Repository For Molecular Brain Neoplasia Data Research Articles

An MRI Radiomics Approach to Predict the Hypercoagulable Status of Gliomas.

Venous thromboembolic events are frequent complications of Glioblastoma Multiforme (GBM) and low-grade gliomas (LGGs). The overexpression of tissue factor (TF) plays an essential role in the local hypercoagulable phenotype that underlies these complications. Our aim was to build an MRI radiomics model for the non-invasive exploration of the hypercoagulable status of LGG/GBM. Radiogenomics data from The Cancer Genome Atlas (TCGA) and REMBRANDT (Repository for molecular BRAin Neoplasia DaTa) cohorts were used. A logistic regression model (Radscore) was built in order to identify the top 20% TF-expressing tumors, considered to be at high thromboembolic risk. The most contributive MRI radiomics features from LGG/GBM linked to high TF were identified in TCGA using Least Absolute Shrinkage and Selection Operator (LASSO) regression. A logistic regression model was built, whose performance was analyzed with ROC in the TCGA/training and REMBRANDT/validation cohorts: AUC = 0.87 [CI95: 0.81-0.94, p < 0.0001] and AUC = 0.78 [CI95: 0.56-1.00, p = 0.02], respectively. In agreement with the key role of the coagulation cascade in gliomas, LGG patients with a high Radscore had lower overall and disease-free survival. The Radscore was linked to the presence of specific genomic alterations, the composition of the tumor coagulome and the tumor immune infiltrate. Our findings suggest that a non-invasive assessment of the hypercoagulable status of LGG/GBM is possible with MRI radiomics.

Fc gamma receptor IIb in tumor-associated macrophages and dendritic cells drives poor prognosis of recurrent glioblastoma through immune-associated signaling pathways.

Background: Among central nervous system tumors, glioblastoma (GBM) is considered to be the most destructive malignancy. Recurrence is one of the most fatal aspects of GBM. However, the driver molecules that trigger GBM recurrence are currently unclear. Methods: The mRNA expression data and clinical information of GBM and normal tissues were collected from the Chinese Glioma Genome Atlas The Cancer Genome Atlas (TCGA), and REpository for Molecular BRAin Neoplasia DaTa (REMBRANDT) cohorts. The DESeq2 R package was used to identify the differentially expressed genes between primary and recurrent GBM. ClueGO, Kyoto Encyclopedia of Genes and Genomes (KEGG), Biological Process in Gene ontology (GO-BP), and the Protein ANalysis THrough Evolutionary Relationships (PANTHER) pathway analyses were performed to explore the enriched signaling pathways in upregulated DEGs in recurrent GBM. A gene list that contained potential oncogenes that showed a significant negative correlation with patient survival from The Cancer Genome Atlas was used to further screen driver candidates for recurrent GBM. Univariate Cox proportional hazards regression analyses were used to investigate the risk score for the mRNA expression of the candidates. Single-cell RNA sequencing (scRNA-Seq) analyses were used to determine the cell type-specific distribution of Fc gamma receptor II b (FcγRIIb) in GBM. Immunohistochemistry (IHC) was used to confirm the FcγRIIb-positive cell populations in primary and paired recurrent GBM. Results: Through DEG analysis and overlap analysis, a total of 10 genes that are upregulated in recurrent GBM were screened. Using validation databases, FcγRIIb was identified from the 10 candidates that may serve as a driver for recurrent GBM. FCGR2B expression, not mutation, further showed a highly negative correlation with the poor prognosis of patients with recurrent GBM. Furthermore, scRNA-Seq analyses revealed that tumor-associated macrophage- and dendritic cell-specific FCGR2B was expressed. Moreover, FcγRIIb also showed a strong positive correlation coefficient with major immune-associated signaling pathways. In clinical specimens, FcγRIIb-positive cell populations were higher in recurrent GBM than in primary GBM. Conclusion: This study provides novel insights into the role of FcγRIIb in recurrent GBM and a promising strategy for treatment as an immune therapeutic target.

Establishment and validation of a novel prognostic model for lower-grade glioma based on senescence-related genes.

Increasing studies have indicated that senescence was associated with tumorigenesis and progression. Lower-grade glioma (LGG) presented a less invasive nature, however, its treatment efficacy and prognosis prediction remained challenging due to the intrinsic heterogeneity. Therefore, we established a senescence-related signature and investigated its prognostic role in LGGs. The gene expression data and clinicopathologic features were from The Cancer Genome Atlas (TCGA) database. The experimentally validated senescence genes (SnGs) from the CellAge database were obtained. Then LASSO regression has been performed to build a prognostic model. Cox regression and Kaplan-Meier survival curves were performed to investigate the prognostic value of the SnG-risk score. A nomogram model has been constructed for outcome prediction. Immunological analyses were further performed. Data from the Chinese Glioma Genome Atlas (CGGA), Repository of Molecular Brain Neoplasia Data (REMBRANDT), and GSE16011 were used for validation. The 6-SnG signature has been established. The results showed SnG-risk score could be considered as an independent predictor for LGG patients (HR=2.763, 95%CI=1.660-4.599, P&lt;0.001). The high SnG-risk score indicated a worse outcome in LGG (P&lt;0.001). Immune analysis showed a positive correlation between the SnG-risk score and immune infiltration level, and the expression of immune checkpoints. The CGGA datasets confirmed the prognostic role of the SnG-risk score. And Kaplan-Meier analyses in theadditional datasets (CGGA, REMBRANDT, and GSE16011) validated the prognostic role of the SnG-signature (P&lt;0.001 for all). The SnG-related prognostic model could predict the survival of LGG accurately. This study proposed a novel indicator for predicting the prognosis of LGG and provided potential therapeutic targets.

Convolutional Neural Network featuring VGG-16 Model for Glioma Classification

Magnetic Resonance Imaging (MRI) is a body sensing technique that can produce detailed images of the condition of organs and tissues. Specifically related to brain tumors, the resulting images can be analyzed using image detection techniques so that tumor stages can be classified automatically. Detection of brain tumors requires a high level of accuracy because it is related to the effectiveness of medical actions and patient safety. So far, the Convolutional Neural Network (CNN) or its combination with GA has given good results. For this reason, in this study, we used a similar method but with a variant of the VGG-16 architecture. VGG-16 variant adds 16 layers by modifying the dropout layer (using softmax activation) to reduce overfitting and avoid using a lot of hyper-parameters. We also experimented with using augmentation techniques to anticipate data limitations. Experiment using data The Cancer Imaging Archive (TCIA) - The Repository of Molecular Brain Neoplasia Data (REMBRANDT) contains MRI images of 130 patients with different ailments, grades, races, and ages with 520 images. The tumor type was Glioma, and the images were divided into grades II, III, and IV, with the composition of 226, 101, and 193 images, respectively. The data is divided by 68% and 32% for training and testing purposes. We found that VGG-16 was more effective for brain tumor image classification, with an accuracy of up to 100%.Â

Tripartite motif-containing 25 facilitates immunosuppression and inhibits apoptosis of glioma via activating NF-κB.

As a crucial tumor type of the central nervous system, gliomas are characterized by a dismal prognosis. Tripartite motif-containing 25 (TRIM25), an essential E3 ubiquitin ligase, participates in various biological processes. This study sought to demonstrate its functional role in gliomas. Data obtained from publicly available databases - including The Cancer Genome Atlas (TCGA), the Chinese Glioma Genome Atlas (CGGA), and the Repository for Molecular Brain Neoplasia Data (REMBRANDT) - were employed. TRIM25 expression pattern and its association with different clinical characteristics were analyzed. Kaplan-Meier analysis was utilized to compare different TRIM25 expressions with glioma patients' survival. Subsequently, we performed bioinformatic analyses to investigate the biological functions of TRIM25, which were further validated by in vitro experiments, CIBERSORT algorithm, and ESTIMATE evaluation. TRIM25 expression was upregulated in glioma patients and can predict an unfavorable prognosis. Bioinformatic results indicated the involvement of TRIM25 in apoptosis and immune regulation. TRIM25 was associated with programmed death-ligand 1 (PD-L1) related and macrophage-induced immune suppression in gliomas. Meanwhile, silencing TRIM25 promoted apoptosis in glioma cells, which is attributed to its regulation of NF-κB. Therefore, TRIM25 contributed to the glioma malignant progression and suppressive immune microenvironments via NF-κB activation, which may play a therapeutic role in gliomas.

Abstract LB528: A microglia associated gene signature predicts survival risk in glioma patients

Abstract Background: Gliomas are common tumors that affect the brain and spinal cord. A complex tumor microenvironment is one of the leading reasons for a poor prognosis in glioma patients. Microglia, as part of the immune microenvironment of gliomas, may facilitate glioma growth and invasion. However, the correlation between the microglia abundance and glioma prognosis has not been clarified. Method: Our goal was to examine the relationship between microglia abundance and glioma prognosis. We analyzed the single-cell RNA sequences of human and mouse glioma cells to identify microglia marker genes. Then, we built a LASSO Cox regression model of microglia abundance signatures in gliomas. The Cancer Genome Atlas (TCGA) dataset (Low-grade gliomas, LGG) was used as the training cohort. The Repository of Molecular Brain Neoplasia Data (REMBRANDT) dataset and Chinese Glioma Genome Atlas (CGGA) dataset was used to validate the model as the validation cohorts. Findings: Overall survival was significantly lower in those with a high level of microglia infiltration. Additionally, we found that microglia could interact with the tumor microenvironment and genomic features of gliomas, making microglia abundance negatively associated with the prognosis of glioma patients. Microglia abundance was positively correlated with immune genes expression and immune-related pathways. By applying our LASSO Cox regression model to gliomas, we found that patients with high-risk scores had significantly shorter overall survival (OS) than those with low-risk scores. Conclusions: Taken together, our findings suggest that microglia abundance may be negatively associated with survival in gliomas. Based on the novel microglia abundance signatures, we developed a high accuracy LASSO Cox regression model. In this study, we demonstrated that the model accurately predicted the prognosis of glioma patients and could offer new therapeutic targets for microglial-directed therapies. Keywords: Gliomas; Microglia abundance; LASSO Cox regression; TCGA; Signature.- 1 - Citation Format: Chongming Jiang, Evelien Schaafsma, Yanding Zhao, Thinh Nguyen, Kenneth Zhu, Chao Cheng. A microglia associated gene signature predicts survival risk in glioma patients [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr LB528.

Pyroptosis-Related Gene Signature Predicts Prognosis and Indicates Immune Microenvironment Infiltration in Glioma

Objective: Gliomas are the most common primary tumors in the central nervous system with a bad prognosis. Pyroptosis, an inflammatory form of regulated cell death, plays a vital role in the progression and occurrence of tumors. However, the value of pyroptosis related genes (PRGs) in glioma remains poorly understood. This study aims to construct a PRGs signature risk model and explore the correlation with clinical characteristics, prognosis, tumor microenviroment (TME), and immune checkpoints. Methods: RNA sequencing profiles and the relevant clinical data were obtained from the Chinese Glioma Genome Atlas (CGGA), the Cancer Genome Atlas (TCGA), the Repository of Molecular Brain Neoplasia Data (REMBRANDT), and the Genotype-Tissue Expression Project (GTEx-Brain). Then, the differentially expressed pyroptosis related genes (PRGs) were identified, and the least absolute shrinkage and selection operator (LASSO) and mutiCox regression model was generated using the TCGA-train dataset. Then the expression of mRNA and protein levels of PRGs signature was detected through qPCR and human protein atlas (HPA). Further, the predictive ability of the PRGs-signature, prognostic analysis, and stratification analysis were utilized and validated using TCGA-test, CGGA, and REMBRANDT datasets. Subsequently, we constructed the nomogram by combining the PRGs signature and other key clinical features. Moreover, we used gene set enrichment analysis (GSEA), GO, KEGG, the tumor immune dysfunction and exclusion (TIDE) single-sample GSEA (ssGSEA), and Immunophenoscore (IPS) to determine the relationship between PRGs and TME, immune infiltration, and predict the response of immune therapy in glioma. Results: A four-gene PRGs signature (CASP4, CASP9, GSDMC, IL1A) was identified and stratified patients into low- or high-risk group. Survival analysis, ROC curves, and stratified analysis revealed worse outcomes in the high-risk group than in the low-risk group. Correlation analysis showed that the risk score was correlated with poor disease features. Furthermore, GSEA and immune infiltrating and IPS analysis showed that the PRGs signature could potentially predict the TME, immune infiltration, and immune response in glioma. Conclusion: The newly identified four-gene PRGs signature is effective in diagnosis and could robustly predict the prognosis of glioma, and its impact on the TME and immune cell infiltrations may provide further guidance for immunotherapy.

An Automated Brain Image Analysis System for Brain Cancer using Shearlets

In this paper, an Automated Brain Image Analysis (ABIA) system that classifies the Magnetic Resonance Imaging (MRI) of human brain is presented. The classification of MRI images into normal or low grade or high grade plays a vital role for the early diagnosis. The Non-Subsampled Shearlet Transform (NSST) that captures more visual information than conventional wavelet transforms is employed for feature extraction. As the feature space of NSST is very high, a statistical t-test is applied to select the dominant directional sub-bands at each level of NSST decomposition based on sub-band energies. A combination of features that includes Gray Level Co-occurrence Matrix (GLCM) based features, Histograms of Positive Shearlet Coefficients (HPSC), and Histograms of Negative Shearlet Coefficients (HNSC) are estimated. The combined feature set is utilized in the classification phase where a hybrid approach is designed with three classifiers; k-Nearest Neighbor (kNN), Naive Bayes (NB) and Support Vector Machine (SVM) classifiers. The output of individual trained classifiers for a testing input is hybridized to take a final decision. The quantitative results of ABIA system on Repository of Molecular Brain Neoplasia Data (REMBRANDT) database show the overall improved performance in comparison with a single classifier model with accuracy of 99% for normal/abnormal classification and 98% for low and high risk classification.

Modified Visual Geometric Group Architecture for MRI Brain Image Classification

The advancement of automated medical diagnosis in biomedical engineering has become an important area of research. Image classification is one of the diagnostic approaches that do not require segmentation which can draw quicker inferences. The proposed non-invasive diagnostic support system in this study is considered as an image classification system where the given brain image is classified as normal or abnormal. The ability of deep learning allows a single model for feature extraction as well as classification whereas the rational models require separate models. One of the best models for image localization and classification is the Visual Geometric Group (VGG) model. In this study, an efficient modified VGG architecture for brain image classification is developed using transfer learning. The pooling layer is modified to enhance the classification capability of VGG architecture. Results show that the modified VGG architecture outperforms the conventional VGG architecture with a 5% improvement in classification accuracy using 16 layers on MRI images of the REpository of Molecular BRAin Neoplasia DaTa (REMBRANDT) database.

Bendlets and Ensemble Learning Based MRI Brain Classification System

Brain tumours are composed of cells where the growth is unrestrained. Though the incidence rate is lower, it is a serious threatening disease to human lives. For effective treatment, an accurate and quick method to classify Magnetic Resonance Imaging (MRI) is required. To identify the meaningful patterns and to interpret images, pattern recognition algorithms are developed. In this work, an extension of Shearlet transform named Bendlets is employed to interpret MRI images and decision making is done by ensemble learning using k-Nearest Neighbor (kNN), Naive Bayesian and Support Vector Machine (SVM) classifiers. The Bendlet and Ensemble Learning (BEL) based system utilizes Bendlet Co-Occurrence Features (BCFs) and Histograms of Positive and Negative Bendlet Coefficients (HPBC & HNBC) from the dominant sub-band as texture descriptors. The rate of classification by the BEL system for the 200 images from REpository of Molecular BRAin Neoplasia DaTa (REMBRANDT) is 99.5% at the initial stage (normal/abnormal classification) and 99% at the final stage (low-risk/high-risk classification). Based on the results, the implementation of BEL system could provide continuous monitoring of the progress of brain tumour very effectively and also offers a real-time response.

Comprehensive analysis of the expression levels and prognostic values of PRDX family genes in glioma

Gliomas are a histologically and molecularly heterogeneous group of neoplasms accounting for 80% of malignant primary brain tumors. Growing evidence suggests that production of reactive oxygen species (ROS) is linked to glioma pathogenesis, although it is still unclear whether it is a cause or an effect of this process. Peroxiredoxins (PRDXs), a family of six antioxidant proteins, may promote or inhibit carcinogenesis, depending on the tumor type and stage. The current knowledge on their expression, regulation and functions in glioma is scarce. In this study, a comprehensive analysis of PRDXs expression in distinct glioma subtypes and non-tumor brain tissues was conducted using gene expression data from The Cancer Genome Atlas (TCGA), REpository for Molecular BRAin NeoplasiaDaTa (REMBRANDT), The Chinese Glioma Atlas (CGGA) and Gene Expression Omnibus (GEO) datasets. The association between gene expression and patient survival was investigated. DNA methylation, mutations, copy number alterations of deregulated PRDXs as well as the correlation between gene expression and tumor-infiltrating immune cells were assessed. The analysis revealed overexpression of PRDX1, PRDX4, and PRDX6 in most histological glioma types compared to the non-tumor tissues, while PRDX2, PRDX3 and PRDX5 expression remained unaltered. The expression of PRDX4 and PRDX6 was higher in mesenchymal than proneural and classical glioma subtypes. Moreover, lower expression of PRDX1, PRDX4 and PRDX6 was observed in tumors with a glioma CpG island methylator phenotype (G-CIMP) compared to non-G-CIMP tumors, as well as in isocitrate dehydrogenase (IDH) mutant and 1p/19q co-deleted gliomas compared to the wild-type counterparts. High expression of PRDX1, PRDX4 or PRDX6 correlated with poor survival of glioma patients. PRDX1 and PRDX6 displayed a positive correlation with different immune cell population in low grade gliomas and, to a lesser extent, in glioblastoma. PRDX1 expression exhibited negative correlation with DNA methylation. These results indicate that high expression of PRDX1, PRDX4 and PRDX6 is associated with poor outcome in gliomas.

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

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

The prognosis of glioblastoma: a large, multifactorial study

Objective Glioblastoma is the most common and fatal primary brain tumor in adults. Even with maximal resection and a series of postoperative adjuvant treatments, the median overall survival (OS) of glioblastoma patients remains approximately 15 months. The Huashan Hospital glioma bank contains more than 2000 glioma tissue samples with long-term follow-up data; almost half of these samples are from glioblastoma patients. Several large glioma databases with long-term follow-up data have reported outcomes of glioblastoma patients from countries other than China. We investigated the prognosis of glioblastoma patients in China and compared the survival outcomes among patients from different databases. Methods The data for 967 glioblastoma patients who underwent surgery at Huashan Hospital and had long-term follow-up records were obtained from our glioma registry (diagnosed from 29 March 2010, through 7 June 2017). Patients were eligible for inclusion if they underwent surgical resection for newly diagnosed glioblastomas and had available data of survival and personal information. Data of 778 glioblastoma patients were collected from three separate online databases (448 patients from The Cancer Genome Atlas (TCGA, https://cancergenome.nih.gov), 191 from REpository for Molecular BRAin Neoplasia DaTa (REMBRANDT) database (GSE108476) and 132 from data set GSE16011(Hereafter called as the French database). We compared the prognosis of glioblastoma patients from records among the different databases and the changes in survival outcomes of glioblastoma patients from Huashan Hospital over an 8-year period. Results The median OS of glioblastoma patients was 16.3 (95% CI: 15.4–17.2) months for Huashan Hospital, 13.8 (95% CI: 12.9–14.9) months for TCGA, 19.3 (95% CI: 17.0–20.0) months for the REMBRANDT database, and 9.1 months for the French database. The median OS of glioblastoma patients from Huashan Hospital improved from 15.6 (2010–2013, 95% CI: 14.4–16.6) months to 18.2 (2014–2017, 95% CI: 15.8–20.6) months over the study period (2010–2017). In addition, the prognosis of glioblastoma patients with total resection was significantly better than that of glioblastoma patients with sub-total resection or biopsy. Conclusions Our study confirms that treatment centered around maximal surgical resection brought survival benefits to glioblastoma patients after adjusting to validated prognostic factors. In addition, an improvement in prognosis was observed among glioblastoma patients from Huashan Hospital over the course of our study. We attributed it to the adoption of a new standard of neurosurgical treatment on the basis of neurosurgical multimodal technologies. Even though the prognosis of glioblastoma patients remains poor, gradual progress is being made.

Inception Architecture for Brain Image Classification

A non-invasive diagnostic support system for brain cancer diagnosis is presented in this study. Recently, very deeper convolution neural networks are designed for computerized tasks such as image classification, natural language processing. One of the standard architecture designs is the Visual Geometric Group (VGG) models. It uses a large number of small convolution filters (3x3) connected serially. Before applying max pooling, convolution filters are stacked up to four layers to extract features’ abstraction. The main drawback of going deeper is over fitting, and also updating gradient weights is very hard. These limitations are overcome using the inception module, which is wider rather than deeper. Also, it has parallel convolution layers with 3x3, 5x5, and 1x1 filters that reduce the computational complexity due to stacking, and the outputs are concatenated. This study’s experimental results show the usefulness of inception architecture for aiding brain image classification on Repository of Molecular Brain Neoplasia DaTa (REMBRANDT) Magnetic Resonance Imaging (MRI) images with an average accuracy of 95.1%, sensitivity of 96.2%, and specificity of 94%.

Design of Deep Neural Architecture for Brain Cancer Classification Using Pyramid Design

In this study, Deep Neural Network (DNN) with Pyramid Design (PD) is developed for brain image classification. The main objective is to design a non-invasive procedure for diagnosing brain cancer using deep learning. The signal intensities obtained from the brain tissues using Magnetic Resonance Imaging (MRI) can be used for effective treatment. A pyramid consists of a predefined number of convolution layers and a max pool layer to abstract features. In the DNN-PD approach, the pyramid is stacked with an increasing number of convolution layers and convolution filters from one pyramid to the next pyramid and so on. The stacking is employed recursively to get more accurate results. It shows that the proposed architecture gives efficient results with 98.5% accuracy using REpository of Molecular BRAin Neoplasia DaTa (REMBRANDT) database. The diagnostic results of the proposed Deep Neural architecture with the expert’s analysis can reduce the biopsies, a dangerous procedure with a great deal of pain.

Identification and Validation of an 11-Ferroptosis Related Gene Signature and Its Correlation With Immune Checkpoint Molecules in Glioma.

BackgroundGlioma is the most common primary malignant brain tumor with significant mortality and morbidity. Ferroptosis, a novel form of programmed cell death (PCD), is critically involved in tumorigenesis, progression and metastatic processes.MethodsWe revealed the relationship between ferroptosis-related genes and glioma by analyzing the mRNA expression profiles from The Cancer Genome Atlas (TCGA), Chinese Glioma Genome Atlas (CGGA), GSE16011, and the Repository of Molecular Brain Neoplasia Data (REMBRANDT) datasets. The least absolute shrinkage and selection operator (LASSO) Cox regression analysis was performed to construct a ferroptosis-associated gene signature in the TCGA cohort. Glioma patients from the CGGA, GSE16011, and REMBRANDT cohorts were used to validate the efficacy of the signature. Receiver operating characteristic (ROC) curve analysis was applied to measure the predictive performance of the risk score for overall survival (OS). Univariate and multivariate Cox regression analyses of the 11-gene signature were performed to determine whether the ability of the prognostic signature in predicting OS was independent. Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were conducted to identify the potential biological functions and pathways of the signature. Subsequently, we performed single sample gene set enrichment analysis (ssGSEA) to explore the correlation between risk scores and immune status. Finally, seven putative small molecule drugs were predicted by Connectivity Map.ResultsThe 11-gene signature was identified to divide patients into two risk groups. ROC curve analysis indicated the 11-gene signature as a potential diagnostic factor in glioma patients. Multivariate Cox regression analyses showed that the risk score was an independent predictive factor for overall survival. Functional analysis revealed that genes were enriched in iron-related molecular functions and immune-related biological processes. The results of ssGSEA indicated that the 11-gene signature was correlated with the initiation and progression of glioma. The small molecule drugs we selected showed significant potential to be used as putative drugs.Conclusionwe identified a novel ferroptosis-related gene signature for prognostic prediction in glioma patients and revealed the relationship between ferroptosis-related genes and immune checkpoint molecules.

Identification of Periostin as a Potential Biomarker in Gliomas by Database Mining

Bioinformatics analysis integrating microenvironmental factors and single cell analysis segregated the glioblastoma (GBM) subtype into 3 subtypes: proneural, classic, and mesenchymal. Mesenchymal GBM tends to have the worst survival but benefits from aggressive treatment protocols. Therefore, it is clinically meaningful to identify relevant biomarkers to distinguish the mesenchymal subtype. Moreover, in developing nations with limited resources, rigorous examinations are costly and inefficient for patient care. In this study, we analyzed The Cancer Genome Atlas (TCGA)-Glioblastoma and TCGA-Low-Grade Glioma RNA sequencing (RNAseq) cohorts and confirmed that the mesenchymal subtype was associated with the worst prognosis. We identified periostin (POSTN) as a mesenchymal subtype biomarker with prognostic value across histologic grades and confirmed the reliability of POSTN by gene expression meta-analysis combining TCGA, Chinese Glioma Genome Atlas (CGGA) and REMBRANDT (Repository for Molecular Brain Neoplasia Data) GBM cohorts (hazard ratio, 1.71 [range, 1.47-2.07], n= 693) and LGG cohorts (hazard ratio, 2.55 [range, 1.61-4.05], n= 1226). By using available online glioma databases, our study provided an insight into the expression of POSTN as an independent predictor for patients with glioma (GBM and LGG) and could be useful for diagnostic simplification to identify high-risk groups.

Acetylcholine Receptor Activation as a Modulator of Glioblastoma Invasion.

Grade IV astrocytomas, or glioblastomas (GBMs), are the most common malignant primary brain tumor in adults. The median GBM patient survival of 12–15 months has remained stagnant, in spite of treatment strategies, making GBMs a tremendous challenge clinically. This is at least in part due to the complex interaction of GBM cells with the brain microenvironment and their tendency to aggressively infiltrate normal brain tissue. GBMs frequently invade supratentorial brain regions that are richly innervated by neurotransmitter projections, most notably acetylcholine (ACh). Here, we asked whether ACh signaling influences the biology of GBMs. We examined the expression and function of known ACh receptors (AChRs) in large GBM datasets, as well as, human GBM cell lines and patient-derived xenograft lines. Using RNA-Seq data from the “The Cancer Genome Atlas” (TCGA), we confirmed the expression of AChRs and demonstrated the functionality of these receptors in GBM cells with time-lapse calcium imaging. AChR activation did not alter cell proliferation or migration, however, it significantly increased cell invasion through complex extracellular matrices. This was due to the enhanced activity of matrix metalloproteinase-9 (MMP-9) from GBM cells, which we found to be dependent on an intracellular calcium-dependent mechanism. Consistent with these findings, AChRs were significantly upregulated in regions of GBM infiltration in situ (Ivy Glioblastoma Atlas Project) and elevated expression of muscarinic AChR M3 correlated with reduced patient survival (TCGA). Data from the Repository for Molecular Brain Neoplasia Data (REMBRANDT) dataset also showed the co-expression of choline transporters, choline acetyltransferase, and vesicular acetylcholine transporters, suggesting that GBMs express all the proteins required for ACh synthesis and release. These findings identify ACh as a modulator of GBM behavior and posit that GBMs may utilize ACh as an autocrine signaling molecule.

ARL3 is downregulated and acts as a prognostic biomarker in glioma

BackgroundGlioma is the most common primary malignant brain tumor in adults with a poor prognosis. ARL3 is a member of the ARF family, and plays a key role in ciliary function and lipid-modified protein trafficking. ARL3 has been reported to be involved in ciliary diseases, in which it affects kidney and photoreceptor development. However, the functional role of ARL3 in cancer remains unknown. In this study, we aimed to explore ARL3 expression and its roles in glioma prognosis.MethodsRT-PCR and immunohistochemistry were performed to examine the expression level of ARL3 in glioma samples. Data from The Cancer Genome Atlas (TCGA), Chinese Glioma Genome Atlas (CGGA) and Repository for Molecular Brain Neoplasia Data (REMBRANDT) databases were employed to investigate ARL3 expression and its roles in glioma prognosis. A nomogram for predicting 3- or 5-year survival was established using Cox proportional hazards regression. Finally, gene ontology (GO) analysis, gene set enrichment analysis (GSEA), and gene set variation analysis (GSVA) were performed to explore the biological function.ResultsARL3 expression was downregulated in glioma, and associated with poor prognosis in glioma patients. The C-indexes, areas under the ROC curve and calibration plots of the nomogram indicated an effective predictive performance for glioma patients. In addition, GO and pathway analyses suggested the involvement of ARL3 in angiogenesis and immune cell infiltration in the microenvironment.ConclusionsLow ARL3 expression predicted poor prognosis and contributed to antiangiogenesis and the proportion of infiltrating immune cells in the GBM microenvironment. Thus, ARL3 may be a prognostic marker and therapeutic target for glioma.

MGSEA \u2013 a multivariate Gene set enrichment analysis

BackgroundGene Set Enrichment Analysis (GSEA) is a powerful tool to identify enriched functional categories of informative biomarkers. Canonical GSEA takes one-dimensional feature scores derived from the data of one platform as inputs. Numerous extensions of GSEA handling multimodal OMIC data are proposed, yet none of them explicitly captures combinatorial relations of feature scores from multiple platforms.ResultsWe propose multivariate GSEA (MGSEA) to capture combinatorial relations of gene set enrichment among multiple platform features. MGSEA successfully captures designed feature relations from simulated data. By applying it to the scores of delineating breast cancer and glioblastoma multiforme (GBM) subtypes from The Cancer Genome Atlas (TCGA) datasets of CNV, DNA methylation and mRNA expressions, we find that breast cancer and GBM data yield both similar and distinct outcomes. Among the enriched functional categories, subtype-specific biomarkers are dominated by mRNA expression in many functional categories in both cancer types and also by CNV in many functional categories in breast cancer. The enriched functional categories belonging to distinct combinatorial patterns are involved different oncogenic processes: cell proliferation (such as cell cycle control, estrogen responses, MYC and E2F targets) for mRNA expression in breast cancer, invasion and metastasis (such as cell adhesion and epithelial-mesenchymal transition (EMT)) for CNV in breast cancer, and diverse processes (such as immune and inflammatory responses, cell adhesion, angiogenesis, and EMT) for mRNA expression in GBM. These observations persist in two external datasets (Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) for breast cancer and Repository for Molecular Brain Neoplasia Data (REMBRANDT) for GBM) and are consistent with knowledge of cancer subtypes. We further compare the characteristics of MGSEA with several extensions of GSEA and point out the pros and cons of each method.ConclusionsWe demonstrated the utility of MGSEA by inferring the combinatorial relations of multiple platforms for cancer subtype delineation in three multi-OMIC datasets: TCGA, METABRIC and REMBRANDT. The inferred combinatorial patterns are consistent with the current knowledge and also reveal novel insights about cancer subtypes. MGSEA can be further applied to any genotype-phenotype association problems with multimodal OMIC data.