The Cancer Genome Atlas Glioma Research Articles

Discovery of GJC1 as a prognostic biomarker in glioma cells: insights into its cell-cycle relationship and differential expression in non-neuronal cells.

Gliomas, originating from the most common non-neuronal cells in the brain (glial cells), are the most common brain tumors and are associated with high mortality and poor prognosis. Glioma cells exhibit a tendency to disrupt normal cell-cycle regulation, leading to abnormal proliferation and malignant growth. This study investigated the predictive potential of GJC1 in gliomas and explored its relationship with the cell cycle. Retrospective analysis of RNA-seq and single-cell sequencing data was conducted using the Chinese Glioma Genome Atlas (CGGA) and The Cancer Genome Atlas (TCGA) databases. The differential expression of GJC1 in gliomas with various pathological features and in different non-neuronal cell groups was analyzed. Functional data were examined using gene set variation analysis (GSVA). Furthermore, CellMiner was used to evaluate the relationship between GJC1 expression and predicted treatment response across these databases. GJC1 expression was enriched in high-grade gliomas and 1p/19q non-codeletion gliomas. GJC1 enrichment was observed in classical and mesenchymal subtypes within the TCGA glioma subtype group. In single-cell subgroup analysis, GJC1 expression was higher in glioma tissues compared to other non-neuronal cells. Additionally, the TCGA classical subtype of glioma cells exhibited more GJC1 expression than the other subgroups. GJC1 emerged as an independent prognostic factor for overall survival in glioma. GSVA unveiled potential mechanisms by which GJC1 may impact cell-cycle regulation in glioma. Finally, a significant correlation was observed between GJC1 expression and the sensitivity of multiple anti-cancer drugs. These findings confirmed GJC1 as a novel biomarker and provided insights into the differential gene expression in non-neuronal cells and the impact of the cell cycle on gliomas. Consequently, GJC1 may be used to predict glioma prognosis and has potential therapeutic value.

The Inflammatory Characteristics of Symptomatic Glioma Associated With Poor Prognosis and Chemoresistance via Tumor Necrosis Factor Signaling Pathway.

Among gliomas, the most common primary malignant brain tumor, incidental gliomas account for 2.5%-5% of cases. The controversy over whether to pursue immediate treatment or adopt a wait-and-see approach remains, and more molecular and immunological evidence is needed for definitive treatment decisions. Total RNA sequencing (RNA-seq) data and single cell RNA sequencing (scRNA-seq) data were retrospectively analyzed to compare the molecular and immunological tumor microenvironment differences between incidental glioma and symptomatic glioma samples. These were classified using symptom data from The Cancer Genome Atlas (TCGA) and public dataset. RNA-seq analysis of the GBMLGG dataset identified 343 genes upregulated in symptomatic glioma and 118 in incidental glioma, with 104 common genes upregulated in symptomatic glioma across both the TCGA and Chinese Glioma Genome Atlas (CGGA) datasets. Enrichment analysis revealed that these 104 genes in symptomatic glioma were significantly associated with immunological pathways. scRNA-seq analysis of glioma revealed 11 cell types, including T cells, myeloid cells, and oligodendrocytes, with the tumor necrosis factor (TNF) signaling pathway strongly influencing other cell types, particularly myeloid cells. Enrichment and survival analyses showed that TNF signaling is associated with temozolomide resistance and poorer prognosis in glioma patients. The findings suggest that symptomatic glioma enhances inflammatory responses linked to poor prognosis and chemoresistance. This supports the hypothesis that immediate treatment of incidental glioma may improve patient outcomes over a wait-and-see approach.

Integration of Multi-Omics Data for the Classification of Glioma Types and Identification of Novel Biomarkers

Glioma is currently one of the most prevalent types of primary brain cancer. Given its high level of heterogeneity along with the complex biological molecular markers, many efforts have been made to accurately classify the type of glioma in each patient, which, in turn, is critical to improve early diagnosis and increase survival. Nonetheless, as a result of the fast-growing technological advances in high-throughput sequencing and evolving molecular understanding of glioma biology, its classification has been recently subject to significant alterations. In this study, we integrate multiple glioma omics modalities (including mRNA, DNA methylation, and miRNA) from The Cancer Genome Atlas (TCGA), while using the revised glioma reclassified labels, with a supervised method based on sparse canonical correlation analysis (DIABLO) to discriminate between glioma types. We were able to find a set of highly correlated features distinguishing glioblastoma from lower-grade gliomas (LGGs) that were mainly associated with the disruption of receptor tyrosine kinases signaling pathways and extracellular matrix organization and remodeling. Concurrently, the discrimination of the LGG types was characterized primarily by features involved in ubiquitination and DNA transcription processes. Furthermore, we could identify several novel glioma biomarkers likely helpful in both diagnosis and prognosis of the patients, including the genes PPP1R8, GPBP1L1, KIAA1614, C14orf23, CCDC77, BVES, EXD3, CD300A, and HEPN1. Collectively, this comprehensive approach not only allowed a highly accurate discrimination of the different TCGA glioma patients but also presented a step forward in advancing our comprehension of the underlying molecular mechanisms driving glioma heterogeneity. Ultimately, our study also revealed novel candidate biomarkers that might constitute potential therapeutic targets, marking a significant stride toward personalized and more effective treatment strategies for patients with glioma.

Cancer-associated fibroblast-associated gene IGFBP2 promotes glioma progression through induction of M2 macrophage polarization.

We elucidated the molecular mechanism of cancer-associated fibroblast (CAF)-associated gene insulin-like growth factor binding protein-2 (IGFBP2)-induced M2 macrophage polarization in the tumor microenvironment involved in glioma progression. The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) provided bulk RNA-sequencing datasets, ESTIMATE scores for glioma stromal cells, and overall survival-clinicopathological correlation analyses. TIMER provided CAF abundance in the TCGA glioma-related dataset, differential gene analysis was performed for high- and low-CAF groups, and weighted gene coexpression network analysis identified CAF-related genes. Univariate and multifactorial cyclooxygenase (COX) regression analyses created the CAF risk models single sample gene set enrichment analysis, CIBERSORT, and GSE84465. Mice were implanted with gliomas, and Western blot and RT-quantitative PCR showed IGFBP2 in tumor tissues. Adeno-associated virus (AAV) decreased IGFBP2, flow cytometry measured M1 and M2 macrophage ratios, and immunohistochemistry detected markers. TCGA and CGGA transcriptome data showed malignant gliomas had higher stromal cell scores and worse prognoses. Low- and high-CAF TCGA gliomas were detected, and differential expression, WGCNA, and multifactorial COX identified 132 CAF-related genes and seven high-risk genes (CPQ, EFEMP2, IGFBP2, RAB42, TNFRSF12A, and VASN). Neither CAF risk score, grade, nor 1p/19q affected glioma prognosis. CAF only enriched EFEMP2 and IGFBP2. Gene Expression Profiling Interactive Analysis compared EFEMP2 and IGFBP2 expression in normal brain tissue and gliomas. Low-grade glioma and malignant glioblastoma highly expressed IGFBP2 and EFEMP2. GSEA raised IGFBP2. CIBERSORT linked M2 macrophage infiltration to TCGA glioma immune cell subpopulation IGFBP2 expression. IGFBP2 knockdown stopped mouse glioma and M2 macrophage polarization. CAF plays a procarcinogenic role in glioma, and the CAF-related gene IGFBP2 could promote glioma progression by inducing M2 macrophage polarization.NEW & NOTEWORTHY The cancer-associated fibroblast (CAF)-related gene insulin-like growth factor binding protein-2 (IGFBP2) is highly expressed in gliomas and is associated with poor prognosis. CAF-related gene IGFBP2 promotes glioma progression by inducing polarization of M2 macrophages. This study provides a new basis for an in-depth investigation of the functional mechanisms of the glioma tumor microenvironment and the search for key genes involved in immune regulation in CAF.

Disclosing transcriptomics network-based signatures of glioma heterogeneity using sparse methods

Gliomas are primary malignant brain tumors with poor survival and high resistance to available treatments. Improving the molecular understanding of glioma and disclosing novel biomarkers of tumor development and progression could help to find novel targeted therapies for this type of cancer. Public databases such as The Cancer Genome Atlas (TCGA) provide an invaluable source of molecular information on cancer tissues. Machine learning tools show promise in dealing with the high dimension of omics data and extracting relevant information from it. In this work, network inference and clustering methods, namely Joint Graphical lasso and Robust Sparse K-means Clustering, were applied to RNA-sequencing data from TCGA glioma patients to identify shared and distinct gene networks among different types of glioma (glioblastoma, astrocytoma, and oligodendroglioma) and disclose new patient groups and the relevant genes behind groups’ separation. The results obtained suggest that astrocytoma and oligodendroglioma have more similarities compared with glioblastoma, highlighting the molecular differences between glioblastoma and the others glioma subtypes. After a comprehensive literature search on the relevant genes pointed our from our analysis, we identified potential candidates for biomarkers of glioma. Further molecular validation of these genes is encouraged to understand their potential role in diagnosis and in the design of novel therapies.

Development and validation of an immune infiltration/tumor proliferation-related Notch3 nomogram for predicting survival in patients with primary glioblastoma.

Background: Notch receptors (Notch 1/2/3/4), the critical effectors of the Notch pathway, participate in the tumorigenesis and progression of many malignancies. However, the clinical roles of Notch receptors in primary glioblastoma (GBM) have not been fully elucidated. Methods: The genetic alteration-related prognostic values of Notch receptors were determined in the GBM dataset from The Cancer Genome Atlas (TCGA). Two GBM datasets from TCGA and Chinese Glioma Genome Atlas (CGGA) were used to explore the differential expression between Notch receptors and IDH mutation status, and GBM subtypes. The biological functions of Notch Receptors were explored by Gene Ontology and KEGG analysis. The expression and prognostic significance of Notch receptors were determined in the TCGA and CGGA datasets and further validated in a clinical GBM cohort by immunostaining. A Notch3-based nomogram/predictive risk model was constructed in the TCGA dataset and validated in the CGGA dataset. The model performance was evaluated by receiver operating curves, calibration curves, and decision curve analyses. The Notch3-related phenotypes were analyzed via CancerSEA and TIMER. The proliferative role of Notch3 in GBM was validated in U251/U87 glioma cells by Western blot and immunostaining. Results: Notch receptors with genetic alterations were associated with poor survival of GBM patients. Notch receptors were all upregulated in GBM of TCGA and CGGA databases and closely related to the regulation of transcription, protein-lysine N-methyltransferase activity, lysine N-methyltransferase activity, and focal adhesion. Notch receptors were associated with Classical, Mesenchymal, and Proneural subtypes. Notch1 and Notch3 were closely correlated with IDH mutation status and G-CIMP subtype. Notch receptors displayed the differential expression at the protein level and Notch3 showed a prognostic significance in a clinical GBM cohort. Notch3 presented an independent prognostic role for primary GBM (IDH1 mutant/wildtype). A Notch3-based predictive risk model presented favorable accuracy, reliability, and net benefits for predicting the survival of GBM patients (IDH1 mutant/wildtype and IDH1 wildtype). Notch3 was closely related to immune infiltration (macrophages, CD4+ T cells, and dendritic cells) and tumor proliferation. Conclusion: Notch3-based nomogram served as a practical tool for anticipating the survival of GBM patients, which was related to immune-cell infiltration and tumor proliferation.

Construction of a pyroptosis-related lncRNAs signature for predicting prognosis and immunotherapy response in glioma.

Recent studies have proved that pyroptosis-related long non-coding RNAs (PRlncRNAs) are closely linked to tumor progression, prognosis, and immunity. Here, we systematically evaluated the correlation of PRlncRNAs with glioma prognosis. This study included 3 glioma cohorts (The Cancer Genome Atlas, Chinese Glioma Genome Atlas, and Gravendeel). Through Pearson correlation analysis, PRlncRNAs were screened from these 3 cohorts. Univariate Cox regression analysis was then carried out to determine the prognostic PRlncRNAs. A pyroptosis-related lncRNAs signature (PRLS) was then built by least absolute shrinkage and selection operator and multivariate Cox analyses. We systematically evaluated the correlation of the PRLS with the prognosis, immune features, and tumor mutation burden in glioma. A total of 14 prognostic PRlncRNAs overlapped in all cohorts and were selected as candidate lncRNAs. Based on The Cancer Genome Atlas cohort, a PRLS containing 7 PRlncRNAs was built. In all cohorts, the PRLS was proved to be a good predictor of glioma prognosis, with a higher risk score related to a poorer prognosis. We observed obvious differences in the immune microenvironment, immune cell infiltration level, and immune checkpoint expression in low- and high-risk subgroups. Compared with low-risk cases, high-risk cases had lower Tumor Immune Dysfunction and Exclusion scores and greater tumor mutation burden, indicating that high-risk cases can be more sensitive to immunotherapy. A nomogram combining PRLS and clinical parameters was constructed, which showed more robust and accurate predictive power. In conclusion, the PRLS is a potentially useful indicator for predicting prognosis and response to immunotherapy in glioma. Our findings may provide a useful insight into clinically individualized treatment strategies for patients.

Cancer cell intrinsic TIM-3 induces glioblastoma progression

Glioblastoma (GBM) is identified to share common signal pathways between glioma and immune cells. Here, we find that Tcell immunoglobulin domain and mucin domain protein 3 (TIM-3) is one of the most common co-inhibitory immune checkpoints in GBM shared by tumor and non-tumor cells. Glioma cell-intrinsic TIM-3 is involved in not only regulating malignant behaviors of glioma cells but also inducing macrophage migration and transition to anti-inflammatory/pro-tumorigenic phenotype by a TIM-3/interleukin 6 (IL6) signal. In mechanism, as one of the major regulators of IL6, TIM-3 regulates its expression through activating NF-κB. Blocking this feedback loop by Tocilizumab, an IL6R inhibitor, inhibited the above effects and repressed the tumorigenicity of GBM invivo. Our work identifies glioma cell-intrinsic functions of TIM-3/IL6 signal mediating the crosstalk feedback loop between glioma cells and tumor-associated macrophages (TAMs). Blocking this feedback loop may provide a novel therapeutic strategy for GBM.

A specific immune signature for predicting the prognosis of glioma patients with IDH1-mutation and guiding immune checkpoint blockade therapy.

Isocitrate dehydrogenase (IDH1) is frequently mutated in glioma tissues, and this mutation mediates specific tumor-promoting mechanisms in glioma cells. We aimed to identify specific immune biomarkers for IDH1-mutation (IDH1mt) glioma. The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) were used to obtain RNA sequencing data and clinical characteristics of glioma tissues, while the stromal and immune scores of TCGA glioma tissues were determined using the ESTIMATE algorithm. Differentially expressed genes (DEGs), the protein–protein interaction(PPI) network, and least absolute shrinkage and selection operator (LASSO) and Cox regression analyses were used to select hub genes associated with stroma and immune scores and the prognoses of patients and to construct the risk model. The practicability and specificity of the risk model in both IDH1mt and IDH1-wildtype (wtIDH1) gliomas in TCGA and CGGA were evaluated. Molecular mechanisms, immunological characteristics and benefits of immune checkpoint blockade therapy in glioma tissues with IDH1mt were analyzed using GSEA, immunohistochemical staining, CIBERSORT, and T-cell dysfunction and exclusion (TIDE) analysis. The overall survival rate for IDH1mt-glioma patients with high stroma/immune scores was lower than that for those with low stroma/immune scores. A total of 222 DEGs were identified in IDH1mt glioma tissues with high stroma/immune scores. Among them, 72 genes had interactions in the PPI network, while three genes, HLA-DQA2, HOXA3, and SAA2, were selected as hub genes and used to construct risk models classifying patients into high- and low-risk score groups, followed by LASSO and Cox regression analyses. This risk model showed prognostic value in IDH1mt glioma in both TCGA and CCGA; nevertheless, the model was not suitable for wtIDH1 glioma. The risk model may act as an independent prognostic factor for IDH1mt glioma. IDH1mt glioma tissues from patients with high-risk scores showed more infiltration of M1 and CD8 T cells than those from patients with low-risk scores. Moreover, TIDE analysis showed that immune checkpoint blockade(ICB) therapy was highly beneficial for IDH1mt patients with high-risk scores. The risk model showed specific potential to predict the prognosis of IDH1mt-glioma patients, as well as guide ICB, contributing to the diagnosis and therapy of IDH1mt-glioma patients.

Machine Learning-Based Analysis of Glioma Grades Reveals Co-Enrichment.

Simple SummaryGliomas are heterogenous types of cancer, therefore the therapy should be personalized and targeted toward specific pathways. We developed a methodology that corrected strong batch effects from The Cancer Genome Atlas datasets and estimated glioma grade-specific co-enrichment mechanisms using machine learning. Our findings created hypotheses for annotations, e.g., pathways, that should be considered as therapeutic targets.Gliomas develop and grow in the brain and central nervous system. Examining glioma grading processes is valuable for improving therapeutic challenges. One of the most extensive repositories storing transcriptomics data for gliomas is The Cancer Genome Atlas (TCGA). However, such big cohorts should be processed with caution and evaluated thoroughly as they can contain batch and other effects. Furthermore, biological mechanisms of cancer contain interactions among biomarkers. Thus, we applied an interpretable machine learning approach to discover such relationships. This type of transparent learning provides not only good predictability, but also reveals co-predictive mechanisms among features. In this study, we corrected the strong and confounded batch effect in the TCGA glioma data. We further used the corrected datasets to perform comprehensive machine learning analysis applied on single-sample gene set enrichment scores using collections from the Molecular Signature Database. Furthermore, using rule-based classifiers, we displayed networks of co-enrichment related to glioma grades. Moreover, we validated our results using the external glioma cohorts. We believe that utilizing corrected glioma cohorts from TCGA may improve the application and validation of any future studies. Finally, the co-enrichment and survival analysis provided detailed explanations for glioma progression and consequently, it should support the targeted treatment.

High expression of TXNDC11 indicated unfavorable prognosis of glioma.

BackgroundThioredoxin domain containing 11 (TXNDC11) has been implicated in numerous cancers. Nevertheless, the function of TXNDC11 in glioma is not well described. This study aimed to assess clinical significance of TXNDC11 in glioma based on bioinformatics analysis and immunohistochemical (IHC) staining.MethodsGEPIA2, The Cancer Genome Atlas (TCGA), and Gene Expression Omnibus (GEO) databases were employed to detect the levels of TXNDC11 transcript in glioma. Gene expression profiles and data from the methylation chip with clinical details from TCGA and Chinese Glioma Genome Atlas (CGGA) of glioma samples were examined. The methylation of TXNDC11 in glioma was evaluated by 450K methylation chip data analysis. The pathways involved in TXNDC11 expression were screened by gene set enrichment analysis (GSEA). The correlation between TXNDC11 and immune cells was analyzed. Protein level of TXNDC11 was detected by IHC staining in glioma specimens.ResultsTXNDC11 was highly expressed in glioma, and high TXNDC11 expression was associated with poor overall survival (OS) and worse clinical prognostic variables. The methylation of cg04399632 was statistically different between glioma samples and normal samples, and was negatively correlated with TXNDC11 expression in glioma patients. Survival analysis demonstrated a poorer prognosis in glioma patients with cg04399632 hypomethylation. TXNDC11-high phenotype was associated with certain immune-related pathways and other signaling pathways in glioma. The expression of TXNDC11 was correlated positively with M2 macrophage infiltration and negatively with M0 and M1 macrophage infiltration. IHC staining confirmed that TXNDC11 expression increased in higher-grade glioma.ConclusionsHigh expression of TXNDC11 may predict unfavorable prognosis of glioma patients.

TMOD-04. T-TYPE CALCIUM CHANNELS DRIVE GLIOBLASTOMA INITIATION AND PROGRESSION

Abstract Glioblastoma is the most common primary malignant brain tumor with a dismal median survival of 15 months. Calcium signaling regulates a plethora of cellular processes making it an ideal target for therapeutic intervention. Our group identified T-type calcium channels (TTCCs) as upregulated in GBM cells, stem cells and human tumors. Analysis of the Cancer Genome Atlas (TCGA) demonstrates for the first time that >20% of GBM patients exhibit mutation, amplification or mRNA upregulation of TTCCs. TCGA and Chinese Glioma Genome Atlas (CGGA) data analyses demonstrate that patients with alterations in TTCC trend toward worse survival compared to normal TTCC expression. Our group utilized an FDA repurposed TTCC blocker Mibefradil to demonstrate inhibition of TTCCs decreases cancer associated signaling, transcription, malignancy parameters and in vivo tumor growth. Mibefradil treatment sensitized Gliomas stem like cells (GSC) xenografts to temozolomide (TMZ) chemotherapy and radiotherapy. To elucidate the roles of the individual TTCCs in vivo we created RCAS/Tva-shTTCC mice for silencing of Cav3.1 and Cav3.2. The silencing of the TTCCs individually decreased tumor growth as measured by MRI and H&E staining. Silencing of TTCCs extended survival of mice in vivo. Silencing of TTCC decreases proliferation (Ki67) and increases apoptosis (Caspase 3). Our findings suggest T-type calcium channels contribute to tumor initiation and progression.

Prognostic Model and Nomogram Construction Based on a Novel Ferroptosis-Related Gene Signature in Lower-Grade Glioma.

Background: Low-grade glioma (LGG) is considered a fatal disease for young adults, with overall survival widely ranging from 1 to 15 years depending on histopathologic and molecular subtypes. As a novel type of programmed cell death, ferroptosis was reported to be involved in tumorigenesis and development, which has been intensively studied in recent years. Methods: For the discovery cohort, data from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) were used to identify the differentially expressed and prognostic ferroptosis-related genes (FRGs). The least absolute shrinkage and selection operator (LASSO) and multivariate Cox were used to establish a prognostic signature with the above-selected FRGs. Then, the signature was developed and validated in TCGA and Chinese Glioma Genome Atlas (CGGA) databases. By combining clinicopathological features and the FRG signature, a nomogram was established to predict individuals’ one-, three-, and five-year survival probability, and its predictive performance was evaluated by Harrell’s concordance index (C-index) and calibration curves. Enrichment analysis was performed to explore the signaling pathways regulated by the signature. Results: A novel risk signature contains seven FRGs that were constructed and were used to divide patients into two groups. Kaplan–Meier (K−M) survival curve and receiver-operating characteristic (ROC) curve analyses confirmed the prognostic performance of the risk model, followed by external validation based on data from the CGGA. The nomogram based on the risk signature and clinical traits was validated to perform well for predicting the survival rate of LGG. Finally, functional analysis revealed that the immune statuses were different between the two risk groups, which might help explain the underlying mechanisms of ferroptosis in LGG. Conclusion: In conclusion, this study constructed a novel and robust seven-FRG signature and established a prognostic nomogram for LGG survival prediction.

RBM8A promotes growth and invasion through the Notch/STAT3 pathway in glioblastoma.

e14038 Background: Glioblastoma (GBM) is a prevalent brain malignance with an extremely poor prognosis, which is attributable to its invasive biological behaviors. The RNA-binding motif protein 8A (RBM8A) has different effects on various human cancers. However, the implication of RBM8A in glioblastoma progression remains unclear. Methods: Glioblastoma (GBM) data set was downloaded from the Cancer Genome Atlas (TCGA). Differential expression analysis was used to screen the differentially expressed genes (DEGs) between GBM and control, RBM8A high and low expression samples, Gene Ontology (GO) analysis and Kyoto Encyclopedia of Gene Genomes (KEGG) analysis were performed on the co-upregulated DEGs. Additionally, We investigated the expression levels of RBM8A in 94 glioblastoma patients and explored the correlation between the RBM8A expressions with prognosis. Using in vitro and in vivo assays, we addressed the functional impacts of RBM8A on and the underlying mechanisms through which RBM8A contribute to glioblastoma progression. In addition, a comprehensive regulatory network of RBM8A regulation was constructed based on STRING database. Molecular docking model was used to predict the possibility of RBM8A binding to target genes. Combined with TCGA and Chinese glioma genome map (CGGA), gene set variance analysis (GSVA) was used to calculate the GSVA scores of the genes involved in the mechanism. Receiver operator characteristic curve (ROC) curve analysis and survival analysis were performed to explore the prognostic and diagnostic ability of GSVA score for GBM. Results: Our results indicate that higher RBM8A expression in glioblastoma tissues was associated with a poor prognosis. In addition, functional enrichment analysis based on genes related to RBM8A expression showed that RBM8A was related to cell cycle and Notch signaling pathway. RBM8A may promote glioblastoma cell proliferation and migration by activating Notch/STAT3 pathway in glioblastoma cells. In vitro and in vivo assays confirmed that knocking down RBM8A inhibited glioblastoma progression and invasion ability. We also observed that the pro-oncogenic effects of RBM8A in glioblastoma tissues were mediated by activation of the Notch/STAT3 pathway. Finally, it was concluded that the GSVA score has good diagnostic and prognostic value for GBM. Conclusions: RBM8A may promote glioblastoma cell proliferation and migration by activating Notch/STAT3 pathway in glioblastoma cells, suggesting that RBM8A may serve as a potential therapeutic target for the treatment of glioblastoma.

A Prognostic Microenvironment-Related Immune Signature via ESTIMATE (PROMISE Model) Predicts Overall Survival of Patients With Glioma.

ObjectiveIn the development of immunotherapies in gliomas, the tumor microenvironment (TME) needs to be investigated. We aimed to construct a prognostic microenvironment-related immune signature via ESTIMATE (PROMISE model) for glioma.MethodsStromal score (SS) and immune score (IS) were calculated via ESTIMATE for each glioma sample in the cancer genome atlas (TCGA), and differentially expressed genes (DEGs) were identified between high-score and low-score groups. Prognostic DEGs were selected via univariate Cox regression analysis. Using the lower-grcade glioma (LGG) data set in TCGA, we performed LASSO regression based on the prognostic DEGs and constructed a PROMISE model for glioma. The model was validated with survival analysis and the receiver operating characteristic (ROC) in TCGA glioma data sets (LGG, glioblastoma multiforme [GBM] and LGG+GBM) and Chinese glioma genome atlas (CGGA). A nomogram was developed to predict individual survival chances. Further, we explored the underlying mechanisms using gene set enrichment analysis (GSEA) and Cibersort analysis of tumor-infiltrating immune cells between risk groups as defined by the PROMISE model.ResultsWe obtained 220 upregulated DEGs and 42 downregulated DEGs in both high-IS and high-SS groups. The Cox regression highlighted 155 prognostic DEGs, out of which we selected 4 genes (CD86, ANXA1, C5AR1, and CD5) to construct a PROMISE model. The model stratifies glioma patients in TCGA as well as in CGGA with distinct survival outcome (P<0.05, Hazard ratio [HR]>1) and acceptable predictive accuracy (AUCs>0.6). With the nomogram, an individualized survival chance could be predicted intuitively with specific age, tumor grade, Isocitrate dehydrogenase (IDH) status, and the PROMISE risk score. ROC showed significant discrimination with the area under curves (AUCs) of 0.917 and 0.817 in TCGA and CGGA, respectively. GSEA between risk groups in both data sets were significantly enriched in multiple immune-related pathways. The Cibersort analysis highlighted four immune cells, i.e., CD 8 T cells, neutrophils, follicular helper T (Tfh) cells, and Natural killer (NK) cells.ConclusionsThe PROMISE model can further stratify both LGG and GBM patients with distinct survival outcomes.These findings may help further our understanding of TME in gliomas and shed light on immunotherapies.

Comprehensive landscape of STEAP family functions and prognostic prediction value in glioblastoma.

Glioblastoma (GBM) is the most common, malignant, and deadly primary glioma. Six-transmembrane epithelial antigen of prostate (STEAP) family isinvolvedin tumorigenesis;here, we haveexploredthe biological function and the prognostic value of the STEAP family in GBM. Differentially expressed STEAP genes in tumor and normal samples were screened by usingThe Cancer Genome Atlas (TCGA) database. Univariate and multivariate Cox regression identified the prognosis-related genes: STEAP2 and STEAP3, which were involved in the regulation of immune response and cell cycle. Finally, a prognostic nomogram combining age, gender, chemotherapy, radiotherapy, IDH1 status, and the risk score model based on STEAP2 and STEAP3 was built and further validated in TCGA and Chinese Glioma Genome Atlas (CGGA) cohorts via concordance index and calibration plot, which suggested a favorable value for prognosis prediction. In conclusion, our results provided a comprehensive analysis of the STEAP family and a model for the prognosis prediction of GBM.

Prognostic value of miR-21 in gliomas: comprehensive study based on meta-analysis and TCGA dataset validation

Recent studies have highlighted the value of microRNA-21 (miR-21) as a prognostic biomarker in gliomas. However, the role of miR-21 in predicting prognosis remains controversial. We performed a comprehensive study based upon a meta-analysis and The Cancer Genome Atlas (TCGA) glioma dataset validation to clarify the prognostic significance of miR-21 in glioma patients. In this study, we searched Embase, PubMed, Web of science, CNKI, SinoMed, and Wanfang databases for records up to May 2018. Relevant data were extracted to assess the correlation between miR-21 expression and survival in glioma patients. Pooled hazard ratios (HRs) with 95% confidence intervals (CIs) were used to describe association strength. We further used multivariate Cox regression analysis to assess miR-21 expression in the TCGA glioma dataset to validate the relationship between miR-21 expression and survival. Nine studies were included in the meta-analysis. Among them, eight studies provided data on overall survival (OS) with a pooled HR of 1.91 (95% CI: 1.34, 2.73), indicating that higher expression of miR-21 was significantly associated with worse OS in glioma patients; for the other study, which provided data on progression-free survival (PFS), no statistically significant HR was reported for PFS in the glioma patients (HR = 1.23, 95% CI: 0.41, 3.72). A multivariate Cox regression analysis of the miR-21 expression in the TCGA glioma dataset revealed that overexpression of miR-21 was a potential independent prognostic biomarker of poorer OS (HR = 1.27, 95% CI: 1.01, 1.59) and poorer PFS (HR = 1.46, 95% CI: 1.17, 1.82). Our findings suggest that higher expression of miR-21 is correlated with poorer glioma prognosis.

Connective tissue growth factor as an unfavorable prognostic marker promotes the proliferation, migration, and invasion of gliomas.

BackgroundIn consideration of the difficulty in diagnosing high heterogeneous glioma, valuable prognostic markers are urgent to be investigated. This study aimed to verify that connective tissue growth factor (CTGF) is associated with the clinical prognosis of glioma, also to analyze the effect of CTGF on the biological function.MethodsIn this study, glioma and non-tumor tissue samples were obtained in 2012 to 2014 from the Department of Neurosurgery of Nanfang Hospital of Southern Medical University, Guangzhou, China. Based on messenger RNA (mRNA) data from the Cancer Genome Atlas (TCGA) and CCGA dataset, combined with related clinical information, we detected the expression of CTGF mRNA in glioma and assessed its effect on the prognosis of glioma patients. High expression of CTGF mRNA and protein in glioma were verified by reverse transcription-polymerase chain reaction, immunohistochemistry, and Western blotting. The role of CTGF in the proliferation, migration, and invasion of gliomas were respectively identified by methylthiazoletetrazolium assay, Transwell and Boyden assay in vitro. The effect on glioma cell circle was assessed by flow cytometry. For higher expression of CTGF in glioblastoma (GBM), the biological function of CTGF in GBM was investigated by gene ontology (GO) analysis.ResultsIn depth analysis of TCGA data revealed that CTGF mRNA was highly expressed in glioma (GBM, n = 163; lowly proliferative glioma [LGG], n = 518; non-tumor brain tissue, n = 207; LGG, t = 2.410, GBM, t = 2.364, P < 0.05). CTGF mRNA and protein expression in glioma (86%) was significantly higher than that in non-tumor tissues (18%) verified by collected samples. Glioma patients with higher expression of CTGF showed an obviously poorer overall survival (35.4 and 27.0 months compared to 63.3 and 55.1 months in TCGA and Chinese Glioma Genome Atlas (CGGA) databases separately, CGGA: χ2 = 7.596, P = 0.0059; TCGA: χ2 = 10.46, P = 0.0012). Inhibiting CTGF expression could significantly suppress the proliferation, migration, and invasion of gliomas. CTGF higher expression had been observed in GBM, and GO analysis demonstrated that the function of CTGF in GBM was mainly associated with metabolism and energy pathways (P < 0.001).ConclusionsCTGF is highly expressed in glioma, especially GBM, as an unfavorable and independent prognostic marker for glioma patients and facilitates the progress of glioma.

Expression of SREBP2 and cholesterol metabolism related genes in TCGA glioma cohorts.

Diffuse gliomas are the most common primary brain tumors. The Cancer Genome Atlas (TCGA) database provides correlative evidence between altered molecular pathways and gliomas. Dysregulated cholesterol homeostasis emerges as a potential indicator of the pathogenesis of gliomas.Mining large cohorts from the TCGA together with database from the Chinese Glioma Genome Atlas (CGGA) for confirmation, we compared gene expression of cholesterol synthesis master regulator SREBP2 and its regulatory networks in low grade glioma (LGG) and glioblastoma (GBM).Our analysis shows that expression of SREBP2 and related genes is lower in GBM than in LGG, indicating that cholesterol metabolism processes, including de novo synthesis, cholesterol uptakes, and cholesterol conversion and efflux, are suppressed in GBM.Overall, our data suggests that SREBP2 transcript could serve as a potential prognosis marker or therapeutic target in diffuse glioma including GBM.

Comparison of pathway and gene-level models for cancer prognosis prediction

BackgroundCancer prognosis prediction is valuable for patients and clinicians because it allows them to appropriately manage care. A promising direction for improving the performance and interpretation of expression-based predictive models involves the aggregation of gene-level data into biological pathways. While many studies have used pathway-level predictors for cancer survival analysis, a comprehensive comparison of pathway-level and gene-level prognostic models has not been performed. To address this gap, we characterized the performance of penalized Cox proportional hazard models built using either pathway- or gene-level predictors for the cancers profiled in The Cancer Genome Atlas (TCGA) and pathways from the Molecular Signatures Database (MSigDB).ResultsWhen analyzing TCGA data, we found that pathway-level models are more parsimonious, more robust, more computationally efficient and easier to interpret than gene-level models with similar predictive performance. For example, both pathway-level and gene-level models have an average Cox concordance index of ~ 0.85 for the TCGA glioma cohort, however, the gene-level model has twice as many predictors on average, the predictor composition is less stable across cross-validation folds and estimation takes 40 times as long as compared to the pathway-level model. When the complex correlation structure of the data is broken by permutation, the pathway-level model has greater predictive performance while still retaining superior interpretative power, robustness, parsimony and computational efficiency relative to the gene-level models. For example, the average concordance index of the pathway-level model increases to 0.88 while the gene-level model falls to 0.56 for the TCGA glioma cohort using survival times simulated from uncorrelated gene expression data.ConclusionThe results of this study show that when the correlations among gene expression values are low, pathway-level analyses can yield better predictive performance, greater interpretative power, more robust models and less computational cost relative to a gene-level model. When correlations among genes are high, a pathway-level analysis provides equivalent predictive power compared to a gene-level analysis while retaining the advantages of interpretability, robustness and computational efficiency.