patients with histologically confirmed LGG were collected from the Chinese glioma genome atlas database and significant differences in expression levels of 33 genes were found. One of the genes, Very large G-protein-coupled receptor-1 (VLGR1), had been previously associated with seizures. Therefore, we investigated the association between LGG-related epilepsy and VLGR1, which played a role in idiopathic epilepsy. The level of VLGR1 expression was compared between patients with epileptic seizures and those without using the reads per kilobase transcriptome per million method. To evaluate the prognostic role of VLGR1 gene expression, the progression-free survival was determined by the Kaplan-Meier method and a multivariate Cox model. We demonstrated that VLGR1 had a significantly lower expression level in patients with epileptic seizures compared to seizure-free patients (p = 0.003). Furthermore, VLGR1 was highly associated with the presence of seizures in a multivariate statistical model. However, VLGR1 could not serve as an independent prognostic factor to determine progression-free survival of LGG patients. Based on RNA sequence data analysis, our results suggest that low expression of VLGR1 is a significant risk factor of epileptic seizures in patients with LGG.