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

CD97 maintains tumorigenicity of glioblastoma stem cells via mTORC2 signaling and is targeted by CAR Th9 cells

Glioblastoma (GBM) stem cells (GSCs) contribute to poor prognosis in patients with GBM. Identifying molecular markers is crucial for developing targeted therapies. Here, we identify cluster of differentiation 97 (CD97) as an optimal GSC surface antigen for potential targeting by chimeric antigen receptor (CAR) T cell therapy through in vitro antibody screening. CD97 is consistently expressed in all validated patient-derived GSCs and positively correlated with known intracellular GSC markers. Silencing CD97 reduces GSC tumorigenicity-related activities, including self-renewal, proliferation, and tumor progression. Transcriptome analysis reveals that CD97 activates mTORC2, leading to AKT S473 phosphorylation and enhanced expression of the downstream genes ARHGAP1, BZW1, and BZW2. Inhibiting mTORC2 with JR-AB2-011 suppresses GSC tumorigenicity and downstream gene expression. We develop CD97-CAR T helper (Th) 9 cells, which exhibit potent cytotoxic effects in vitro and extend survival in mice. These findings suggest that CD97 is a promising GSC-enriched antigen and that targeting it with CAR Th9 cells offers a potential therapeutic strategy for GBM.

SOCS1: A potential diagnostic and prognostic marker for aggressive gliomas and a new target for immunotherapy

Gliomas, the most common and deadly cancers of the central nervous system, present a unique immunological barrier that severely undermines the effectiveness of immunotherapies. Suppressor of cytokine signaling 1 (SOCS1), belonging to the SOCS protein family and playing a pivotal role in various cancer treatment strategies and is abundant in high-grade gliomas. This study conducted a comparative analysis of SOCS1 and glioma immune checkpoints. It underscores the feasibility of leveraging SOCS1 as a promising diagnostic and prognostic marker for aggressive gliomas, thus offering novel targets for glioma immunotherapy. Comprehensive gene expression analyses and clinical data validations were performed across multiple databases. The expression and biological functions of SOCS1 were examined through an array of techniques including pan-cancer analysis, functional enrichment, gene set variation analysis, and immune microenvironment examination. This was done alongside a comparison of the similarities between SOCS1 and various glioma immune checkpoints. Utilizing clinical information from patients, a bespoke predictive model was developed to further corroborate the prognostic capabilities of SOCS1. The investigation revealed considerable similarities between SOCS1 and several immune checkpoints such as CTLA4, demonstrating SOCS1's role as an independent prognostic factor positively influencing glioma patient outcomes. The inclusion of SOCS1 in the developed predictive model significantly enhanced its precision. Our findings highlight SOCS1's potential as an innovative target for glioma immunotherapy, providing a novel strategy to overcome the immunological barriers posed by gliomas. Furthermore, identifying SOCS1 as a viable diagnostic marker for aggressive gliomas improves the accuracy of prognostic predictions for affected patients.

Advances in Neuroimaging and Multiple Post-Processing Techniques for Epileptogenic Zone Detection of Drug-Resistant Epilepsy

Among the approximately 20 million patients with drug-resistant epilepsy (DRE) worldwide, the vast majority can benefit from surgery to minimize seizure reduction and neurological impairment. Precise preoperative localization of epileptogenic zone (EZ) and complete resection of the lesions can influence the postoperative prognosis. However, precise localization of EZ is difficult, and the structural and functional alterations in the brain caused by DRE vary by etiology. Neuroimaging has emerged as an approach to identify the seizure-inducing structural and functional changes in the brain, and magnetic resonance imaging (MRI) and positron emission tomography (PET) have become routine noninvasive imaging tools for preoperative evaluation of DRE in many epilepsy treatment centers. Multimodal neuroimaging offers unique advantages in detecting EZ, especially in improving the detection rate of patients with negative MRI or PET findings. This approach can characterize the brain imaging characteristics of patients with DRE caused by different etiologies, serving as a bridge between clinical and pathological findings and providing a basis for individualized clinical treatment plans. In addition to the integration of multimodal imaging modalities and the development of special scanning sequences and image post-processing techniques for early and precise localization of EZ, the application of deep machine learning for extracting image features and deep learning-based artificial intelligence have gradually improved diagnostic efficiency and accuracy. These improvements can provide clinical assistance for precisely outlining the scope of EZ and indicating the relationship between EZ and functional brain areas, thereby enabling standardized and precise surgery and ensuring good prognosis. However, most existing studies have limitations imposed by factors such as their small sample sizes or hypothesis-based study designs. Therefore, we believe that the application of neuroimaging and post-processing techniques in DRE requires further development and that more efficient and accurate imaging techniques are urgently needed in clinical practice. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY: Stage 2.

Differential gene expression analysis supports dysregulation of mitochondrial activity as a new perspective for glioblastoma's aggressiveness

Brain cancer is considered one of the most aggressive and lethal types of cancer, including primary tumors, being subdivided into milder forms such as low-grade gliomas and glioblastoma, considered the most aggressive form with higher invasion. Among the hallmarks of glioblastoma, the deregulation of mitochondrial metabolism has not yet been fully elucidated. Therefore, the search for mitochondrial biomarkers that can be used as indicators of the progression of this type of cancer is necessary. The aim of this study was to investigate the difference in gene expression between astrocytoma-type gliomas and glioblastomas, and how genes involved in mitochondrial metabolism can influence the proliferative cascade and be associated with tumor invasion. From the differential analysis of glioblastoma expression when compared to the milder form, 11 differentially expressed genes (DEGs) were found in our study, six of which were upregulated (ATP5MGL, C15orf48, MCUB, TERT, AGXT and CYP27B1) and four downregulated (SLC2A4, GK2, SLC25A48, ETNPPL and HMGCS2). To validate the findings, we used other independent bulk RNA-seq datasets and evaluated the number of normalized counts of the DEGs founded. Among these genes, we highlight that none of them had been reported in glioblastoma until this research, and we suggest these genes as possible biomarkers to be further explored, since they are associated with essential pathways for the tumor, such as glucose metabolization, gluconeogenesis, calcium and vitamin D metabolism, tumor progression and activation of the invasion cascade.

Seizure Burden and Clinical Risk Factors in Glioma-Related Epilepsy: Insights From MRI Voxel-Based Lesion-Symptom Mapping

BACKGROUND

Epilepsy is the most common preoperative symptom in patients with supratentorial gliomas. Identifying tumor locations and clinical factors associated with preoperative epilepsy is important for understanding seizure risk.

PURPOSE

To investigate the key brain areas and risk factors associated with preoperative seizures in glioma patients.

STUDY TYPE

Retrospective.

POPULATION

A total of 735 patients with primary diffuse supratentorial gliomas (372 low grade; 363 high grade) with preoperative MRI and pathology data.

FIELD STRENGTH/SEQUENCE

Axial T2-weighted fast spin-echo sequence at 3.0 T.

ASSESSMENT

Seizure burden was defined as the number of preoperative seizures within 6 months. Tumor and high-signal edema areas on T2 images were considered involved regions. A voxel-based lesion-symptom mapping analysis was used to identify voxels associated with seizure burden. The involvement of peak voxels (those most associated with seizure burden) and clinical factors were assessed as risk factors for preoperative seizure.

STATISTICAL TESTS

Univariable and multivariable binary and ordinal logistic regression analyses and chi-square tests were performed, with results reported as odds ratios (ORs) and 95% confidence intervals. A P-value <0.05 was considered significant.

RESULTS

A total of 448 patients experienced preoperative seizures. Significant seizure burden-related voxels were located in the right hippocampus and left insular cortex (based on 1000 permutation tests), with significant differences observed in both low- and high-grade tumors. Tumor involvement in the peak voxel region was an independent risk factor for an increased burden of preoperative seizures (OR = 6.98). Additionally, multivariable binary logistic regression results indicated that 1p/19q codeletion (OR = 1.51), intermediate tumor volume (24.299-97.066 cm3), and involvement of the peak voxel (OR = 6.06) were independent risk factors for preoperative glioma-related epilepsy.

CONCLUSION

Voxel areas identified through voxel-based lesion-symptom mapping analysis, along with clinical factors, show associations with clinical seizure burden, offering insights for assessing seizure burden for glioma patients.

LEVEL OF EVIDENCE

4 TECHNICAL EFFICACY: Stage 1.

Bioinformatic and clinical experimental assay uncovers resistance and susceptibility mechanisms of human glioblastomas to temozolomide and identifies new combined and individual survival biomarkers outperforming MGMT promoter methylation

Background

Glioblastoma (GBM) is the most aggressive and lethal central nervous system (CNS) tumor. The treatment strategy is mainly surgery and/or radiation therapy, both combined with adjuvant temozolomide (TMZ) chemotherapy. Historically, methylation of MGMT gene promoter is used as the major biomarker predicting individual tumor response to TMZ.

Objectives

This research aimed to analyze genes and molecular pathways of DNA repair as biomarkers for sensitivity to TMZ treatment in GBM using updated The Cancer Genome Atlas (TCGA) data and validate the results on experimental datasets.

Methods

Survival analysis of GBM patients under TMZ therapy and hazard ratio (HR) calculation were used to assess all putative biomarkers on World Health Organization CNS5 reclassified TCGA project collection of molecular profiles and experimental multicenter GBM patient cohort. Pathway activation levels were calculated for 38 DNA repair pathways. TMZ sensitivity pathway was reconstructed using a human interactome model built using pairwise interactions extracted from 51,672 human molecular pathways.

Results

We found that expression/activation levels of seven and six emerging gene/pathway biomarkers served as high-quality positive (HR < 0.61) and negative (HR > 1.63), respectively, patient survival biomarkers performing better than MGMT methylation. Positive survival biomarkers were enriched in the processes of ATM-dependent checkpoint activation and cell cycle arrest whereas negative-in excision DNA repair. We also built and characterized gene pathways which were informative for GBM patient survival following TMZ administration (HR 0.18-0.44, p < 0.0009; area under the curve 0.68-0.9).

Conclusion

In this study, a comprehensive analysis of the expression of 361 DNA repair genes and activation levels of 38 DNA repair pathways revealed 13 potential survival biomarkers with increased prognostic potential compared to MGMT methylation. We algorithmically reconstructed the TMZ sensitivity pathway with strong predictive capacity in GBM.

Molecular mechanisms and diagnostic model of glioma-related epilepsy

Epilepsy is one of the most common symptoms in patients with gliomas; however, the mechanisms underlying its interaction are not yet clear. Moreover, epidemiological studies have not accurately identified patients with glioma-related epilepsy (GRE), and there is an urgent need to identify the molecular mechanisms and markers of its occurrence. We analyzed the demographics, transcriptome, whole-genome, and methylation sequences of 997 patients with glioma, to determine the genetic differences between glioma and GRE patients and to determine the upregulated molecular function, cellular composition, biological processes involved, signaling pathways, and immune cell infiltration. Twelve machine learning algorithms were refined into 113 combinatorial algorithms for building diagnostic recognition models. A total of 342 patients with GRE were identified with WHO grade 2 (174), grade 3 (107), and grade 4 (61). The mean age of the patients with GREs, with IDH mutations (n = 217 [63%]) and 1p19q non-codeletion (n = 169 [49%]), was 38 years old. GRE molecular functions were mainly passive transmembrane transporter protein activity, ion channel activity, and gated channel activity. Cellular components were enriched in the cation-channel and transmembrane transporter complexes. Cerebral cortical development regulates the membrane potential and synaptic organization as major biological processes. The signaling pathways mainly focused on cholinergic, GABAergic, and glutamatergic synapses. LASSO, combined with Random Forest, was the best diagnostic model and identified nine diagnostic genes. This study provides new insights and future perspectives for resolving the molecular mechanisms of GRE.

A novel network-based method identifies a cuproplasia-related pan-cancer gene signature to predict patient outcome

Copper is a vital micronutrient involved in many biological processes and is an essential component of tumour cell growth and migration. Copper influences tumour growth through a process called cuproplasia, defined as abnormal copper-dependent cell-growth and proliferation. Copper-chelation therapy targeting this process has demonstrated efficacy in several clinical trials against cancer. While the molecular pathways associated with cuproplasia are partially known, genetic heterogeneity across different cancer types has limited the understanding of how cuproplasia impacts patient survival. Utilising RNA-sequencing data from The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) datasets, we generated gene regulatory networks to identify the critical cuproplasia-related genes across 23 different cancer types. From this, we identified a novel 8-gene cuproplasia-related gene signature associated with pan-cancer survival, and a 6-gene prognostic risk score model in low grade glioma. These findings highlight the use of gene regulatory networks to identify cuproplasia-related gene signatures that could be used to generate risk score models. This can potentially identify patients who could benefit from copper-chelation therapy and identifies novel targeted therapeutic strategies.

Spatial Distribution of Meningiomas: A Magnetic Resonance Image Atlas

BACKGROUND AND OBJECTIVES

The size and anatomic location of meningiomas have been shown to correlate with distinct clinical manifestations, histopathological subtypes, and surgical risk. However, meningioma anatomic origin sites can be obscured in large tumors and those crossing compartments. We therefore sought to apply unbiased lesion mapping to localize intracranial meningioma distributions and their association with biology and grade.

METHODS

MRI scans, World Health Organization (WHO) grade, and a molecularly Integrated Grade (IG) derived from cytogenetics were analyzed from adult patients with intracranial meningiomas. Semi-automated tumor segmentation was performed on T1-weighted contrast-enhanced MRI. We used the voxel-based lesion mapping technique to generate a meningioma atlas, mapping spatial frequency and correlating with tumor grades.

RESULTS

Of 881 patients with meningioma (median age: 57 years, 68.8% female), 589 were WHO grade 1 (66.8%), 265 WHO grade 2 (30.1%), and 27 WHO grade 3 (3.1%) with a median tumor volume of 14.6 cm3. After molecular reclassification, 585 were IG-1 (66.4%), 160 IG-2 (18.2%), and 136 IG-3 (15.4%). Benign tumors were concentrated in and around the midline anterior skull base while malignant meningiomas were enriched in the falcine/parasagittal region and the sphenoid wing, similar to the distribution when stratified by chromosome 1p loss. Meningiomas exhibited sharper spatial clustering when stratified by the molecular IG than by WHO grade. WHO grade 2 meningiomas divided equally across IG 1-3, with corresponding partition of spatial distribution in the midline anterior skull base (in WHO grade 2, IG-1) and falcine/parasagittal and sphenoid regions (WHO grade 2, IG-3). Meningioma volumes significantly varied across age, sex, and WHO/IG grades.

CONCLUSION

We demonstrate the utility of voxel-based lesion mapping for intracranial tumors, characterizing distinct meningioma distribution patterns across histopathological and molecularly defined grades. Molecular grading associated with sharper tumor spatial clusters, supporting a phenotype-genotype association in meningiomas.

Association of M2 macrophages with EMT in glioma identified through combination of multi-omics and machine learning

Background

The incidence of glioma, a prevalent brain malignancy, is increasing, particularly among the elderly population. This study aimed to elucidate the clinical importance of epithelial-mesenchymal transition (EMT) in gliomas and its association with malignancy and prognosis.

Background

The incidence of glioma, particularly among elderly individuals, is on the rise. The malignancy of glioma is determined not only by the oncogenic properties of tumor cells but also by the composition of the tumor microenvironment, which includes immune system macrophages. The prevalence of M2-type macrophages typically fosters tumor progression, yet the underlying mechanism remains elusive. Our study explored the clinical importance of epithelial-mesenchymal transition (EMT) in gliomas and its association with malignancy and prognosis.

Methods

Our study used the gene set variation analysis (GSVA) algorithm to classify different levels of EMT activation based on the transcriptomic and multi-omics data. Machine learning (ML) and single-cell analysis were integrated into our model for comprehensive analysis. A predictive model was constructed and in vitro experiments were performed to validate our findings.

Results

Our study classified 1,641 samples into two clusters based on EMT activation: the EMT-hot group and the EMT-cold group. The EMT-hot group had elevated copy number loss, tumor mutational burden (TMB), and a poorer survival rate. Conversely, the EMT-cold group showed a better survival rate, likely attributed to lower stromal and immune cell scores, as well as decreased expression of human leukocyte antigen-related genes. Driving genes were identified through weighted gene coexpression network analysis (WGCNA) and dimensionality reduction techniques. These genes were then utilized in the construction of a prognostic model using ML and protein-protein interaction (PPI) network analysis. Furthermore, the impact of the core genes identified through single-cell analysis on glioma prognosis was examined.

Conclusion

Our research underscores the efficacy of our model in predicting glioma prognosis and elucidates the connection between the M2 macrophages and EMT. Additionally, core genes such as LY96, C1QB, LGALS1, CSPG5, S100A8, and CHGB were identified as pivotal for mediating the occurrence of EMT induced by M2 macrophages.

BTK Expression Level Prediction and the High-Grade Glioma Prognosis Using Radiomic Machine Learning Models

We aimed to study whether the Bruton's tyrosine kinase (BTK) expression is correlated with the prognosis of patients with high-grade gliomas (HGGs) and predict its expression level prior to surgery, by constructing radiomic models. Clinical and gene expression data of 310 patients from The Cancer Genome Atlas (TCGA) were included for gene-based prognostic analysis. Among them, contrast-enhanced T1-weighted imaging (T1WI + C) from The Cancer Imaging Archive (TCIA) with genomic data was selected from 82 patients for radiomic models, including support vector machine (SVM) and logistic regression (LR) models. Furthermore, the nomogram incorporating radiomic signatures was constructed to evaluate its clinical efficacy. BTK was identified as an independent risk factor for HGGs through univariate and multivariate Cox regression analyses. Three radiomic features were selected to construct the SVM and LR models, and the validation set showed area under curve (AUCs) values of 0.711 (95% CI, 0.598-0.824) and 0.736 (95% CI, 0.627-0.844), respectively. The median survival times of the high Rad_score and low-Rad_score groups based on LR model were 15.53 and 23.03 months, respectively. In addition, the total risk score of each patient was used to construct a predictive nomogram, and the AUCs calculated from the corresponding time-dependent ROC curves were 0.533, 0.659, and 0.767 for 1, 3, and 5 years, respectively. BTK is an independent risk factor associated with poor prognosis in patients, and the radiomic model constructed in this study can effectively and non-invasively predict preoperative BTK expression levels and patient prognosis based on T1WI + C.

Novel mitochondrial-related gene signature predicts prognosis and immunological status in glioma

Background

Mitochondria are the center of cellular metabolism. The relationship between mitochondria and diseases has also been studied for a long time. However, the prognostic role of mitochondrial-related genes (MRGs) in patients with glioma and their biological effects are still unclear. The aim of the study was to construct a mitochondria-related model to assess prognosis and potential biological effects like immune infiltration, gene pathway and mutation, and give some predictive chemotherapeutic agents.

Methods

The data of 675 patients from The Cancer Genome Atlas (TCGA) database were used to identify MRG signature and construct a prognostic model. After validating its robustness in Chinese Glioma Genome Atlas (CGGA), two risk groups derived from the prognostic model were then conducted with Gene Set Enrichment Analysis (GSEA), immune status, mutation status and chemotherapeutic agents prediction.

Results

The prognostic model built from six gene signatures can successfully predict the prognosis and reflect clinicopathological characteristics. Patients in high-risk group displayed significantly worse overall survival (OS), immunosuppression effects, and mutation markers with worse prognosis. Twelve chemotherapeutic agents with strongly correlated sensitivity and risk scores were selected as potential agents.

Conclusions

The novel MRG signatures (TYMP, TSFM, MGME1, BOLA3, TRMT5, NDUFA9) can predict prognosis and immunological status in glioma.

Stemness subtypes in lower-grade glioma with prognostic biomarkers, tumor microenvironment, and treatment response

Our research endeavors are directed towards unraveling the stem cell characteristics of lower-grade glioma patients, with the ultimate goal of formulating personalized treatment strategies. We computed enrichment stemness scores and performed consensus clustering to categorize phenotypes. Subsequently, we constructed a prognostic risk model using weighted gene correlation network analysis (WGCNA), random survival forest regression analysis as well as full subset regression analysis. To validate the expression differences of key genes, we employed experimental methods such as quantitative Polymerase Chain Reaction (qPCR) and assessed cell line proliferation, migration, and invasion. Three subtypes were assigned to patients diagnosed with LGG. Notably, Cluster 2 (C2), exhibiting the poorest survival outcomes, manifested characteristics indicative of the subtype characterized by immunosuppression. This was marked by elevated levels of M1 macrophages, activated mast cells, along with higher immune and stromal scores. Four hub genes-CDCA8, ORC1, DLGAP5, and SMC4-were identified and validated through cell experiments and qPCR. Subsequently, these validated genes were utilized to construct a stemness risk signature. Which revealed that Lower-Grade Glioma (LGG) patients with lower scores were more inclined to demonstrate favorable responses to immune therapy. Our study illuminates the stemness characteristics of gliomas, which lays the foundation for developing therapeutic approaches targeting CSCs and enhancing the efficacy of current immunotherapies. By identifying the stemness subtype and its correlation with prognosis and TME patterns in glioma patients, we aim to advance the development of personalized treatments, enhancing the ability to predict and improve overall patient prognosis.

A novel disulfide death-related genes prognostic signature identifies the role of IPO4 in glioma progression

BACKGROUND

"Disulfide death," a form of cellular demise, is triggered by the abnormal accumulation of intracellular disulfides under conditions of glucose deprivation. However, its role in the prognosis of glioma remains undetermined. Therefore, the main objective of this study is to establish prognostic signature based on disulfide death-related genes (DDRGs) and to provide new solutions in choosing the effective treatment of glioma.

METHODS

The RNA transcriptome, clinical information, and mutation data of glioma samples were sourced from The Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA), while normal samples were obtained from the Genotype-Tissue Expression (GTEx). DDRGs were compiled from previous studies and selected through differential analysis and univariate Cox regression analysis. The molecular subtypes were determined through consensus clustering analysis. Further, LASSO analysis was employed to select characteristic genes, and subsequently, a risk model comprising seven DDRGs was constructed based on multivariable Cox analysis. Kaplan-Meier survival curves were employed to assess survival differences between high and low-risk groups. Additionally, functional analyses (GO, KEGG, GSEA) were conducted to explore the potential biological functions and signaling pathways of genes associated with the model. The study also explored immune checkpoint (ICP) genes, immune cell infiltration levels, and immune stromal scores. Finally, the effect of Importin-4(IPO4) on glioma has been further confirmed through RT-qPCR, Western blot, and cell functional experiments.

RESULTS

7 genes associated with disulfide death were obtained and two subgroups of patients with different prognosis and clinical characteristics were identified. Risk signature was subsequently developed and proved to serve as an prognostic predictor. Notably, the high-risk group exhibited an immunosuppressive microenvironment characterized by a high concentration of M2 macrophages and regulatory T cells (Tregs). In contrast, the low-risk group showed lower half-maximal inhibitory concentration (IC50) values. Therefore, patients in the high-risk group may benefit more from immunotherapy, while patients in the low-risk group may benefit more from chemotherapy. In addition, in vitro experiments have shown that inhibition of the expression of IPO4 leads to a significant reduction in the proliferation, migration, and invasion of glioma cells.

CONCLUSION

This study identified two glioma subtypes and constructed a prognostic signature based on DDRGs. The signature has the potential to optimize the selection of patients for immune- and chemotherapy and provided a potential therapeutic target for glioma.

Efficacy of Personalized Postoperative Epilepsy Management in Patients with Glioblastoma Utilizing IDH1 Gene Assessment

Objective

We explored the correlation between the presence of isocitrate dehydrogenase-1 (IDH1) mutations and the incidence of postoperative epilepsy in patients with glioblastoma, as well as assessed the efficacy of preemptive administration of antiepileptic medications in mitigating the occurrence of postoperative epilepsy.

Methods

Fifty-three patients who received a postoperative pathological diagnosis of glioblastoma, were enrolled in this study. Tumor specimens were subjected to IDH1 gene analysis. The patient cohort was stratified based on their IDH1 mutation status and the administration of prophylactic antiepileptic drugs during the postoperative phase. We subsequently conducted a comparative analysis of postoperative epileptic complications within each patient subgroup.

Results

In the cohort of 53 patients under study, the occurrence of epilepsy was observed in 10 out of 21 patients carrying IDH1 mutations, while 5 out of 32 patients with wild-type IDH1 also experienced epilepsy, revealing a statistically significant difference (P < 0.05). Among the 27 patients who received prophylactic antiepileptic drugs, 6 of them developed epilepsy, whereas 9 out of 26 patients who did not receive prophylactic antiepileptic drugs exhibited concurrent epilepsy, with no statistically significant difference (P > 0.05). However, when performing a subgroup analysis, it was found that 3 out of 12 patients with IDH1 mutations who received prophylactic antiepileptic drugs experienced epilepsy, whereas 7 out of 9 patients who did not receive prophylactic antiepileptic drugs developed epilepsy, demonstrating a statistically significant difference (P < 0.05). Furthermore, within the group of 15 patients with wild-type IDH1, 3 patients who received prophylactic antiepileptic drugs developed epilepsy, while 2 cases of epilepsy occurred among the 17 patients who did not receive prophylactic antiepileptic drugs, with no statistically significant difference (P > 0.05).

Conclusion

In individuals with IDH1 mutant glioblastoma who have undergone surgical resection, the implementation of preventive antiepileptic therapy demonstrates a potential to diminish the occurrence of postoperative epilepsy.

Tumor suppressor role of the complement inhibitor CSMD1 and its role in TNF-induced neuroinflammation in gliomas

BACKGROUND

The complement inhibitor CSMD1 acts as a tumor suppressor in various types of solid cancers. Despite its high level of expression in the brain, its function in gliomas, malignant brain tumors originating from glial cells, has not been investigated.

METHODS

Three cohorts of glioma patients comprising 1500 patients were analyzed in our study along with their clinical data. H4, U-118 and U-87 cell lines were used to investigate the tumor suppressor function of CSMD1 in gliomas. PDGFB-induced brain tumor model was utilized for the validation of in vitro data.

RESULTS

The downregulation of CSMD1 expression correlated with reduced overall and disease-free survival, elevated tumor grade, wild-type IDH genotype, and intact 1p/19q status. Moreover, enhanced activity was noted in the neuroinflammation pathway. Importantly, ectopic expression of CSMD1 in glioma cell lines led to decreased aggressiveness in vitro. Mechanically, CSMD1 obstructed the TNF-induced NF-kB and STAT3 signaling pathways, effectively suppressing the secretion of IL-6 and IL-8. There was also reduced survival in PDGFB-induced brain tumors in mice when Csmd1 was downregulated.

CONCLUSIONS

Our study has identified CSMD1 as a tumor suppressor in gliomas and elucidated its role in TNF-induced neuroinflammation, contributing to a deeper understanding of glioma pathogenesis.

Identification of PANoptosis-related predictors for prognosis and tumor microenvironment by multiomics analysis in glioma

PANoptosis is a newly described inflammatory programmed cell death, that highlights coordination between pyroptosis, apoptosis and necroptosis. However, the functions of PANoptosis-related genes in glioma progression still remain to be explored. This study aims to identify PANoptosis-related predictors that may be utilized for prognosis prediction and development of new therapeutic targets. Firstly, bulk and single-cell RNA-seq (scRNA-seq) data of glioma patients were extracted from TCGA, CGGA and GEO database. Genetic analysis indicates a considerably high mutation frequency of PANoptosis-related genes (PANRGs) in glioma. Consensus clustering was applied to reveal different subtypes of glioma based on PANRGs. Two PANoptosis subtypes with distinct prognostic and TME characteristics were identified. Then, with LASSO-Cox regression analysis, four PANoptosis-related predictors (MYBL2, TUBA1C, C21orf62 and KCNIP2) were determined from bulk and scRNA-seq analysis. Predictive PANRG score model was established with these predictors and its correlation with tumor microenvironment (TME) was investigated. The results showed that patients with low PANRG score, had higher infiltration of anti-tumor immune cells, higher MSI score and lower TIDE score, which are more likely to benefit from immunotherapy. Further analysis identified 16 potential drugs associated with PANoptosis-related predictors. Moreover, the expression levels of four PANoptosis-related predictors were examined in clinical samples and the results were consistent with those analyzed in the database. Besides, we also confirmed the biological functions of two oncogenic predictors (MYBL2 and TUBA1C) by cell experiments, which revealed that knockdown of MYBL2 or TUBA1C could significantly inhibit the proliferation and migration of glioma cells. These findings highlight the prognostic value and biological functions of PANRGs in glioma, which may provide valuable insights for individualized treatment.

Integrated bioinformatics analysis and experimental validation identified CDCA families as prognostic biomarkers and sensitive indicators for rapamycin treatment of glioma

The cell division cycle associated (CDCA) genes regulate the cell cycle; however, their relationship with prognosis in glioma has been poorly reported in the literature. The Cancer Genome Atlas (TCGA) was utilized to probe the CDCA family in relation to the adverse clinical features of glioma. Glioma single-cell atlas reveals specific expression of CDCA3, 4, 5, 8 in malignant cells and CDCA7 in neural progenitor cells (NPC)-like malignant cells. Glioma data from TCGA, the China Glioma Genome Atlas Project (CGGA) and the gene expression omnibus (GEO) database all demonstrated that CDCA2, 3, 4, 5, 7 and 8 are prognostic markers for glioma. Further analysis identified CDCA2, 5 and 8 as independent prognostic factors for glioma. Lasso regression-based risk models for CDCA families demonstrated that high-risk patients were characterized by high tumor mutational burden (TMB), low levels of microsatellite instability (MSI), and low tumor immune dysfunction and rejection (TIDE) scores. These pointed to immunotherapy for glioma as a potentially viable treatment option Further CDCA clustering suggested that the high CDCA subtype exhibited a high macrophage phenotype and was associated with a higher antigen presentation capacity and high levels of immune escape. In addition, hsa-mir-15b-5p was predicted to be common regulator of CDCA3 and CDCA4, which was validated in U87 and U251 cells. Importantly, we found that CDCAs may indicate response to drug treatment, especially rapamycin, in glioma. In summary, our results suggest that CDCAs have potential applications in clinical diagnosis and as drug sensitivity markers in glioma.

Identification of MDK as a Hypoxia- and Epithelial-Mesenchymal Transition-Related Gene Biomarker of Glioblastoma Based on a Novel Risk Model and In Vitro Experiments

BACKGROUND

Tumor cells are commonly exposed to a hypoxic environment, which can easily induce the epithelial-mesenchymal transition (EMT) of tumor cells, further affecting tumor proliferation, invasion, metastasis, and drug resistance. However, the predictive role of hypoxia and EMT-related genes in glioblastoma (GBM) has not been investigated.

METHODS

Intersection genes were identified by weighted correlation network analysis (WGCNA) and differential expression analyses, and a risk model was further constructed by LASSO and Cox analyses. Clinical, immune infiltration, tumor mutation, drug treatment, and enrichment profiles were analyzed based on the risk model. The expression level of the MDK gene was tested using RT-PCR, immunohistochemistry, and immunofluorescence. CCK8 and EdU were employed to determine the GBM cells' capacity for proliferation while the migration and invasion ability were detected by a wound healing assay and transwell assay, respectively.

RESULTS

Based on the GBM data of the TCGA and GTEx databases, 58 intersection genes were identified, and a risk model was constructed. The model was verified in the CGGA cohort, and its accuracy was confirmed by the ROC curve (AUC = 0.807). After combining clinical subgroups, univariate and multivariate Cox regression analyses showed that risk score and age were independent risk factors for GBM patients. Furthermore, our subsequent analysis of immune infiltration, tumor mutation, and drug treatment showed that risk score and high- and low-risk groups were associated with multiple immune cells, mutated genes, and drugs. Enrichment analysis indicated that the differences between high- and low-risk groups were manifested in tumor-related pathways, including the PI3K-AKT and JAK-STAT pathways. Finally, in vivo experiments proved that the hypoxia environment promoted the expression of MDK, and MDK knockdown reduced the proliferation, migration, and EMT of GBM cells induced by hypoxia.

CONCLUSIONS

Our novel prognostic correlation model provided more potential treatment strategies for GBM patients.

Glioblastoma pseudoprogression and true progression reveal spatially variable transcriptional differences

Post-resection radiologic monitoring to identify areas of new or progressive enhancement concerning for cancer recurrence is critical during patients with glioblastoma follow-up. However, treatment-related pseudoprogression presents with similar imaging features but requires different clinical management. While pathologic diagnosis is the gold standard to differentiate true progression and pseudoprogression, the lack of objective clinical standards and admixed histologic presentation creates the needs to (1) validate the accuracy of current approaches and (2) characterize differences between these entities to objectively differentiate true disease. We demonstrated using an online RNAseq repository of recurrent glioblastoma samples that cancer-immune cell activity levels correlate with heterogenous clinical outcomes in patients. Furthermore, nCounter RNA expression analysis of 48 clinical samples taken from second neurosurgical resection supports that pseudoprogression gene expression pathways are dominated with immune activation, whereas progression is predominated with cell cycle activity. Automated image processing and spatial expression analysis however highlight a failure to apply these broad expressional differences in a subset of cases with clinically challenging admixed histology. Encouragingly, applying unsupervised clustering approaches over our segmented histologic images provides novel understanding of morphologically derived differences between progression and pseudoprogression. Spatially derived data further highlighted polarization of myeloid populations that may underscore the tumorgenicity of novel lesions. These findings not only help provide further clarity of potential targets for pathologists to better assist stratification of progression and pseudoprogression, but also highlight the evolution of tumor-immune microenvironment changes which promote tumor recurrence.

Unveiling the Influence of Tumor Microenvironment and Spatial Heterogeneity on Temozolomide Resistance in Glioblastoma Using an Advanced Human In Vitro Model of the Blood-Brain Barrier and Glioblastoma

Glioblastoma (GBM) is the most common primary malignant brain cancer in adults with a dismal prognosis. Temozolomide (TMZ) is the first-in-line chemotherapeutic; however, resistance is frequent and multifactorial. While many molecular and genetic factors have been linked to TMZ resistance, the role of the solid tumor morphology and the tumor microenvironment, particularly the blood-brain barrier (BBB), is unknown. Here, the authors investigate these using a complex in vitro model for GBM and its surrounding BBB. The model recapitulates important clinical features such as a dense tumor core with tumor cells that invade along the perivascular space; and a perfusable BBB with a physiological permeability and morphology that is altered in the presence of a tumor spheroid. It is demonstrated that TMZ sensitivity decreases with increasing cancer cell spatial organization, and that the BBB can contribute to TMZ resistance. Proteomic analysis with next-generation low volume sample workflows of these cultured microtissues revealed potential clinically relevant proteins involved in tumor aggressiveness and TMZ resistance, demonstrating the utility of complex in vitro models for interrogating the tumor microenvironment and therapy validation.

Unexplained Causes of Glioma-Associated Epilepsies: A Review of Theories and an Area for Research

Approximately 30% of glioma patients are able to survive beyond one year postdiagnosis. And this short time is often overshadowed by glioma-associated epilepsy. This condition severely impairs the patient's quality of life and causes great suffering. The genetic, molecular and cellular mechanisms underlying tumour development and epileptogenesis remain incompletely understood, leading to numerous unanswered questions. The various types of gliomas, namely glioblastoma, astrocytoma and oligodendroglioma, demonstrate distinct seizure susceptibility and disease progression patterns. Patterns have been identified in the presence of IDH mutations and epilepsy, with tumour location in cortical regions, particularly the frontal lobe, showing a more frequent association with seizures. Altered expression of TP53, MGMT and VIM is frequently detected in tumour cells from individuals with epilepsy associated with glioma. However, understanding the pathogenesis of these modifications poses a challenge. Moreover, hypoxic effects induced by glioma and associated with the HIF-1a factor may have a significant impact on epileptogenesis, potentially resulting in epileptiform activity within neuronal networks. We additionally hypothesise about how the tumour may affect the functioning of neuronal ion channels and contribute to disruptions in the blood-brain barrier resulting in spontaneous depolarisations.

Distinct aneuploid evolution of astrocytoma and glioblastoma during recurrence

Astrocytoma and glioblastoma (GB) are reclassified subtypes of adult diffuse gliomas based on distinct isocitrate dehydrogenase (IDH) mutation in the fifth edition of the WHO Classification of Tumors of the Central Nervous System. The recurrence of gliomas is a common and inevitable challenge, and analyzing the distinct genomic alterations in astrocytoma and GB could provide insights into their progression. This study conducted a longitudinal investigation, utilizing whole-exome sequencing, on 65 paired primary/recurrent gliomas. It examined chromosome arm aneuploidies, copy number variations (CNVs) of cancer-related genes and pathway enrichments during the relapse. The veracity of these findings was verified through the integration of our data with multiple public resources and by corroborative immunohistochemistry (IHC). The results revealed a greater prevalence of aneuploidy changes and acquired CNVs in recurrent lower grade astrocytoma than in relapsed grade 4 astrocytoma and GB. Larger aneuploidy changes were predictive of an unfavorable prognosis in lower grade astrocytoma (P < 0.05). Further, patients with acquired gains of 1q, 6p or loss of 13q at recurrence had a shorter overall survival in lower grade astrocytoma (P < 0.05); however, these prognostic effects were confined in grade 4 astrocytoma and GB. Moreover, acquired gains of 12 genes (including VEGFA) on 6p during relapse were associated with unfavorable prognosis for lower grade astrocytoma patients. Notably, elevated VEGFA expression during recurrence corresponded to poorer survival, validated through IHC and CGGA data. To summarize, these findings offer valuable insights into the progression of gliomas and have implications for guiding therapeutic approaches during recurrence.

Comprehensive analysis of the prognostic and immunological signature of eight Tripartitemotif (TRIM) family molecules in human gliomas

BACKGROUND

TRIM family molecules have been identified as being involved in the tumor progression of various cancer types. Increasingly, experimental evidence indicates that some of TRIM family molecules are implicated in glioma tumorigenesis. However, the diverse genomic changes, prognostic values and immunological landscapes of TRIM family of molecules have yet to be fully determined in glioma.

METHODS

In our study, employing the comprehensive bioinformatics tools, we evaluated the unique functions of 8 TRIM members including TRIM5/17/21/22/24/28/34/47 in gliomas.

RESULTS

The expression levels of 7 TRIM members (TRIM5/21/22/24/28/34/47) were higher in glioma as well as its diverse cancer subtypes than in normal tissues, whereas the expression level of TRIM17 was the opposite, lower in the former than in the latter. In addition, survival analysis revealed that the high expression profiles of TRIM5/21/22/24/28/34/47 were associated with poor overall survival (OS), disease-specific survival (DSS) and progress-free interval (PFI) in glioma patients, whereas TRIM17 displayed adverse outcomes. Moreover, the 8 TRIM molecules expression as well as methylation profiles remarkably correlated with different WHO grades. And genetic alterations, including mutations and copy number alterations (CNAs), in the TRIM family were correlated with longer OS, DSS and progress-free survival (PFS) in glioma patients. Furthermore, through Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis results of these 8 molecules and their related genes, we found that these molecules may change the immune infiltration of the tumor microenvironment and regulate the expression of immune checkpoint molecules (ICMs), affecting the occurrence and development of gliomas. The correlation analyses between the 8 TRIM molecules and TMB (tumor mutational burden)/MSI (microsatellite instability)/ICMs discovered that as the expression level of TRIM5/21/22/24/28/34/47 increased, the TMB score also increased significantly, while TRIM17 showed an opposite outcome. Further, a 6-gene signature (TRIM 5/17/21/28/34/47) for predicting overall survival (OS) in gliomas was built by using the least absolute shrinkage and selection operator (LASSO) regression, and the survival and time-dependent ROC analyses all were found to perform well in testing and validation cohorts. Results of multivariate COX regression analysis showed that TRIM5/28 are both expected to become independent risk predictors to guide clinical treatment.

CONCLUSION

In general, the results indicate that TRIM5/17/21/22/24/28/34/47 might exert a crucial influence on gliomas tumorigenesis and might be putative prognostic markers and therapeutic targets for glioma patients.

Development and validation of a predictive model in diagnosis and prognosis of primary glioblastoma patients based on Homeobox A family

BACKGROUND

Homeobox A (HOXA) family is involved in the development of malignancies as either tumor suppressors or oncogenes. However, their roles in glioblastoma (GBM) and clinical significance have not been fully elucidated.

METHODS

HOXA mutation and expressions in pan-cancers were investigated using GSCA and Oncomine, which in GBM were validated by cBioPortal, Chinese Glioma Genome Atlas (CGGA), and The Cancer Genome Atlas (TCGA) datasets. Kaplan-Meier analyses were conducted to determine prognostic values of HOXAs at genetic and mRNA levels. Diagnostic roles of HOXAs in tumor classification were explored by GlioVis and R software. Independent prognostic HOXAs were identified using Cox survival analyses, the least absolute shrinkage and selection operator (LASSO) regression, quantitative real-time PCR, and immunohistochemical staining. A HOXAs-based nomogram survival prediction model was developed and evaluated using Kaplan-Meier analysis, time-dependent Area Under Curve, calibration plots, and Decision Curve Analysis in training and validation cohorts.

RESULTS

HOXAs were highly mutated and overexpressed in pan-cancers, especially in CGGA and TCGA GBM datasets. Genetic alteration and mRNA expression of HOXAs were both found to be prognostic. Specific HOXAs could distinguish IDH mutation (HOXA1-7, HOXA9, HOXA13) and molecular GBM subtypes (HOXA1-2, HOXA9-11, HOXA13). HOXA1/2/3/10 were confirmed to be independent prognostic members, with high expressions validated in clinical GBM tissues. The HOXAs-based nomogram model exhibited good prediction performance and net benefits for patients in training and validation cohorts.

CONCLUSION

HOXA family has diagnostic values, and the HOXAs-based nomogram model is effective in survival prediction, providing a novel approach to support the treatment of GBM patients.

The value of basement membrane-associated genes in the prognosis and immune regulation of glioma

Gliomas have a high incidence rate in central nervous tumors. Although many breakthroughs have been made in the pathogenesis and treatment of glioma, the recurrence and metastasis rates of patients have not been improved based on the uniqueness of glioma. Glioma destroys the surrounding basement membrane (BM), leading to local infiltration, resulting in the corresponding clinical and neurological symptoms. Therefore, exploring the biological roles played by BM associated genes in glioma is particularly necessary for a comprehensive understanding of the biological processes of glioma and its treatment. Differential expression and univariate COX regression analyses were used to identify the basement membrane genes (BMGs) to be included in the model. LASSO regression was used to construct the BMG model. The Kaplan-Meier (KM) survival analysis model was used to assess the prognosis discrimination between training sets, validation sets, and clinical subgroups. Receiver-operating characteristic (ROC) analysis was used to test the prognostic efficacy of the model. Use calibration curves to verify the accuracy of nomograms. Gene ontology (GO), Kyoto encyclopedia of genes and genomes (KEGG), and gene set enrichment analysis (GSEA) were used to analyze the function and pathway enrichment among the model groups. ESTIMATE and other 7 algorithms including CIBERSORT were used to evaluate the immune microenvironment. "pRRophetic" was used to evaluate drug sensitivity. This study demonstrated that high-risk genes (LAMB4, MMP1, MMP7) promote glioma progression and negatively correlate with patient prognosis. In the tumor microenvironment (TME), high-risk genes have increased scores of macrophages, neutrophils, immune checkpoints, chemokines, and chemokine receptors. This study suggests that BMGs, especially high-risk-related genes, are potential sites for glioma therapy, a new prospect for comprehensively understanding the molecular mechanism of glioma.

Comprehensive analysis of oxidative stress-related lncRNA signatures in glioma reveals the discrepancy of prognostic and immune infiltration

Oxidative stress refers to the process of reactive oxide species (ROS) increase in human body due to various factors, which leads to oxidative damage in human tissues. Current studies have confirmed that sustained oxidative stress is one of the distinctive features throughout the development of tumors. Numerous reports have shown that lncRNAs can regulate the process of oxidative stress through multiple pathways. However, the relationship between glioma-associated oxidative stress and lncRNAs is not clearly investigated. RNA sequencing data of GBM (glioblastoma) and LGG (low grade glioma) and corresponding clinical data were retrieved from the TCGA database. Oxidative stress related lncRNAs (ORLs) were identified by Pearson correlation analysis. Prognostic models for 6-ORLs were structured in the training cohort by univariate Cox regression analysis, multivariate Cox regression analysis and LASSO regression analysis. We constructed the nomogram and verified its predictive efficacy by Calibration curves and DCA decision curves. The biological functions and pathways of 6-ORLs-related mRNAs were inferred by Gene Set Enrichment Analysis. Immune cell abundance and immune function associated with risk score (RS) were estimated by ssGSEA, CIBERSORT and MCPcounter synthetically. External validation of the signature was completed using the CGGA-325 and CGGA-693 datasets. 6-ORLs signature-AC083864.2, AC107294.1, AL035446.1, CRNDE, LINC02600, and SNAI3-AS1-were identified through our analysis as being predictive of glioma prognosis. Kaplan-Meier and ROC curves indicated that the signature has a dependable predictive efficacy in the TCGA training cohort, validation cohort and CGGA-325/CGGA-693 test cohort. The 6-ORLs signature were verified to be independent prognostic predictors by multivariate cox regression and stratified survival analysis. Nomogram built with risk scores had strong predictive efficacy for patients' overall survival (OS). The outcomes of the functional enrichment analysis revealing potential molecular regulatory mechanisms for the 6-ORLs. Patients in the high-risk subgroup presented a significant immune microenvironment of macrophage M0 and cancer-associated fibroblast infiltration which was associated with a poorer prognosis. Finally, the expression levels of 6-ORLs in U87/U251/T98/U138 and HA1800 cell lines were verified by RT-qPCR. The nomogram in this study has been made available as a web version for clinicians. This 6-ORLs risk signature has the capabilities to predict the prognosis of glioma patients, assist in evaluating immune infiltration, and assess the efficacy of various anti-tumor systemic therapy regimens.

A novel gene, TARDBP, and the protein it encodes can predict glioma patient prognosis and establish a prediction model

BACKGROUND

TDP-43 (43-kD transactive response DNA-binding protein) is a DNA-/RNA-binding protein that plays an important role in several nervous system diseases, such as amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD). Whether it plays an important role in glioma patients is unknown.

METHODS

Datasets were downloaded from the Chinese Glioma Genome Atlas (CGGA) website ( http://www.cgga.org.cn/ ). Cox survival analysis was performed to determine the relationship between TARDBP gene expression and the overall survival of glioma patients. GO analyses were performed to determine the biological functions of the TARDBP gene. Finally, we used PRS type, age, grade, IDH mutation status, 1p/19q codeletion status, and expression value of the TARDBP gene to construct a prediction model. With this model, we can predict patients' 1-, 2-, 3-, 5-, and 10-year survival rates.

RESULTS

The TARDBP gene plays an important role in glioma patients. The expression of the TARDBP gene has a significant correlation with glioma patient survival. We also constructed an ideal prediction model.

CONCLUSION

Our findings suggest that the TARDBP gene and the protein it encodes play important roles in glioma patients. The expression of the TARDBP gene has a significant correlation with the overall survival of glioma patients.

Establishment and validation of a novel integrin-based prognostic gene signature that sub-classifies gliomas and effectively predicts immunosuppressive microenvironment

The integrin family members play a key role in cancer immunomodulation and prognosis. We comprehensively analyzed the expression patterns and clinical significance of integrin family-related genes in gliomas. A total of 2293 gliomas from the Cancer Genome Atlas (TCGA), Chinese Glioma Genome Atlas (CGGA) and Gliovis platform were enrolled for analyses. Twenty-six integrin coding genes showed different expression patterns between glioma and normal brain tissues. We screened an integrin family-related gene signature (ITGA5, ITGA9, ITGAE, ITGB7 and ITGB8) that showed independent prognostic value and sub-classified gliomas into different prognostic and molecular clusters, further composed an integrin-based risk score model associated with glioma malignant clinical features, overall survival (OS), and immune microenvironment alterations. Besides, glioma patients with high-risk scores showed chemotherapeutic resistance and more immune cells infiltration as well as high immune checkpoints expression. Concurrently, we also revealed that high-risk score group presented resistance to T cell-mediated cancer killing process and lower rates of response to immune checkpoint blockade (ICB) treatment. In conclusion, our study identified a valuable integrin gene signature that predicted gliomas OS effectively, and sub-classified them into different phenotypes and accompanied with immunological changes, possibly acted as a biomarker for ICB treatment.

Identification of Prominent Genes between 3D Glioblastoma Models and Clinical Samples via GEO/TCGA/CGGA Data Analysis

A paradigm shift in preclinical evaluations of new anticancer GBM drugs should occur in favour of 3D cultures. This study leveraged the vast genomic data banks to investigate the suitability of 3D cultures as cell-based models for GBM. We hypothesised that correlating genes that are highly upregulated in 3D GBM models will have an impact in GBM patients, which will support 3D cultures as more reliable preclinical models for GBM. Using clinical samples of brain tissue from healthy individuals and GBM patients from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), Chinese Glioma Genome Atlas (CGGA), and Genotype-Tissue Expression (GTEx) databases, several genes related to pathways such as epithelial-to-mesenchymal transition (EMT)-related genes (CD44, TWIST1, SNAI1, CDH2, FN1, VIM), angiogenesis/migration-related genes (MMP1, MMP2, MMP9, VEGFA), hypoxia-related genes (HIF1A, PLAT), stemness-related genes (SOX2, PROM1, NES, FOS), and genes involved in the Wnt signalling pathway (DKK1, FZD7) were found to be upregulated in brain samples from GBM patients, and the expression of these genes were also enhanced in 3D GBM cells. Additionally, EMT-related genes were upregulated in GBM archetypes (wild-type IDH1^R132 ) that historically have poorer treatment responses, with said genes being significant predictors of poorer survival in the TCGA cohort. These findings reinforced the hypothesis that 3D GBM cultures can be used as reliable models to study increased epithelial-to-mesenchymal transitions in clinical GBM samples.

Brain lesion locations associated with secondary seizure generalization in tumors and strokes

Structural brain lesions are the most common cause of adult-onset epilepsy. The lesion location may contribute to the risk for epileptogenesis, but whether specific lesion locations are associated with a risk for secondary seizure generalization from focal to bilateral tonic-clonic seizures, is unknown. We identified patients with a diagnosis of adult-onset epilepsy caused by an ischemic stroke or a tumor diagnosed at the Turku University Hospital in 2004-2017. Lesion locations were segmented on patient-specific MR imaging and transformed to a common brain atlas (MNI space). Both region-of-interest analyses (intersection with the cortex, hemisphere, and lobes) and voxel-wise analyses were conducted to identify the lesion locations associated with focal to bilateral tonic-clonic compared to focal seizures. We included 170 patients with lesion-induced epilepsy (94 tumors, 76 strokes). Lesions predominantly localized in the cerebral cortex (OR 2.50, 95% C.I. 1.21-5.15, p = .01) and right hemisphere (OR 2.22, 95% C.I. 1.17-4.20, p = .01) were independently associated with focal to bilateral tonic-clonic seizures. At the lobar-level, focal to bilateral tonic-clonic seizures were associated with lesions in the right frontal cortex (OR 4.41, 95% C.I. 1.44-13.5, p = .009). No single voxels were significantly associated with seizure type. These effects were independent of lesion etiology. Our results demonstrate that lesion location is associated with the risk for secondary generalization of epileptic seizures. These findings may contribute to identifying patients at risk for focal to bilateral tonic-clonic seizures.

H3 G34-mutant high-grade gliomas: integrated clinical, imaging and pathological characterisation of a single-centre case series

BACKGROUND

Diffuse hemispheric glioma, H3 G34-mutant, is a novel paediatric tumour type in the fifth edition of the WHO classification of CNS tumours associated with an invariably poor outcome. We present a comprehensive clinical, imaging and pathological review of this entity.

METHODS

Patients with confirmed H3 G34R-mutant high-grade glioma were included in a single-centre retrospective cohort study and examined for clinical, radiological and histo-molecular data.

RESULTS

Twelve patients were enrolled in the study - 7 males/5 females; the mean age was 17.5 years (10-57 years). Most patients presented with signs of raised intracranial pressure (8/12). The frontal lobe (60%) was the prevalent location, with a mixed cystic-nodular appearance (10/12) and presence of vascular flow voids coursing through/being encased by the mass (8/12), and all tumours showed cortical invasion. Nine patients had subtotal resection limited by functional margins, two patients underwent supra-total resection, and one patient had biopsy only. 5-ALA was administered to 6 patients, all of whom showed positive fluorescence. Histologically, the tumours showed a marked heterogeneity and aggressive spread along pre-existing brain structures and leptomeninges. In addition to the diagnostic H3 G34R/V mutation, pathogenic variants in TP53 and ATRX genes were found in most cases. Potential targetable mutations in PDGFRA and PIK3CA genes were detected in five cases. The MGMT promoter was highly methylated in half of the samples. Methylation profiling was a useful diagnostic tool and highlighted recurrent structural chromosome abnormalities, such as PDGFRA amplification, CDKN2A/B deletion, PTEN loss and various copy number changes in the cyclin D-CDK4/Rb pathway. Radiochemotherapy was the most common adjuvant treatment (9/12), and the average survival was 19.3 months.

CONCLUSIONS

H3 G34R-mutant hemispheric glioma is a distinct entity with characteristic imaging and pathological features. Genomic landscaping of individual tumours can offer an opportunity to adapt individual therapies and improve patient management.

SLC12A5 as a novel potential biomarker of glioblastoma multiforme

BACKGROUND

Glioblastoma multiforme (GBM) is the most prevalent and malignant intracranial tumor with significant features of dismal prognosis and limited therapeutic solutions. Consequently, the present studies are committed to exploring potential biomarkers through bioinformatics analysis, which may serve as valuable prognostic predictors or novel therapeutic targets and provide new insights into the pathogenesis of GBM.

METHODS

We filtered overlapping differentially expressed genes (DEGs) based on expression profilings from three GBM microarray datasets (GSE116520, GSE4290 and GSE68848) and combined RNA sequencing data from The Cancer Genome Atlas and the Genotype-Tissue Expression databases. Hub genes were prioritized from DEGs after performing protein-protein interaction (PPI) network analysis and weighted gene co-expression network analysis (WGCNA). This was followed by survival analysis to identify potential biomarkers among hub genes. Ultimately, the distributions of gene expressions, genetic alterations, upstream regulatory mechanisms and enrichments of gene functions of the identified biomarkers were analysed on public databases. QRT-PCR, immunohistochemical staining and western blotting was also used to confirm the gene expression patterns in GBM and normal brain tissues. CCK-8 assay clarified the effects of the genes on GBM cells.

RESULTS

A total of 322 common DEGs were determined and nine genes were subsequently considered as hub genes by the combination of PPI network analysis and WGCNA. Only SLC12A5 had prognostic significance, which was deficient in GBM whereas especially enriched in normal neural tissues. SLC12A5 overexpression would inhibit cell proliferation of U251MG. Genetic alterations of SLC12A5 were rarely seen in GBM patients, and there was no apparent association existed between SLC12A5 expression and DNA methylation. SLC12A5 was prominently involved in ion transport, synapse and neurotransmitter.

CONCLUSION

SLC12A5 shows promise to function as a novel effective biomarker for GBM and deserves further systematic research.

Epilepsy-related white matter network changes in patients with frontal lobe glioma

PURPOSE

Epilepsy is a common symptom in patients with frontal lobe glioma. Tumor-related epilepsy was recently considered a type of network disease. Glioma can severely influence the integrity of the white matter network. The association between white matter network changes and presurgical epilepsy remains unclear in glioma patients. This study aims to identify alterations to the subcortical brain networks caused by glioma and glioma-related epilepsy.

METHODS

Sixty-one patients with frontal lobe gliomas were enrolled and stratified into the epileptic and non-epileptic groups. Additionally, 14 healthy participants were enrolled after matching for age, sex, and education level. All participants underwent diffusion tensor imaging. Graph theoretical analysis was applied to reveal topological changes in their white matter networks. Regions affected by tumors were excluded from the analysis.

RESULTS

Global efficiency was significantly decreased (p = 0.008), while the shortest path length increased (p = 0.02) in the left and right non-epileptic groups compared to the controls. A total of five edges exhibited decreased fiber count in the non-epileptic group (p < 0.05, false discovery rate-corrected). The topological properties and connectional edges showed no significant differences when comparing the epileptic groups and the controls. Additionally, the degree centrality of several nodes connected to the alternated edges was also diminished.

CONCLUSIONS

Compared to the controls, the epilepsy groups showed raletively intact WM networks, while the non-epileptsy groups had damaged network with lower efficiency and longer path length. These findings indicated that the occurrence of glioma related epilepsy have association with white matter network intergrity.

Identification and validation of a novel prognostic signature based on mitochondria and oxidative stress related genes for glioblastoma

BACKGROUND

Mitochondria represent a major source of reactive oxygen species (ROS) in cells, and the direct increase in ROS content is the primary cause of oxidative stress, which plays an important role in tumor proliferation, invasion, angiogenesis, and treatment. However, the relationship between mitochondrial oxidative stress-related genes and glioblastoma (GBM) remains unclear. This study aimed to investigate the value of mitochondria and oxidative stress-related genes in the prognosis and therapeutic targets of GBM.

METHODS

We retrieved mitochondria and oxidative stress-related genes from several public databases. The LASSO regression and Cox analyses were utilized to build a risk model and the ROC curve was used to assess its performance. Then, we analyzed the correlation between the model and immunity and mutation. Furthermore, CCK8 and EdU assays were utilized to verify the proliferative capacity of GBM cells and flow cytometry was used to analyze apoptosis rates. Finally, the JC-1 assay and ATP levels were utilized to detect mitochondrial function, and the intracellular ROS levels were determined using MitoSOX and BODIPY 581/591 C11.

RESULTS

5 mitochondrial oxidative stress-related genes (CTSL, TXNRD2, NUDT1, STOX1, CYP2E1) were screened by differential expression analysis and Cox analysis and incorporated in a risk model which yielded a strong prediction accuracy (AUC value = 0.967). Furthermore, this model was strongly related to immune cell infiltration and mutation status and could identify potential targeted therapeutic drugs for GBM. Finally, we selected NUDT1 for further validation in vitro. The results showed that NUDT1 was elevated in GBM, and knockdown of NUDT1 inhibited the proliferation and induced apoptosis of GBM cells, while knockdown of NUDT1 damaged mitochondrial homeostasis and induced oxidative stress in GBM cells.

CONCLUSION

Our study was the first to propose a prognostic model of mitochondria and oxidative stress-related genes, which provided potential therapeutic strategies for GBM patients.

Differential regulation of H3K9/H3K14 acetylation by small molecules drives neuron-fate-induction of glioma cell

Differentiation therapy using small molecules is a promising strategy for improving the prognosis of glioblastoma (GBM). Histone acetylation plays an important role in cell fate determination. Nevertheless, whether histone acetylation in specific sites determines GBM cells fate remains to be explored. Through screening from a 349 small molecule-library, we identified that histone deacetylase inhibitor (HDACi) MS-275 synergized with 8-CPT-cAMP was able to transdifferentiate U87MG GBM cells into neuron-like cells, which were characterized by cell cycle arrest, rich neuron biomarkers, and typical neuron electrophysiology. Intriguingly, acetylation tags of histone 3 at lysine 9 (H3K9ac) were decreased in the promoter of multiple oncogenes and cell cycle genes, while ones of H3K9ac and histone 3 at lysine 14 (H3K14ac) were increased in the promoter of neuron-specific genes. We then compiled a list of genes controlled by H3K9ac and H3K14ac, and proved that it is a good predictive power for pathologic grading and survival prediction. Moreover, cAMP agonist combined with HDACi also induced glioma stem cells (GSCs) to differentiate into neuron-like cells through the regulation of H3K9ac/K14ac, indicating that combined induction has the potential for recurrence-preventive application. Furthermore, the combination of cAMP activator plus HDACi significantly repressed the tumor growth in a subcutaneous GSC-derived tumor model, and temozolomide cooperated with the differentiation-inducing combination to prolong the survival in an orthotopic GSC-derived tumor model. These findings highlight epigenetic reprogramming through H3K9ac and H3K14ac as a novel approach for driving neuron-fate-induction of GBM cells.

Unruptured brain arteriovenous malformations causing seizures localize to one common brain network

Seizures are a frequent symptom of unruptured brain arteriovenous malformations (bAVMs). However, the brain regions responsible for these seizures remain unclear. To identify the brain regions causally involved in bAVM-related seizures, we retrospectively reviewed 220 patients with unruptured bAVMs. Using voxel-based lesion-symptom mapping (VLSM) analyses, we tested whether individual brain regions were associated with unruptured bAVM-related seizures. The result revealed that unruptured bAVMs causing seizures are anatomically heterogeneous at the voxel level. Subsequently, lesion network mapping (LNM) analyses was performed to determine whether bAVMs causing seizures belonged to a distributed brain network. LNM analyses indicated that these lesions were located in a functional network characterized by connectivity to the left caudate and precuneus. Moreover, the discrimination performance of the identified seizure network was evaluated in discovery set by calculating the individualized network damage score and was tested in validation set. Based on the calculated network damage scores, patients were divided into low-, medium-, and high-risk groups. The prevalence of seizures significantly differed among the three risk categories in both discovery (p = .003) and validation set (p = .004). Finally, we calculated the percentage of voxels in the canonical resting-state networks that overlapped with the seizure-susceptible brain regions to investigate the involvement of resting-state networks. With an involvement percentage over 50%, the frontoparietal control (82.9%), limbic function (76.7%), and default mode network (69.3%) were considered to be impacted in bAVM-related seizures. Our study identified the seizure-susceptible brain regions for unruptured bAVMs, which could be a plausible neuroimaging biomarker in predicting possible seizures.

Integrated Bioinformatic Analysis of the Correlation of HOXA10 Expression with Survival and Immune Cell Infiltration in Lower Grade Glioma

Homeobox A10 (HOXA10) encodes a transcription factor that regulates developmental processes. Whether HOXA10 mRNA levels in lower grade glioma (LGG) correlate with survival and immune cell infiltration has not been evaluated. The differential expression of HOXA10 in different tumors and their corresponding normal tissues was evaluated by exploring public datasets. The correlations between HOXA10 and survival, tumor immune cell infiltration, diverse gene mutation characteristics, and tumor mutation burden in LGG were also investigated using several independent datasets. Pathway enrichment analysis was conducted to identify HOXA10-associated signaling pathways. We found that HOXA10 expression levels did not significantly differ between LGG tumors and normal tissues. Upon assessing the association between HOXA10 expression and immune cell infiltration in LGG, as expected, HOXA10 gene mRNA levels were positively associated with B-cell and dendritic cell infiltration levels in public online datasets. Different HOXA10 expression groups showed diverse gene mutation characteristics and TMB, and low HOXA10 expression was closely related to improved LGG patient survival. Pathway enrichment analysis of HOXA10-associated genes indicated that the cell cycle signaling pathway may participate in affecting the outcomes of LGG patients. Our findings showed that HOXA10 expression was associated with LGG prognosis and tumor immunity.

A Novel Prognostic Marker and Therapeutic Target Associated with Glioma Progression in a Tumor Immune Microenvironment

Background

Immune microenvironment serves a vital role in glioma progression, and a large number of studies have found that tumor progression can be reduced to some extent by modulating the immune process in tumors.

Materials and Methods

ImmuneScore of each sample in CGGA datasets were calculated with Estimate R package, and samples were grouped by median ImmuneScore values for differential analysis to obtain immune microenvironment differential genes. We further conducted survival analysis, ROC curve analysis, independent prognostic analysis, and clinical correlation analysis on glioma sample genes in CGGA to obtain glioma prognostic genes, and then identified their intersection with immune microenvironment DEGs by Venn tool. The GEPIA and UALCAN databases were used to verify the differential expression of intersecting genes in the glioma and normal brain and to identify our target gene. After validation of their prognostic value, we constructed a nomogram to calculate the risk score and to estimate the accuracy of prognostic model. We mined co-expression genes, enriched functions and pathways, and correlations to immune cell infiltration of unigene with an online database. Finally, we verified the differential expression of FCGBP in glioma by immunohistochemical staining.

Results

We finally selected Fc fragment of IgG-binding protein (FCGBP) as our study gene. The prognostic values of FCGBP were validated by a series of analyses. Immunohistochemical staining showed that FCGBP expression increased in gliomas and was up-regulated with the progression of glioma grade.

Conclusion

As a key unigene in glioma progression, FCGBP contributes to the regulation of immune microenvironment and has the potential to be a prognostic biomarker and immune targets.

Construction of a machine learning-based artificial neural network for discriminating PANoptosis related subgroups to predict prognosis in low-grade gliomas

The poor prognosis of gliomas necessitates the search for biomarkers for predicting clinical outcomes. Recent studies have shown that PANoptosis play an important role in tumor progression. However, the role of PANoptosis in in gliomas has not been fully clarified.Low-grade gliomas (LGGs) from TCGA and CGGA database were classified into two PANoptosis patterns based on the expression of PANoptosis related genes (PRGs) using consensus clustering method, followed which the differentially expressed genes (DEGs) between two PANoptosis patterns were defined as PANoptosis related gene signature. Subsequently, LGGs were separated into two PANoptosis related gene clusters with distinct prognosis based on PANoptosis related gene signature. Univariate and multivariate cox regression analysis confirmed the prognostic values of PANoptosis related gene cluster, based on which a nomogram model was constructed to predict the prognosis in LGGs. ESTIMATE algorithm, MCP counter and CIBERSORT algorithm were utilized to explore the distinct characteristics of tumor microenvironment (TME) between two PANoptosis related gene clusters. Furthermore, an artificial neural network (ANN) model based on machine learning methods was developed to discriminate distinct PANoptosis related gene clusters. Two external datasets were used to verify the performance of the ANN model. The Human Protein Atlas website and western blotting were utilized to confirm the expression of the featured genes involved the ANN model. We developed a machine learning based ANN model for discriminating PANoptosis related subgroups with drawing implications in predicting prognosis in gliomas.

A prognostic estimation model based on mRNA-sequence data for patients with oligodendroglioma

Background

The diagnosis of oligodendroglioma based on the latest World Health Organization Classification of Tumors of the Central Nervous System (WHO CNS 5) criteria requires the codeletion of chromosome arms 1p and 19q and isocitrate dehydrogenase gene (IDH) mutation (mut). Previously identified prognostic indicators may not be completely suitable for patients with oligodendroglioma based on the new diagnostic criteria. To find potential prognostic indicators for oligodendroglioma, we analyzed the expression of mRNAs of oligodendrogliomas in Chinese Glioma Genome Atlas (CGGA).

Methods

We collected 165 CGGA oligodendroglioma mRNA-sequence datasets and divided them into two cohorts. Patients in the two cohorts were further classified into long-survival and short-survival subgroups. The most predictive mRNAs were filtered out of differentially expressed mRNAs (DE mRNAs) between long-survival and short-survival patients in the training cohort by least absolute shrinkage and selection operator (LASSO), and risk scores of patients were calculated. Univariate and multivariate analyses were performed to screen factors associated with survival and establish the prognostic model. qRT-PCR was used to validate the expression differences of mRNAs.

Results

A total of 88 DE mRNAs were identified between the long-survival and the short-survival groups in the training cohort. Seven RNAs were selected to calculate risk scores. Univariate analysis showed that risk level, age, and primary-or-recurrent status (PRS) type were statistically correlated with survival and were used as factors to establish a prognostic model for patients with oligodendroglioma. The model showed an optimal predictive accuracy with a C-index of 0.912 (95% CI, 0.679-0.981) and harbored a good agreement between the predictions and observations in both training and validation cohorts.

Conclusion

We established a prognostic model based on mRNA-sequence data for patients with oligodendroglioma. The predictive ability of this model was validated in a validation cohort, which demonstrated optimal accuracy. The 7 mRNAs included in the model would help predict the prognosis of patients and guide personalized treatment.

Transcription factor CASZ1 increases an oncogenic transcriptional process in tumorigenesis and progression of glioma cells

As a transcription factor, the role of CASZ1 in different entities is inconsistent. Glioma is one of the leading causes of cancer death worldwide. Its prognostic relevance and biological functions in glioma remain obscure. We focused on the role, mechanism, and prognostic value of CASZ1 in glioma cells. Herein, CASZ1 was identified as a novel potential oncogene in glioma tissues from GEO and TCGA datasets. CASZ1 was highly expressed in glioma tissues, predicting poor prognosis in glioma patients. Knockdown of CASZ1 inhibited proliferation and invasion in vitro, whereas upregulation of CASZ1 presented opposite results. Overexpression of CASZ1 increased transcriptional process of target gene p75NTR. CASZ1 was the potential transcriptional regulators for p75NTR. In addition, the p75NTR expression is essential for CASZ1 to exert its function as an oncogene. Our findings indicate that highly expressed CASZ1 in glioma cells acts as a pro-oncogene factor in gliomas via regulating transcriptional process of target gene p75NTR, which was identified as an unfavorable prognostic marker in patients with gliomas. CASZ1 is expected to become a novel target for the treatment of gliomas.

LACTB suppresses migration and invasion of glioblastoma via downregulating RHOC/Cofilin signaling pathway

Glioblastoma (GBM) is the most malignant tumor in human brain. High invasiveness of this tumor is the main reason causing treatment failure and recurrence. Previous study has found that LACTB is a novel tumor suppressor in breast cancer. Moreover, the function of LACTB in other tumors and mechanisms involving LACTB were also reported. However, the role and relevant mechanisms of LACTB in GBM invasion remains to be revealed. Our aim is to investigate the role LACTB in GBM migration and invasion. We found that LACTB was downregulated in gliomas compared to normal brain tissues. Overexpression of LACTB suppressed migration and invasion of LN229 and U87 cell lines. Mechanistically, LACTB overexpression downregulated the mesenchymal markers. Moreover, LACTB overexpression downregulated the expression of RHOC and inhibited RHOC/Cofilin signaling pathway. The study suggests that LACTB suppresses migration and invasion of GBM cell lines via downregulating RHOC/Cofilin signaling pathway. These findings suggest that LACTB may be a potential treatment target of GBM.

Structuring and validating a prognostic model for low-grade gliomas based on the genes for plasma membrane tension

Background

Recent studies indicate that cell mechanics are associated with malignancy through its impact on cell migration and adhesion. Gliomas are the most common primary malignant brain tumors. Low-grade gliomas (LGGs) include diffuse LGGs (WHO grade II) and intermediate-grade gliomas (WHO grade III). Few studies have focused on membrane tension in LGGs. Herein, we assessed the prognostic value of plasma membrane tension-related genes (MTRGs) in LGGs.

Methods

We selected plasma MTRGs identified in previous studies for analysis. Based on LGG RNA sequencing (RNA-seq) data in The Cancer Genome Atlas, a prognostic signature containing four genes was constructed via log-rank testing, LASSO regression and stepwise multivariate Cox regression and was validated with other datasets. Additionally, functional annotation, pathway enrichment and immune and molecular characteristics of the prognostic model defined subgroups were analyzed. Thereafter, a predictive nomogram that integrated baseline characteristics was constructed to determine the 3, 5, and 10-year overall survival (OS) of patients with LGG. Differentially expressed genes were confirmed via quantitative reverse-transcription polymerase chain reaction (qRT-PCR) and immunohistochemistry (IHC).

Results

Our MTRG prognostic signature was based on ARFIP2, PICK1, SH3GL2, and SRGAP3 expression levels. The high-risk group was more positively associated with apoptosis and cell adhesion pathways and exhibited a low IDH1 mutation rate, high TP53 mutation rate and a low 1p19q co-deletion rate. The high-risk group also exhibited incremental infiltration of immune cells, more forceful immune activities and high expression of immune checkpoints as well as benefited less from immune therapy compared with the low-risk group. Our prognostic model had better forecasting ability than other scoring systems. We found that the nomogram was a better tool for predicting outcomes for patients with LGG. Finally, qRT-PCR confirmed that SH3GL2 and SRGAP3 expression levels in glioma tissues were significantly lower than those in normal brain tissues. The results of IHC analysis confirmed that SH3GL2 protein expression was higher in patients with longer survival.

Conclusion

Our plasma membrane tension-related gene prognostic signature is a prospective tool that can differentiate between prognosis, gene mutation landscape, immune microenvironment, immune infiltration and immunotherapeutic efficacy in LGG.

Integrin subunit alpha V is a potent prognostic biomarker associated with immune infiltration in lower-grade glioma

As a member of integrin receptor family, ITGAV (integrin subunit α V) is involved in a variety of cell biological processes and overexpressed in various cancers, which may be a potential prognostic factor. However, its prognostic value and potential function in lower-grade glioma (LGG) are still unclear, and in terms of immune infiltration, it has not been fully elucidated. Here, the expression preference, prognostic value, and clinical traits of ITGAV were investigated using The Cancer Genome Atlas database (n = 528) and the Chinese Glioma Genome Atlas dataset (n = 458). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses and gene set enrichment analysis (GSEA) were used to explore the biological function of ITGAV. Using R package "ssGSEA" analysis, it was found thatthe ITGAV mRNA expression level showed intense correlation with tumor immunity, such as tumor-infiltrating immune cells and multiple immune-related genes. In addition, ITGAV is associated with some immune checkpoints and immune checkpoint blockade (ICB) and response to chemotherapy. and the expression of ITGAV protein in LGG patients was verified via immunohistochemistry (IHC). ITGAV expression was higher in LGG tissues than in normal tissues (P < 0.001) and multifactor analysis showed that ITGAV mRNA expression was an independent prognostic factor for LGG overall survival (OS; hazard ratio = 2.113, 95% confidence interval = 1.393-3.204, P < 0.001). GSEA showed that ITGAV expression was correlated with Inflammatory response, complement response, KRAS signal, and interferon response. ssGSEA results showed a positive correlation between ITGAV expression and Th2 cell infiltration level. ITGAV mRNA was overexpressed in LGG, and high ITGAV mRNA levels were found to be associated with poor protein expression and poor OS. ITGAV is therefore a potential biomarker for the diagnosis and prognosis of LGG and may be a potential immunotherapy target.

MELK is a prognostic biomarker and correlated with immune infiltration in glioma

Objective

Glioma accounts for the vast majority of primary brain tumors with inevitable recurrence and poor prognosis. Maternal embryonic leucine zipper kinase (MELK) is overexpressed in multiple human tumors and could activate a variety of oncogenic-associated signal pathways. However, its role in the glioma microenvironment is still largely unknown.

Methods

We collected the RNA sequence data and clinical information of gliomas from the Chinese Glioma Genome Atlas (CGGA), The Cancer Genome Atlas (TCGA), and the Gene Expression Omnibus (GEO) databases, and investigated MELK expression and its correlation with clinicopathologic features and prognosis in glioma. Moreover, the relationship between MELK expression and immune cell infiltration in the tumor microenvironment of gliomas was explored through single-sample gene set enrichment analysis (ssGSEA) and CIBERSORT. In addition, gene set enrichment analysis (GSEA) and Metascape online analysis were performed to find out signaling pathways enriched by differentially expressed genes (DEGs) between high- and low-MELK expression groups. Finally, immunohistochemistry was performed to validate our findings.

Results

Data analysis of CGGA and GEO datasets showed that MELK was significantly upregulated in gliomas than in normal brain tissues, and MELK expression was obviously correlated with clinicopathologic features, including age, WHO grade, histological subtype, IDH mutant status, 1p19q codeletion status, and PRS type. Stratified analysis, Cox regression analysis, and nomogram model revealed that high expression of MELK predicted poor survival; hence, MELK could serve as an independent prognostic biomarker for glioma. Moreover, results from enrichment pathway analysis indicated that the immune system process, angiogenesis, apoptosis, cell cycle, and other oncogenic-related signal pathways were significantly enriched between high- and low-MELK expression groups. Immune infiltration analysis demonstrated that increased MELK expression was significantly correlated with higher immune scores, higher fractions of immunocytes (T cells, NK cells resting, macrophages, resting mast cells, and neutrophils), and higher expression levels of immune checkpoints (B7-H3, CTLA4, LAG3, PD-1, PD-L1, and TIM3). Finally, immunohistochemistry analysis validated our findings that high expression of MELK relates to increased malignancy and poor prognosis of glioma.

Conclusion

Our findings identified that MELK could act as an independent prognostic indicator and potential immunotherapy target for glioma. In conclusion, these findings suggested that DDOST mediated the immunosuppressive microenvironment of gliomas and could be an important biomarker in diagnosing and treating gliomas.

Basement-Membrane-Related Gene Signature Predicts Prognosis in WHO Grade II/III Gliomas

Gliomas that are classified as grade II or grade III lesions by the World Health Organization (WHO) are highly aggressive, and some may develop into glioblastomas within a short period, thus portending the conferral of a poor prognosis for patients. Previous studies have implicated basement membrane (BM)-related genes in glioma development. In this study, we constructed a prognostic model for WHO grade II/III gliomas in accordance with the risk scores of BM-related genes. Differentially expressed genes (DEGs) in the glioma samples relative to normal samples were screened from the GEO database, and five prognostically relevant BM-related genes, including NELL2, UNC5A, TNC, CSPG4, and SMOC1, were selected using Cox regression analyses for the risk score model. The median risk score was calculated, based on which high- and low-risk groups of patients were generated. The clinical information, pathological information, and risk group were combined to establish a prognostic nomogram. Both the nomogram and risk score model performed well in the independent CGGA cohort. Gene set enrichment analysis (GSEA) and immune profile, drug sensitivity, and tumor mutation burden (TMB) analyses were performed in the two risk groups. A significant enrichment of 'Autophagy-other', 'Collecting duct acid secretion', 'Glycosphingolipid biosynthesis-lacto and neolacto series', 'Valine, leucine, and isoleucine degradation', 'Vibrio cholerae infection', and other pathways were observed for patients with high risk. In addition, higher proportions of monocytes and resting CD4 memory T cells were observed in the low- and high-risk groups, respectively. In conclusion, the BM-related gene risk score model can guide the clinical management of WHO grade II and III gliomas.

Metabolic-related gene pairs signature analysis identifies ABCA1 expression levels on tumor-associated macrophages as a prognostic biomarker in primary IDHWT glioblastoma

Background

Although isocitrate dehydrogenase (IDH) mutation serves as a prognostic signature for routine clinical management of glioma, nearly 90% of glioblastomas (GBM) patients have a wild-type IDH genotype (IDH^WT) and lack reliable signatures to identify distinct entities.

Methods

To develop a robust prognostic signature for IDH^WT GBM patients, we retrospectively analyzed 4 public datasets of 377 primary frozen tumor tissue transcriptome profiling and clinical follow-up data. Samples were divided into a training dataset (204 samples) and a validation (173 samples) dataset. A prognostic signature consisting of 21 metabolism-related gene pairs (MRGPs) was developed based on the relative ranking of single-sample gene expression levels. GSEA and immune subtype analyses were performed to reveal differences in biological processes between MRGP risk groups. The single-cell RNA-seq dataset was used to examine the expression distribution of each MRG constituting the signature in tumor tissue subsets. Finally, the association of MRGs with tumor progression was biologically validated in orthotopic GBM models.

Results

The metabolic signature remained an independent prognostic factor (hazard ratio, 5.71 [3.542-9.218], P < 0.001) for stratifying patients into high- and low-risk levels in terms of overall survival across subgroups with MGMTp methylation statuses, expression subtypes, and chemo/ratio therapies. Immune-related biological processes were significantly different between MRGP risk groups. Compared with the low-risk group, the high-risk group was significantly enriched in humoral immune responses and phagocytosis processes, and had more monocyte infiltration and less activated DC, NK, and γδ T cell infiltration. scRNA-seq dataset analysis identified that the expression levels of 5 MRGs (ABCA1, HMOX1, MTHFD2, PIM1, and PTPRE) in TAMs increased with metabolic risk. With tumor progression, the expression level of ABCA1 in TAMs was positively correlated with the population of TAMs in tumor tissue. Downregulation of ABCA1 levels can promote TAM polarization towards an inflammatory phenotype and control tumor growth.

Conclusions

The metabolic signature is expected to be used in the individualized management of primary IDH^WT GBM patients.

GBP2 as a potential prognostic predictor with immune-related characteristics in glioma

Guanylate binding protein 2 (GBP2) is a member of the guanine binding protein family, and its relationship with prognostic outcomes and tumor immune microenvironments in glioma remains elusive. We found GBP2 were increased in glioma tissues at both mRNA and protein levels. Kaplan-Meier curves revealed that high GBP2 expression was linked with worse survival of glioma patients, and multivariate Cox regression analysis indicated that high GBP2 expression was an independent prognostic factor for glioma. Combined analysis in immune database revealed that the expression of GBP2 was significantly related to the level of immune infiltration and immunomodulators. Single-cell analysis illustrated the high expression of GBP2 in malignant glioma cells showed the high antigen presentation capability, which were confirmed by real-time polymerase chain reaction (qRT-PCR) data. Additionally, the hsa-mir-26b-5p and hsa-mir-335-5p were predicted as GBP2 regulators and were validated in U87 and U251 cells. Our results first decipher immune-related characteristics and noncoding regulators of GBP2 in glioma, which may provide insights into associated immunotherapies and prognostic predictor.