Identification of prognostic gene signatures of glioblastoma: a study based on TCGA data analysis

A review on brain tumor segmentation based on deep learning methods with federated learning techniques

Brain tumors have become a severe medical complication in recent years due to their high fatality rate. Radiologists segment the tumor manually, which is time-consuming, error-prone, and expensive. In recent years, automated segmentation based on deep learning has demonstrated promising results in solving computer vision problems such as image classification and segmentation. Brain tumor segmentation has recently become a prevalent task in medical imaging to determine the tumor location, size, and shape using automated methods. Many researchers have worked on various machine and deep learning approaches to determine the most optimal solution using the convolutional methodology. In this review paper, we discuss the most effective segmentation techniques based on the datasets that are widely used and publicly available. We also proposed a survey of federated learning methodologies to enhance global segmentation performance and ensure privacy. A comprehensive literature review is suggested after studying more than 100 papers to generalize the most recent techniques in segmentation and multi-modality information. Finally, we concentrated on unsolved problems in brain tumor segmentation and a client-based federated model training strategy. Based on this review, future researchers will understand the optimal solution path to solve these issues.

RNA cytosine methyltransferase NSUN5 promotes protein synthesis and tumorigenic phenotypes in glioblastoma

Glioblastoma (GBM) is the most common and aggressive malignant primary brain tumor in adults. The standard treatment achieves a median overall survival for GBM patients of only 15 months. Hence, novel therapies based on an increased understanding of the mechanistic underpinnings of GBM are desperately needed. In this study, we show that elevated expression of 28S rRNA (cytosine-C(5))-methyltransferase NSUN5, which methylates cytosine 3782 of 28S rRNA in GBM cells, is strongly associated with the poor survival of GBM patients. Moreover, we demonstrate that overexpression of NSUN5 increases protein synthesis in GBM cells. NSUN5 knockdown decreased protein synthesis, cell proliferation, sphere formation, migration, and resistance to temozolomide in GBM cell lines. NSUN5 knockdown also decreased the number and size of GBM neurospheres in vitro. As a corollary, mice harboring U251 tumors wherein NSUN5 was knocked down survived longer than mice harboring control tumors. Taken together, our results suggest that NSUN5 plays a protumorigenic role in GBM by enabling the enhanced protein synthesis requisite for tumor progression. Accordingly, NSUN5 may be a hitherto unappreciated target for the treatment of GBM.

Development and validation a prognostic model based on natural killer T cells marker genes for predicting prognosis and characterizing immune status in glioblastoma through integrated analysis of single-cell and bulk RNA sequencing

BACKGROUND

Glioblastoma (GBM) is an aggressive and unstoppable malignancy. Natural killer T (NKT) cells, characterized by specific markers, play pivotal roles in many tumor-associated pathophysiological processes. Therefore, investigating the functions and complex interactions of NKT cells is great interest for exploring GBM.

METHODS

We acquired a single-cell RNA-sequencing (scRNA-seq) dataset of GBM from Gene Expression Omnibus (GEO) database. The weighted correlation network analysis (WGCNA) was employed to further screen genes subpopulations. Subsequently, we integrated the GBM cohorts from The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) databases to describe different subtypes by consensus clustering and developed a prognostic model by least absolute selection and shrinkage operator (LASSO) and multivariate Cox regression analysis. We further investigated differences in survival rates and clinical characteristics among different risk groups. Furthermore, a nomogram was developed by combining riskscore with the clinical characteristics. We investigated the abundance of immune cells in the tumor microenvironment (TME) by CIBERSORT and single sample gene set enrichment analysis (ssGSEA) algorithms. Immunotherapy efficacy assessment was done with the assistance of Tumor Immune Dysfunction and Exclusion (TIDE) and The Cancer Immunome Atlas (TCIA) databases. Real-time quantitative polymerase chain reaction (RT-qPCR) experiments and immunohistochemical profiles of tissues were utilized to validate model genes.

RESULTS

We identified 945 NKT cells marker genes from scRNA-seq data. Through further screening, 107 genes were accurately identified, of which 15 were significantly correlated with prognosis. We distinguished GBM samples into two distinct subtypes and successfully developed a robust prognostic prediction model. Survival analysis indicated that high expression of NKT cell marker genes was significantly associated with poor prognosis in GBM patients. Riskscore can be used as an independent prognostic factor. The nomogram was demonstrated remarkable utility in aiding clinical decision making. Tumor immune microenvironment analysis revealed significant differences of immune infiltration characteristics between different risk groups. In addition, the expression levels of immune checkpoint-associated genes were consistently elevated in the high-risk group, suggesting more prominent immune escape but also a stronger response to immune checkpoint inhibitors.

CONCLUSIONS

By integrating scRNA-seq and bulk RNA-seq data analysis, we successfully developed a prognostic prediction model that incorporates two pivotal NKT cells marker genes, namely, CD44 and TNFSF14. This model has exhibited outstanding performance in assessing the prognosis of GBM patients. Furthermore, we conducted a preliminary investigation into the immune microenvironment across various risk groups that contributes to uncover promising immunotherapeutic targets specific to GBM.

Gene co-expression network construction and analysis for identification of genetic biomarkers associated with glioblastoma multiforme using topological findings

BACKGROUND

Glioblastoma multiforme (GBM) is one of the most malignant types of central nervous system tumors. GBM patients usually have a poor prognosis. Identification of genes associated with the progression of the disease is essential to explain the mechanisms or improve the prognosis of GBM by catering to targeted therapy. It is crucial to develop a methodology for constructing a biological network and analyze it to identify potential biomarkers associated with disease progression.

METHODS

Gene expression datasets are obtained from TCGA data repository to carry out this study. A survival analysis is performed to identify survival associated genes of GBM patient. A gene co-expression network is constructed based on Pearson correlation between the gene's expressions. Various topological measures along with set operations from graph theory are applied to identify most influential genes linked with the progression of the GBM.

RESULTS

Ten key genes are identified as a potential biomarkers associated with GBM based on centrality measures applied to the disease network. These genes are SEMA3B, APS, SLC44A2, MARK2, PITPNM2, SFRP1, PRLH, DIP2C, CTSZ, and KRTAP4.2. Higher expression values of two genes, SLC44A2 and KRTAP4.2 are found to be associated with progression and lower expression values of seven gens SEMA3B, APS, MARK2, PITPNM2, SFRP1, PRLH, DIP2C, and CTSZ are linked with the progression of the GBM.

CONCLUSIONS

The proposed methodology employing a network topological approach to identify genetic biomarkers associated with cancer.

Favorable prognostic impact of phosphatase and tensin homolog alterations in wild-type isocitrate dehydrogenase and telomerase reverse transcriptase promoter glioblastoma

Background

Telomerase reverse transcriptase promoter (TERTp) mutations are a biological marker of glioblastoma; however, the prognostic significance of TERTp mutational status is controversial. We evaluated this impact by retrospectively analyzing the outcomes of patients with isocitrate dehydrogenase (IDH)- and TERTp-wild-type glioblastomas.

Methods

Using custom next-generation sequencing, we analyzed 208 glioblastoma samples harboring wild-type IDH.

Results

TERTp mutations were detected in 143 samples (68.8%). The remaining 65 (31.2%) were TERTp-wild-type. Among the TERTp-wild-type glioblastoma samples, we observed a significant difference in median progression-free survival (18.6 and 11.4 months, respectively) and overall survival (not reached and 15.7 months, respectively) in patients with and without phosphatase and tensin homolog (PTEN) loss and/or mutation. Patients with TERTp-wild-type glioblastomas with PTEN loss and/or mutation were younger and had higher Karnofsky Performance Status scores than those without PTEN loss and/or mutation. We divided the patients with TERTp-wild-type into 3 clusters using unsupervised hierarchical clustering: Good (PTEN and TP53 alterations; lack of CDKN2A/B homozygous deletion and platelet-derived growth factor receptor alpha (PDGFRA) alterations), intermediate (PTEN alterations, CDKN2A/B homozygous deletion, lack of PDGFRA, and TP53 alterations), and poor (PDGFRA and TP53 alterations, CDKN2A/B homozygous deletion, and lack of PTEN alterations) outcomes. Kaplan-Meier survival analysis indicated that these clusters significantly correlated with the overall survival of TERTp-wild-type glioblastoma patients.

Conclusions

Here, we report that PTEN loss and/or mutation is the most useful marker for predicting favorable outcomes in patients with IDH- and TERTp-wild-type glioblastomas. The combination of 4 genes, PTEN, TP53, CDKN2A/B, and PDGFRA, is important for the molecular classification and individual prognosis of patients with IDH- and TERTp-wild-type glioblastomas.

Deep multilayer brain omics identifies the potential involvement of menopause molecular networks in Gliomas' disease progression

The risk of high-grade gliomas is lower in young females, however, its incidence enhances after menopause, suggesting potential protective roles of female sex hormones. Hormone oscillations after menopause have received attention as a possible risk factor. Little is known about risk factors for adult gliomas. We examined the association of the aging brain after menopause, determining the risk of gliomas with proteomics and the MALDI-MSI experiment. Menopause caused low neurotransmitter levels such as GABA and ACH, high inflammatory factor levels like il-1β, and increased lipid metabolism-related levels like triglycerides in the brain. Upregulated and downregulated proteins after menopause were correlated with differentially expressed glioma genes, such as ACTA2, CAMK2D, FNBPIL, ARL1, HEBP1, CAST, CLIC1, LPCAT4, MAST3, and DOCK9. Furthermore, differential gene expression analysis of monocytes showed that the downregulated gene LPCAT4 could be used as a marker to prevent menopausal gliomas in women. Our findings regarding the association of menopause with the risk of gliomas are consistent with several extensive cohort studies. In view of the available evidence, postmenopausal status is likely to represent a significant risk factor for gliomas.

Improvements in Quality Control and Library Preparation for Targeted Sequencing Allowed Detection of Potentially Pathogenic Alterations in Circulating Cell-Free DNA Derived from Plasma of Brain Tumor Patients

Malignant gliomas are the most frequent primary brain tumors in adults. They are genetically heterogenous and invariably recur due to incomplete surgery and therapy resistance. Circulating tumor DNA (ctDNA) is a component of circulating cell-free DNA (ccfDNA) and represents genetic material that originates from the primary tumor or metastasis. Brain tumors are frequently located in the eloquent brain regions, which makes biopsy difficult or impossible due to severe postoperative complications. The analysis of ccfDNA from a patient's blood presents a plausible and noninvasive alternative. In this study, freshly frozen tumors and corresponding blood samples were collected from 84 brain tumor patients and analyzed by targeted next-generation sequencing (NGS). The cohort included 80 glioma patients, 2 metastatic cancer patients, and 2 primary CNS lymphoma (PCNSL) patients. We compared the pattern of genetic alterations in the tumor DNA (tDNA) with that of ccfDNA. The implemented technical improvements in quality control and library preparation allowed for the detection of ctDNA in 8 out of 84 patients, including 5 out of 80 glioma patients. In 32 out of 84 patients, we found potentially pathogenic genetic alterations in ccfDNA that were not detectable in tDNA. While sequencing ccfDNA from plasma has a low efficacy as a diagnostic tool for glioma patients, we concluded that further improvements in sample processing and library preparation can make liquid biopsy a valuable diagnostic tool for glioma patients.

RAD6 Positively Affects Tumorigenesis of Esophageal Squamous Cell Carcinoma by Regulating Histone Ubiquitination of CCNB1

Objective Esophageal carcinoma (ESCA) is deadly cancer worldwide with unknown etiology. This study aimed to investigate the impact and mechanism of RAD6 on the development of Esophageal squamous cell carcinoma (ESCC).

Expressions of RAD6A and RAD6B in ESCA were investigated from TCGA dataset and their expressions in tissue sample of ESCA patients and cells were determined. Functional experiments were conducted to explore the impact of RAD6A and RAD6B on malignant characteristics of several kinds of ESCC cells. Animal experiment was established and injected with RAD6A and RAD6B shRNA to evaluate the effect on tumor growth.

RAD6A and RAD6B were up-regulated in ESCC cells and tissues. Overexpressed RAD6A and RAD6B similarly increased ESCC cell proliferation, invasion and migration and silencing of RAD6 exerted opposite effects. Knockdown of RAD6A suppressed tumor growth and decreased the level of H2B, as data demonstrated positive correlation between RAD6A and CCNB1 in ESCC tissues.

Collectively, this study elucidates that RAD6 is up-regulated in ESCC and promotes the progression of ESCC through up-regulation of CCNB1 to enhance H2B ubiquitination. These evidence provide a novel insight into the pathogenesis of ESCC and might contribute to the development of targeted therapy.

A Novel 16-Genes Signature Scoring System as Prognostic Model to Evaluate Survival Risk in Patients with Glioblastoma

Previous studies have found that gene expression levels are associated with prognosis and some genes can be used to predict the survival risk of glioblastoma (GBM) patients. However, most of them just built the survival-related gene signature, and personal survival risk can be evaluated only in group. This study aimed to find the prognostic survival related genes of GBM, and construct survival risk prediction model, which can be used to evaluate survival risk by individual. We collected gene expression data and clinical information from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. Cox regression analysis and LASSO-cox regression analysis were performed to get survival-related genes and establish the overall survival prediction model. The ROC curve and Kaplan Meier analysis were used to evaluate the prediction ability of the model in training set and two independent cohorts. We also analyzed the biological functions of survival-related genes by GO and KEGG enrichment analysis. We identified 99 genes associated with overall survival and selected 16 genes (IGFBP2, GPRASP1, C1R, CHRM3, CLSTN2, NELL1, SEZ6L2, NMB, ICAM5, HPCAL4, SNAP91, PCSK1N, PGBD5, INA, UCHL1 and LHX6) to establish the survival risk prediction model. Multivariate Cox regression analysis indicted that the risk score could predict overall survival independent of age and gender. ROC analyses showed that our model was more robust than four existing signatures. The sixteen genes can also be potential transcriptional biomarkers and the model can assist doctors on clinical decision-making and personalized treatment of GBM patients.

Regulatory Network of PD1 Signaling Is Associated with Prognosis in Glioblastoma Multiforme

Glioblastoma is an aggressive cancer of the brain and spine. While analysis of glioblastoma 'omics data has somewhat improved our understanding of the disease, it has not led to direct improvement in patient survival. Cancer survival is often characterized by differences in gene expression, but the mechanisms that drive these differences are generally unknown. We therefore set out to model the regulatory mechanisms associated with glioblastoma survival. We inferred individual patient gene regulatory networks using data from two different expression platforms from The Cancer Genome Atlas. We performed comparative network analysis between patients with long- and short-term survival. Seven pathways were identified as associated with survival, all of them involved in immune signaling; differential regulation of PD1 signaling was validated to correspond with outcome in an independent dataset from the German Glioma Network. In this pathway, transcriptional repression of genes for which treatment options are available was lost in short-term survivors; this was independent of mutational burden and only weakly associated with T-cell infiltration. Collectively, these results provide a new way to stratify patients with glioblastoma that uses network features as biomarkers to predict survival. They also identify new potential therapeutic interventions, underscoring the value of analyzing gene regulatory networks in individual patients with cancer. SIGNIFICANCE: Genome-wide network modeling of individual glioblastomas identifies dysregulation of PD1 signaling in patients with poor prognosis, indicating this approach can be used to understand how gene regulation influences cancer progression.

Role of Lamin A/C as Candidate Biomarker of Aggressiveness and Tumorigenicity in Glioblastoma Multiforme

Nuclear lamina components have long been regarded as scaffolding proteins, forming a dense fibrillar structure necessary for the maintenance of the nucleus shape in all the animal kingdom. More recently, mutations, aberrant localisation and deregulation of these proteins have been linked to several diseases, including cancer. Using publicly available data we found that the increased expression levels of the nuclear protein Lamin A/C correlate with a reduced overall survival in The Cancer Genome Atlas Research Network (TCGA) patients affected by glioblastoma multiforme (GBM). We show that the expression of the LMNA gene is linked to the enrichment of cancer-related pathways, particularly pathways related to cell adhesion and cell migration. Mimicking the modulation of LMNA in a GBM preclinical cancer model, we confirmed both in vitro and in vivo that the increased expression of LMNA is associated with an increased aggressiveness and tumorigenicity. In addition, delving into the possible mechanism behind LMNA-induced GBM aggressiveness and tumorigenicity, we found that the mTORC2 component, Rictor, plays a central role in mediating these effects.

Pan-cancer evaluation of gene expression and somatic alteration data for cancer prognosis prediction

BACKGROUND

Over the past decades, approaches for diagnosing and treating cancer have seen significant improvement. However, the variability of patient and tumor characteristics has limited progress on methods for prognosis prediction. The development of high-throughput omics technologies now provides multiple approaches for characterizing tumors. Although a large number of published studies have focused on integration of multi-omics data and use of pathway-level models for cancer prognosis prediction, there still exists a gap of knowledge regarding the prognostic landscape across multi-omics data for multiple cancer types using both gene-level and pathway-level predictors.

METHODS

In this study, we systematically evaluated three often available types of omics data (gene expression, copy number variation and somatic point mutation) covering both DNA-level and RNA-level features. We evaluated the landscape of predictive performance of these three omics modalities for 33 cancer types in the TCGA using a Lasso or Group Lasso-penalized Cox model and either gene or pathway level predictors.

RESULTS

We constructed the prognostic landscape using three types of omics data for 33 cancer types on both the gene and pathway levels. Based on this landscape, we found that predictive performance is cancer type dependent and we also highlighted the cancer types and omics modalities that support the most accurate prognostic models. In general, models estimated on gene expression data provide the best predictive performance on either gene or pathway level and adding copy number variation or somatic point mutation data to gene expression data does not improve predictive performance, with some exceptional cohorts including low grade glioma and thyroid cancer. In general, pathway-level models have better interpretative performance, higher stability and smaller model size across multiple cancer types and omics data types relative to gene-level models.

CONCLUSIONS

Based on this landscape and comprehensively comparison, models estimated on gene expression data provide the best predictive performance on either gene or pathway level. Pathway-level models have better interpretative performance, higher stability and smaller model size relative to gene-level models.

Glioblastoma multiforme prognosis: MRI missing modality generation, segmentation and radiogenomic survival prediction

The accurate prognosis of glioblastoma multiforme (GBM) plays an essential role in planning correlated surgeries and treatments. The conventional models of survival prediction rely on radiomic features using magnetic resonance imaging (MRI). In this paper, we propose a radiogenomic overall survival (OS) prediction approach by incorporating gene expression data with radiomic features such as shape, geometry, and clinical information. We exploit TCGA (The Cancer Genomic Atlas) dataset and synthesize the missing MRI modalities using a fully convolutional network (FCN) in a conditional generative adversarial network (cGAN). Meanwhile, the same FCN architecture enables the tumor segmentation from the available and the synthesized MRI modalities. The proposed FCN architecture comprises octave convolution (OctConv) and a novel decoder, with skip connections in spatial and channel squeeze & excitation (skip-scSE) block. The OctConv can process low and high-frequency features individually and improve model efficiency by reducing channel-wise redundancy. Skip-scSE applies spatial and channel-wise excitation to signify the essential features and reduces the sparsity in deeper layers learning parameters using skip connections. The proposed approaches are evaluated by comparative experiments with state-of-the-art models in synthesis, segmentation, and overall survival (OS) prediction. We observe that adding missing MRI modality improves the segmentation prediction, and expression levels of gene markers have a high contribution in the GBM prognosis prediction, and fused radiogenomic features boost the OS estimation.

Role of circulating tumor cell detection in differentiating tumor recurrence from treatment necrosis of brain gliomas

Differentiating treatment necrosis from tumor recurrence poses a diagnostic conundrum for many clinicians in neuro-oncology. To investigate the potential role of circulating tumor cells (CTCs) detection in differentiating tumor recurrence and treatment necrosis in brain gliomas, we retrospectively analyzed the data of 22 consecutive patients with tumor totally removed and new enhancing mass lesion(s) showed on MRI after initial radiotherapy. The 22 patients were finally classified into tumor recurrence group (n = 10) and treatment necrosis group (n = 12), according to evidence from the clinical course (n = 11) and histological confirmation (n = 11). All 22 patients received CTCs detection, and DSC-MRP and 11C-MET-PET were performed on 20 patients (90.9%) and 17patients (77.3%) respectively. The data of the diagnosis efficacy to differentiate the two lesions by CTC detection, MPR and PET were analyzed by ROC analysis. The mean CTCs counts were significantly higher in the tumor recurrence group (6.10 ± 3.28) compared to the treatment necrosis group (1.08 ± 2.54, p < 0.001). The ROC curve showed that an optimized cell count threshold of 2 had 100% sensitivity and 91.2% specificity with AUC = 0.933 to declare tumor recurrence. The diagnostic efficacy of CTC detection was superior to rCBV of DSC-MRP and rSUV_max in MET-PET. Furthermore, we observed that CTCs detection could have a potential role in predicting tumor recurrence in one patient. Our research results preliminarily showed the potential value of CTC detection in differentiating treatment necrosis from tumor recurrence in brain gliomas, and is worthy of further confirmation with large samples involved.

Development of a gene expression-based prognostic signature for IDH wild-type glioblastoma

BACKGROUND

We aimed to develop a gene expression-based prognostic signature for isocitrate dehydrogenase (IDH) wild-type glioblastoma using clinical trial datasets representative of glioblastoma clinical trial populations.

METHODS

Samples were collected from newly diagnosed patients with IDH wild-type glioblastoma in the ARTE, TAMIGA, EORTC 26101 (referred to as "ATE"), AVAglio, and GLARIUS trials, or treated at UCLA. Transcriptional profiling was achieved with the NanoString gene expression platform. To identify genes prognostic for overall survival (OS), we built an elastic net penalized Cox proportional hazards regression model using the discovery ATE dataset. For validation in independent datasets (AVAglio, GLARIUS, UCLA), we combined elastic net-selected genes into a robust z-score signature (ATE score) to overcome gene expression platform differences between discovery and validation cohorts.

RESULTS

NanoString data were available from 512 patients in the ATE dataset. Elastic net identified a prognostic signature of 9 genes (CHEK1, GPR17, IGF2BP3, MGMT, MTHFD1L, PTRH2, SOX11, S100A9, and TFRC). Translating weighted elastic net scores to the ATE score conserved the prognostic value of the genes. The ATE score was prognostic for OS in the ATE dataset (P < 0.0001), as expected, and in the validation cohorts (AVAglio, P < 0.0001; GLARIUS, P = 0.02; UCLA, P = 0.004). The ATE score remained prognostic following adjustment for O6-methylguanine-DNA methyltransferase (MGMT) promoter methylation status and corticosteroid use at baseline. A positive correlation between ATE score and proneural/proliferative subtypes was observed in patients with MGMT non-methylated promoter status.

CONCLUSIONS

The ATE score showed prognostic value and may enable clinical trial stratification for IDH wild-type glioblastoma.

PI3Kγ inhibition suppresses microglia/TAM accumulation in glioblastoma microenvironment to promote exceptional temozolomide response

Precision medicine in oncology leverages clinical observations of exceptional response. Toward an understanding of the molecular features that define this response, we applied an integrated, multiplatform analysis of RNA profiles derived from clinically annotated glioblastoma samples. This analysis suggested that specimens from exceptional responders are characterized by decreased accumulation of microglia/macrophages in the glioblastoma microenvironment. Glioblastoma-associated microglia/macrophages secreted interleukin 11 (IL11) to activate STAT3-MYC signaling in glioblastoma cells. This signaling induced stem cell states that confer enhanced tumorigenicity and resistance to the standard-of-care chemotherapy, temozolomide (TMZ). Targeting a myeloid cell restricted an isoform of phosphoinositide-3-kinase, phosphoinositide-3-kinase gamma isoform (PI3Kγ), by pharmacologic inhibition or genetic inactivation disrupted this signaling axis by reducing microglia/macrophage-associated IL11 secretion in the tumor microenvironment. Mirroring the clinical outcomes of exceptional responders, PI3Kγ inhibition synergistically enhanced the anti-neoplastic effects of TMZ in orthotopic murine glioblastoma models. Moreover, inhibition or genetic inactivation of PI3Kγ in murine glioblastoma models recapitulated expression profiles observed in clinical specimens isolated from exceptional responders. Our results suggest key contributions from tumor-associated microglia/macrophages in exceptional responses and highlight the translational potential for PI3Kγ inhibition as a glioblastoma therapy.

A genetic risk score for glioblastoma multiforme based on copy number variations

Glioblastoma multiforme is the most common form of brain cancer. Several lines of evidence suggest that glioblastoma multiforme has a genetic basis. A genetic test that could identify people who are at high risk of developing glioblastoma multiforme could improve our understanding of this form of brain cancer. Using the Cancer Genome Atlas (TCGA) dataset, we found common germ line DNA copy number variations in the TCGA population. We tested whether different sets of these germ line DNA copy number variations could effectively distinguish patients with glioblastoma multiforme from others in the TCGA dataset. We used a gradient boosting machine, a machine learning classification algorithm, to classify TCGA patients solely based on a set of germline DNA copy number variations. We found that this machine learning algorithm could classify TCGA glioblastoma multiforme patients from the other TCGA patients with an area under the curve (AUC) of the receiver operating characteristic curve (AUC=0.875). Grouped into quintiles, the highest ranked quintile by the machine learning algorithm had an odds ratio of 3.78 (95% CI 3.25-4.40) higher than the average odds ratio and about 40 (95% CI 20-70) times higher than the lowest quintile. The identification of an effective germ line genetic test to stratify risk of developing glioblastoma multiforme should lead to a better understanding of how this cancer forms. This result might ultimately lead to better treatments of glioblastoma multiforme.

SCG3 Protein Expression in Glioma Associates With less Malignancy and Favorable Clinical Outcomes

Introduction: Secretogranin III (SCG3) physiologically participates in neurotransmitter storage/transport and is widely expressed in neuroendocrine tumors. However, there is no report on SCG3 protein expression in gliomas. Methods: The method of immunohistochemical staining on a glioma tissue microarray was utilized to detect SCG3 protein expression and investigate the correlations of its expression with clinicopathological and genetic features in gliomas. The RNA-seq data of SCG3 in The Cancer Genome Atlas database was exploited to explore these correlations at the transcriptional level. Results: There were 57.5% (130/226) glioma cases having SCG3 cytoplasmic staining in the tissue microarray. SCG3 expression inversely correlated with malignancy grade at both transcriptional and protein levels. The highest level was observed in oligodendroglial tumors, especially in oligodendrogliomas (ODs) with IDH-mutation/1p19q-codeletion. The lowest SCG3 expression was observed in glioblastomas (GBMs), especially in the mesenchymal subtype. Nearly a half of GBM cases (44.4%, 64/144) had any discernible SCG3 staining, and were defined as SCG3-positive by the microarray study. SCG3-positive GBM cases exhibited improved overall survival as compared with the SCG3-negative cases (29.3 vs. 14.5 months; Hazard ratio, 0.364; 95% CI, 0.216-0.612; p < 0.001). A multivariate Cox regression analysis also revealed SCG3 positivity as an independent favorable prognosticator in GBM patients. Conclusion: SCG3 protein expression inversely correlates with glioma malignancy and predicts favorable outcomes in GBM patients.

Molecular Mechanisms of Treatment Resistance in Glioblastoma

Glioblastoma is the most common malignant primary brain tumor in adults and is almost invariably fatal. Despite our growing understanding of the various mechanisms underlying treatment failure, the standard-of-care therapy has not changed over the last two decades, signifying a great unmet need. The challenges of treating glioblastoma are many and include inadequate drug or agent delivery across the blood-brain barrier, abundant intra- and intertumoral heterogeneity, redundant signaling pathways, and an immunosuppressive microenvironment. Here, we review the innate and adaptive molecular mechanisms underlying glioblastoma's treatment resistance, emphasizing the intrinsic challenges therapeutic interventions must overcome-namely, the blood-brain barrier, tumoral heterogeneity, and microenvironment-and the mechanisms of resistance to conventional treatments, targeted therapy, and immunotherapy.

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 is involved in tumorigenesis; here, we have explored the biological function and the prognostic value of the STEAP family in GBM. Differentially expressed STEAP genes in tumor and normal samples were screened by using The 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.

Computational identification and characterization of glioma candidate biomarkers through multi-omics integrative profiling

BACKGROUND

Glioma is one of the most common malignant brain tumors and exhibits low resection rate and high recurrence risk. Although a large number of glioma studies powered by high-throughput sequencing technologies have led to massive multi-omics datasets, there lacks of comprehensive integration of glioma datasets for uncovering candidate biomarker genes.

RESULTS

In this study, we collected a large-scale assemble of multi-omics multi-cohort datasets from worldwide public resources, involving a total of 16,939 samples across 19 independent studies. Through comprehensive molecular profiling across different datasets, we revealed that PRKCG (Protein Kinase C Gamma), a brain-specific gene detectable in cerebrospinal fluid, is closely associated with glioma. Specifically, it presents lower expression and higher methylation in glioma samples compared with normal samples. PRKCG expression/methylation change from high to low is indicative of glioma progression from low-grade to high-grade and high RNA expression is suggestive of good survival. Importantly, PRKCG in combination with MGMT is effective to predict survival outcomes in a more precise manner.

CONCLUSIONS

PRKCG bears the great potential for glioma diagnosis, prognosis and therapy, and PRKCG-like genes may represent a set of important genes associated with different molecular mechanisms in glioma tumorigenesis. Our study indicates the importance of computational integrative multi-omics data analysis and represents a data-driven scheme toward precision tumor subtyping and accurate personalized healthcare.

Prognostic Value of C-Reactive Protein to Albumin Ratio in Glioblastoma Multiforme Patients Treated with Concurrent Radiotherapy and Temozolomide

Objective

We investigated the prognostic impact of C-reactive protein to albumin ratio (CRP/Alb) on the survival outcomes of newly diagnosed glioblastoma multiforme (GBM) patients treated with radiotherapy (RT) and concurrent plus adjuvant temozolomide (TMZ).

Methods

The pretreatment CRP and Alb records of GBM patients who underwent RT and concurrent plus adjuvant TMZ were retrospectively analyzed. The CRP/Alb was calculated by dividing serum CRP level by serum Alb level obtained prior to RT. The availability of significant cutoff value for CRP/Alb that interacts with survival was assessed with the receiver-operating characteristic (ROC) curve analysis. The primary endpoint was the association between the CRP/Alb and the overall survival (OS).

Results

A total of 153 patients were analyzed. At a median follow-up of 14.7 months, median and 5-year OS rates were 16.2 months (95% CI: 12.5–19.7) and 9.5%, respectively, for the entire cohort. The ROC curve analysis identified a significant cutoff value at 0.75 point (area under the curve: 74.9%; sensitivity: 70.9%; specificity: 67.7%; P < 0.001) for CRP/Alb that interacts with OS and grouped the patients into two: CRP/Alb <0.75 (n = 61) and ≥0.75 (n = 92), respectively. Survival comparisons revealed that the CRP/Alb <0.75 was associated with a significantly superior median (22.5 versus 15.7 months; P < 0.001) and 5-year (20% versus 0%) rates than the CRP/Alb ≥0.75, which retained its independent significance in multivariate analysis (P < 0.001).

Conclusion

Present results suggested the pretreatment CRP/Alb as a significant and independent inflammation-based index which can be utilized for further prognostic lamination of GBM patients.

Radiogenomics model for overall survival prediction of glioblastoma

Glioblastoma multiforme (GBM) is a very aggressive and infiltrative brain tumor with a high mortality rate. There are radiomic models with handcrafted features to estimate glioblastoma prognosis. In this work, we evaluate to what extent of combining genomic with radiomic features makes an impact on the prognosis of overall survival (OS) in patients with GBM. We apply a hypercolumn-based convolutional network to segment tumor regions from magnetic resonance images (MRI), extract radiomic features (geometric, shape, histogram), and fuse with gene expression profiling data to predict survival rate for each patient. Several state-of-the-art regression models such as linear regression, support vector machine, and neural network are exploited to conduct prognosis analysis. The Cancer Genome Atlas (TCGA) dataset of MRI and gene expression profiling is used in the study to observe the model performance in radiomic, genomic, and radiogenomic features. The results demonstrate that genomic data are correlated with the GBM OS prediction, and the radiogenomic model outperforms both radiomic and genomic models. We further illustrate the most significant genes, such as IL1B, KLHL4, ATP1A2, IQGAP2, and TMSL8, which contribute highly to prognosis analysis. Graphical Abstract Our Proposed fully automated "Radiogenomic"" approach for survival prediction overview. It fuses geometric, intensity, volumetric, genomic and clinical information to predict OS.

Next Generation Sequencing and Machine Learning Technologies Are Painting the Epigenetic Portrait of Glioblastoma

Even with a rare occurrence of only 1.35% of cancer cases in the United States of America, brain tumors are considered as one of the most lethal malignancies. The most aggressive and invasive type of brain tumor, glioblastoma, accounts for 60–70% of all gliomas and presents with life expectancy of only 12–18 months. Despite trimodal treatment and advances in diagnostic and therapeutic methods, there are no significant changes in patient outcome. Our understanding of glioblastoma was significantly improved with the introduction of next generation sequencing technologies. This led to the identification of different genetic and molecular subtypes, which greatly improve glioblastoma diagnosis. Still, because of the poor life expectancy, novel diagnostic, and treatment methods are broadly explored. Epigenetic modifications like methylation and changes in histone acetylation are such examples. Recently, in addition to genetic and molecular characteristics, epigenetic profiling of glioblastomas is also used for sample classification. Further advancement of next generation sequencing technologies is expected to identify in detail the epigenetic signature of glioblastoma that can open up new therapeutic opportunities for glioblastoma patients. This should be complemented with the use of computational power i.e., machine and deep learning algorithms for objective diagnostics and design of individualized therapies. Using a combination of phenotypic, genotypic, and epigenetic parameters in glioblastoma diagnostics will bring us closer to precision medicine where therapies will be tailored to suit the genetic profile and epigenetic signature of the tumor, which will grant longer life expectancy and better quality of life. Still, a number of obstacles including potential bias, availability of data for minorities in heterogeneous populations, data protection, and validation and independent testing of the learning algorithms have to be overcome on the way.

DeepSurvNet: deep survival convolutional network for brain cancer survival rate classification based on histopathological images

Histopathological whole slide images of haematoxylin and eosin (H&E)-stained biopsies contain valuable information with relation to cancer disease and its clinical outcomes. Still, there are no highly accurate automated methods to correlate histolopathological images with brain cancer patients' survival, which can help in scheduling patients therapeutic treatment and allocate time for preclinical studies to guide personalized treatments. We now propose a new classifier, namely, DeepSurvNet powered by deep convolutional neural networks, to accurately classify in 4 classes brain cancer patients' survival rate based on histopathological images (class I, 0-6 months; class II, 6-12 months; class III, 12-24 months; and class IV, >24 months survival after diagnosis). After training and testing of DeepSurvNet model on a public brain cancer dataset, The Cancer Genome Atlas, we have generalized it using independent testing on unseen samples. Using DeepSurvNet, we obtained precisions of 0.99 and 0.8 in the testing phases on the mentioned datasets, respectively, which shows DeepSurvNet is a reliable classifier for brain cancer patients' survival rate classification based on histopathological images. Finally, analysis of the frequency of mutations revealed differences in terms of frequency and type of genes associated to each class, supporting the idea of a different genetic fingerprint associated to patient survival. We conclude that DeepSurvNet constitutes a new artificial intelligence tool to assess the survival rate in brain cancer. Graphical abstract A DCNN model was generated to accurately predict survival rates of brain cancer patients (classified in 4 different classes) accurately. After training the model using images from H&E stained tissue biopsies from The Cancer Genome Atlas database (TCGA, left), the model can predict for each patient, based on a histological image (top right), its survival class accurately (bottom right).

Secreted Phosphoprotein 1 (SPP1) and Fibronectin 1 (FN1) Are Associated with Progression and Prognosis of Esophageal Cancer as Identified by Integrated Expression Profiles Analysis

Background

Esophageal cancer is a malignant tumor with a complex pathogenesis and a poor 5-year survival rate, which encourages researchers to explore its molecular mechanisms deeper to improve the prognosis.

Material/Methods

DEGs were from 4 Gene Expression Omnibus (GEO) databases (GSE92396, GSE20347, GSE23400, and GSE45168) including 87 esophageal tumor samples and 84 normal samples. We performed Gene Ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, Protein-Protein interaction (PPI) analysis, and GeneMANIA to identify the DEGs. Gene set enrichment analysis (GSEA) and Kaplan-Meier survival analyses were performed.

Results

There was an overlapping subset consisting of 120 DEGs that was present in all esophageal tumor samples. The DEGs were enriched in extracellular matrix (ECM)-receptor interaction, as well as focal adhesion and transcriptional mis-regulation in cancer. The 2 most crucial regulatory pathways in esophageal cancer were the amebiasis pathway and the PI3K-Akt signaling pathway. Secreted phosphoprotein 1 (SPP1) and fibronectin 1 (FN1) were selected and verified in an independent cohort and samples using the TCGA and GTEx projects. Gene set enrichment analysis (GSEA) showed that proteasome and nucleotide excision repair were 2 most differentially enriched pathways in the SPP1 high-expression phenotype, and ECM-receptor interaction and focal adhesion in FN1 high-expression phenotype. Kaplan-Meier survival analysis showed that SPP1 and FN1 were significantly positively related to overall survival and had the potential to predict patient relapse.

Conclusions

Our analysis is the first to show that SPP1 and FN1 might work as biological markers of progression and prognosis in esophageal carcinoma (ESCA).

Expression and function of chondroitin 4-sulfate and chondroitin 6-sulfate in human glioma

Key molecules promoting migration and invasion exist in the extracellular matrix, and include chondroitin 4-sulfate (C4S) and chondroitin 6-sulfate (C6S), functionally important carbohydrate chains of chondroitin sulfate proteoglycans that participate in regulating cancer development. Here, we show that C4S and C6S expression is upregulated in human glioma tissues, when compared to normal brain tissue, and that the extent of upregulation positively correlated with glioma malignancy. Treatment of cultured glioma cells with C4S and C6S enhanced cell viability, migration, and invasion, increased MMP-2 and MMP-9 levels, enhanced N-cadherin, but reduced E-cadherin expression. Inhibition of expression of the two CS synthetic enzymes chondroitin 4-O-sulfotransferase-1 (C4ST-1/CHST11) and chondroitin 6-O-sulfotransferase-1 (C6ST-1/CHST3) suppressed cell viability, migration and invasion, reduced MMP-2 and MMP-9 expression, and reduced N-cadherin expression, but increased E-cadherin levels. The C4S- and C6S-enhanced epithelial-to-mesenchymal transition and expression of MMP-2 occurred via activation of the PI3K/AKT signaling pathway, known to be involved in promoting cell migration and invasion. In immune-deficient larval zebrafish, C4S and C6S increased the numbers of viable tumor cells, thereby promoting glioma cell proliferation. The present observations point to a novel role of C4S and C6S in human glioma cell functions, thus possibly representing targets in glioma therapy.

NFкB is a critical transcriptional regulator of atypical cadherin FAT1 in glioma

Background

Overexpression of FAT1 gene and its oncogenic effects have been reported in several cancers. Previously, we have documented upregulation of FAT1 gene in glioblastoma (GBM) tumors which was found to increase the expression of proinflammatory markers, HIF-1α, stemness genes and EMT markers in glioma cells. Here, we reveal NFкB (RelA)/RelA/p65 as the transcriptional regulator of FAT1 gene in GBM cells.

Methods

In-silico analysis of FAT1 gene promoter was performed using online bioinformatics tool Promo alggen (Transfac 8.3) to identify putative transcription factor(s) binding motifs. A 4.0 kb FAT1 promoter (− 3220 bp to + 848 bp w.r.t. TSS + 1) was cloned into promoter less pGL3Basic reporter vector. Characterization of FAT1 promoter for transcriptional regulation was performed by in-vitro functional assays using promoter deletion constructs, site directed mutagenesis and ChIP in GBM cells.

Results

Expression levels of NFкB (RelA) and FAT1 were found to be increased and positively correlated in GBM tumors (n = 16), REMBRANDT GBM-database (n = 214) and TCGA GBM-database (n = 153). In addition to glioma, positive correlation between NFкB (RelA) and FAT1 expression was also observed in other tumors like pancreatic, hepatocellular, lung and stomach cancers (data extracted from TCGA tumor data). A 4.0 kb FAT1-promoter-construct [− 3220 bp/+ 848 bp, transcription start site (TSS) + 1, having 17 NFкB (RelA) motifs] showed high FAT1 promoter luciferase-activity in GBM cells (U87MG/A172/U373MG). FAT1 promoter deletion-construct pGL3F1 [− 200 bp/+ 848 bp, with 3-NFкB (RelA)-motifs] showed the highest promoter activity. Exposure of GBM cells to known NFкB (RelA)-activators [severe-hypoxia/TNF-α/ectopic-NFкB (RelA) + IKBK vectors] led to increased pGL3F1-promoter activity and increased endogenous-FAT1 expression. Conversely, siRNA-mediated NFкB (RelA) knockdown led to decreased pGL3F1-promoter activity and decreased endogenous-FAT1 expression. Deletion of NFкB (RelA)-motif at − 90 bp/− 80 bp [pGL3F1δ1-construct] showed significant decrease in promoter activity. Site directed mutagenesis at -90 bp/− 80 bp and ChIP assay for endogenous-NFкB (RelA) confirmed the importance of this motif in FAT1 expression regulation. Significant reduction in the migration, invasion as well as colony forming capacity of the U87MG glioma cells was observed on siRNA-mediated knockdown of NFкB (RelA).

Conclusion

Since FAT1 and NFкB (RelA) are independently known to promote pro-tumorigenic inflammation and upregulate the expression of HIF-1α/EMT/stemness in tumors, targeting the NFкB (RelA)-FAT1 axis may attenuate an important tumor-promoting pathway in GBM. This may also be applicable to other tumors.

Transcriptome profiling-based identification of prognostic subtypes and multi-omics signatures of glioblastoma

Glioblastoma (GBM) is a lethal tumor, but few biomarkers and molecular subtypes predicting prognosis are available. This study was aimed to identify prognostic subtypes and multi-omics signatures for GBM. Using oncopression and TCGA-GBM datasets, we identified 80 genes most associated with GBM prognosis using correlations between gene expression levels and overall survival of patients. The prognostic score of each sample was calculated using these genes, followed by assigning three prognostic subtypes. This classification was validated in two independent datasets (REMBRANDT and Severance). Functional annotation revealed that invasion- and cell cycle-related gene sets were enriched in poor and favorable group, respectively. The three GBM subtypes were therefore named invasive (poor), mitotic (favorable), and intermediate. Interestingly, invasive subtype showed increased invasiveness, and MGMT methylation was enriched in mitotic subtype, indicating need for different therapeutic strategies according to prognostic subtypes. For clinical convenience, we also identified genes that best distinguished the invasive and mitotic subtypes. Immunohistochemical staining showed that markedly higher expression of PDPN in invasive subtype and of TMEM100 in mitotic subtype (P < 0.001). We expect that this transcriptome-based classification, with multi-omics signatures and biomarkers, can improve molecular understanding of GBM, ultimately leading to precise stratification of patients for therapeutic interventions.

Genetic and Epigenetic Features of Rapidly Progressing IDH-Mutant Astrocytomas

IDH-mutant astrocytomas are significantly less aggressive than their IDH-wildtype counterparts. We analyzed The Cancer Genome Atlas dataset (TCGA) and identified a small group of IDH-mutant, WHO grade II-III astrocytomas (n = 14) with an unexpectedly poor prognosis characterized by a rapid progression to glioblastoma and death within 3 years of the initial diagnosis. Compared with IDH-mutant tumors with the typical, extended progression-free survival in a control group of age-similar patients, the tumors in the rapidly progressing group were characterized by a markedly increased level of overall copy number alterations ([CNA]; p = 0.006). In contrast, the mutation load was similar, as was the methylation pattern, being consistent with IDH-mutant astrocytoma. Two of the gliomas (14%) in the rapidly progressing, IDH-mutant group but none of the other grade II-III gliomas in the TCGA (n = 283) had pathogenic mutations in genes (FANCB and APC) associated with maintaining chromosomal stability. These results suggest that chromosomal instability can negate the beneficial effect of IDH mutations in WHO II-III astrocytomas. The mechanism of the increased CNA is unknown but in some cases appears to be due to mutations in genes with a role in chromosomal stability. Increased CNA could serve as a biomarker for tumors at risk for rapid progression.

TACCO, a Database Connecting Transcriptome Alterations, Pathway Alterations and Clinical Outcomes in Cancers

Because of innumerable cancer sequencing projects, abundant transcriptome expression profiles together with survival data are available from the same patients. Although some expression signatures for prognosis or pathologic staging have been identified from these data, systematically discovering such kind of expression signatures remains a challenge. To address this, we developed TACCO (Transcriptome Alterations in CanCer Omnibus), a database for identifying differentially expressed genes and altered pathways in cancer. TACCO also reveals miRNA cooperative regulations and supports construction of models for prognosis. The resulting signatures have great potential for patient stratification and treatment decision-making in future clinical applications. TACCO is freely available at http://tacco.life.nctu.edu.tw/.

Prognostic Gene Discovery in Glioblastoma Patients using Deep Learning

This study aims to discover genes with prognostic potential for glioblastoma (GBM) patients’ survival in a patient group that has gone through standard of care treatments including surgeries and chemotherapies, using tumor gene expression at initial diagnosis before treatment. The Cancer Genome Atlas (TCGA) GBM gene expression data are used as inputs to build a deep multilayer perceptron network to predict patient survival risk using partial likelihood as loss function. Genes that are important to the model are identified by the input permutation method. Univariate and multivariate Cox survival models are used to assess the predictive value of deep learned features in addition to clinical, mutation, and methylation factors. The prediction performance of the deep learning method was compared to other machine learning methods including the ridge, adaptive Lasso, and elastic net Cox regression models. Twenty-seven deep-learned features are extracted through deep learning to predict overall survival. The top 10 ranked genes with the highest impact on these features are related to glioblastoma stem cells, stem cell niche environment, and treatment resistance mechanisms, including POSTN, TNR, BCAN, GAD1, TMSB15B, SCG3, PLA2G2A, NNMT, CHI3L1 and ELAVL4.

Genome-wide expression profiling of glioblastoma using a large combined cohort

Glioblastomas (GBMs), are the most common intrinsic brain tumors in adults and are almost universally fatal. Despite the progresses made in surgery, chemotherapy, and radiation over the past decades, the prognosis of patients with GBM remained poor and the average survival time of patients suffering from GBM was still short. Discovering robust gene signatures toward better understanding of the complex molecular mechanisms leading to GBM is an important prerequisite to the identification of novel and more effective therapeutic strategies. Herein, a comprehensive study of genome-scale mRNA expression data by combining GBM and normal tissue samples from 48 studies was performed. The 147 robust gene signatures were identified to be significantly differential expression between GBM and normal samples, among which 100 (68%) genes were reported to be closely associated with GBM in previous publications. Moreover, function annotation analysis based on these 147 robust DEGs showed certain deregulated gene expression programs (e.g., cell cycle, immune response and p53 signaling pathway) were associated with GBM development, and PPI network analysis revealed three novel hub genes (RFC4, ZWINT and TYMS) play important role in GBM development. Furthermore, survival analysis based on the TCGA GBM data demonstrated 38 robust DEGs significantly affect the prognosis of GBM in OS (p < 0.05). These findings provided new insights into molecular mechanisms underlying GBM and suggested the 38 robust DEGs could be potential targets for the diagnosis and treatment.

Hypoxia-inducible factor 2α: a novel target in gliomas

Hypoxia is an important contributor to aggressive behavior and resistance mechanisms in glioblastoma. Upregulation of hypoxia inducible transcription factors (HIFs) is the primary adaptive cellular response to a hypoxic environment. While HIF1α has been widely studied in cancer, HIF2α offers a potentially more specific and appealing target in glioblastoma given expression in glioma stem cells and not normal neural progenitors, activation in states of chronic hypoxia and expression that correlates with glioma patient survival. A first-in-class HIF2α inhibitor, PT2385, is in clinical trials for renal cell carcinoma, and provides the first opportunity to therapeutically target this important pathway in glioma biology.

GPR56/ADGRG1 Inhibits Mesenchymal Differentiation and Radioresistance in Glioblastoma

A mesenchymal transition occurs both during the natural evolution of glioblastoma (GBM) and in response to therapy. Here, we report that the adhesion G-protein-coupled receptor, GPR56/ADGRG1, inhibits GBM mesenchymal differentiation and radioresistance. GPR56 is enriched in proneural and classical GBMs and is lost during their transition toward a mesenchymal subtype. GPR56 loss of function promotes mesenchymal differentiation and radioresistance of glioma initiating cells both in vitro and in vivo. Accordingly, a low GPR56-associated signature is prognostic of a poor outcome in GBM patients even within non-G-CIMP GBMs. Mechanistically, we reveal GPR56 as an inhibitor of the nuclear factor kappa B (NF-κB) signaling pathway, thereby providing the rationale by which this receptor prevents mesenchymal differentiation and radioresistance. A pan-cancer analysis suggests that GPR56 might be an inhibitor of the mesenchymal transition across multiple tumor types beyond GBM.

A comprehensive analysis of prognosis prediction models based on pathway‑level, gene‑level and clinical information for glioblastoma

The present study aimed to develop a pathway-based prognosis prediction model for glioblastoma (GBM). Univariate and multivariate Cox regression analysis were used to identify prognosis-related genes and clinical factors using mRNA-seq data of GBM samples from The Cancer Genome Atlas (TCGA) database. The expression matrix of prognosis-related genes was transformed into pathway deregulation score (PDS) based on the Gene Set Enrichment Analysis (GSEA) repository using Pathifier software. With PDS scores as input, L1-penalized estimation-based Cox-proportional hazards (PH) model was used to identify prognostic pathways. Consequently, a prognosis prediction model based on these prognostic pathways was constructed for classifying patients in the TCGA set or each of the three validation sets into two risk groups. The survival difference between these risk groups was then analyzed using Kaplan-Meier survival analysis and log-rank test. In addition, a gene-based prognostic model was constructed using the Cox-PH model. The model of prognostic pathway combined with clinical factors was also evaluated. In total, 148 genes were discovered to be associated with prognosis. The Cox-PH model identified 13 prognostic pathways. Subsequently, a prognostic model based on the 13 pathways was constructed, and was demonstrated to successfully differentiate overall survival in the TCGA set and in three independent sets. However, the gene-based prognosis model was validated in only two of the three independent sets. Furthermore, the pathway+clinic factor-based model exhibited better predictive results compared with the pathway-based model. In conclusion, the present study suggests a promising prognosis prediction model of 13 pathways for GBM, which may be superior to the gene-level information-based prognostic model.

The Pluripotent Stem-Cell Marker Alkaline Phosphatase is Highly Expressed in Refractory Glioblastoma with DNA Hypomethylation

Background

Hypomethylation of genomic DNA induces stem-cell properties in cancer cells and contributes to the treatment resistance of various malignancies.

Objective

To examine the correlation between the methylation status of stem-cell-related genes and the treatment outcomes in patients with glioblastoma (GBM).

Methods

The genome-wide DNA methylation status was determined using HumanMethylation450 BeadChips, and the methylation status was compared between a group of patients with good prognosis (survival > 4 yr) and a group with poor prognosis (survival < 1 yr). Immunohistochemistry for proteins translated from hypomethylated genes, including alkaline phosphatase (ALPL), CD133, and CD44, was performed in 70 GBMs and 60 oligodendroglial tumors.

Results

The genomic DNA in refractory GBM was more hypomethylated than in GBM from patients with relatively long survival (P = .0111). Stem-cell-related genes including ALPL, CD133, and CD44 were also significantly hypomethylated. A validation study using immunohistochemistry showed that DNA hypomethylation was strongly correlated with high protein expression of ALPL, CD133, and CD44. GBM patients with short survival showed high expression of these stem-cell markers. Multivariate analysis confirmed that co-expression of ALPL + CD133 or ALPL + CD44 was a strong predictor of short survival. Anaplastic oligodendroglial tumors without isocitrate dehydrogenase 1 mutation were significantly correlated with high ALPL expression and poor survival.

Conclusion

Accumulation of stem-cell properties due to aberrant DNA hypomethylation is associated with the refractory nature of GBM.

Circulating tumor cell is a common property of brain glioma and promotes the monitoring system

Brain glioma is the most common primary intracranial tumor characterized by dismal prognosis and frequent recurrence, yet a real-time and reliable biological approach to monitor tumor response and progression is still lacking. Recently, few studies have reported that circulating tumor cells (CTCs) could be detected in glioblastoma multiform (GBM), providing the possibility of its application in brain glioma monitoring system. But its application limits still exist, because the detection rate of CTCs is still low and was exclusively limited to high- grade gliomas. Here, we adopted an advanced integrated cellular and molecular approach of SE-iFISH to detect CTCs in the peripheral blood (PB) of patients with 7 different subtypes of brain glioma, uncovering the direct evidences of glioma migration. We identified CTCs in the PB from 24 of 31 (77%) patients with glioma in all 7 subtypes. No statistical difference of CTC incidence and count was observed in different pathological subtypes or WHO grades of glioma. Clinical data revealed that CTCs, to some extent, was superior to MRI in monitoring the treatment response and differentiating radionecrosis from recurrence of glioma. Conclusively, CTCs is a common property of brain gliomas of various pathological subtypes, which has provided an ultimate paradox for the hypothesis “soil and seed”. It can be used to monitor the microenvironment of gliomas dynamically, which will be a meaningful complement to radiographic imaging.

Glioblastoma and acute myeloid leukemia: malignancies with striking similarities

Acute myeloid leukemia (AML) and glioblastoma (GB) are two malignancies associated with high incidence of treatment refractoriness and generally, uniformly poor survival outcomes. While the former is a hematologic (i.e. a "liquid") malignancy and the latter a solid tumor, the two diseases share both clinical and biochemical characteristics. Both diseases exist predominantly in primary (de novo) forms, with only a small subset of each progressing from precursor disease states like the myelodysplastic syndromes or diffuse glioma. More importantly, the primary and secondary forms of each disease are characterized by common sets of mutations and gene expression abnormalities. The primary versions of AML and GB are characterized by aberrant RAS pathway, matrix metalloproteinase 9, and Bcl-2 expression, and their secondary counterparts share abnormalities in TP53, isocitrate dehydrogenase, ATRX, inhibitor of apoptosis proteins, and survivin that both influence the course of the diseases themselves and their progression from precursor disease. An understanding of these shared features is important, as it can be used to guide both the research about and treatment of each.

Analysis of the cancer genome atlas (TCGA) database identifies an inverse relationship between interleukin-13 receptor α1 and α2 gene expression and poor prognosis and drug resistance in subjects with glioblastoma multiforme

Glioblastoma multiforme (GBM) is the most common primary brain tumor in adults. A variety of targeted agents are being tested in the clinic including cancer vaccines, immunotoxins, antibodies and T cell immunotherapy for GBM. We have previously reported that IL-13 receptor subunits α1 and α2 of IL-13R complex are overexpressed in GBM. We are investigating the significance of IL-13Rα1 and α2 expression in GBM tumors. In order to elucidate a possible relationship between IL-13Rα1 and α2 expression with severity and prognoses of subjects with GBM, we analyzed gene expression (by microarray) and clinical data available at the public The Cancer Genome Atlas (TCGA) database (Currently known as Global Data Commons). More than 40% of GBM samples were highly positive for IL-13Rα2 mRNA (Log2 ≥ 2) while only less than 16% samples were highly positive for IL-13Rα1 mRNA. Subjects with high IL-13Rα1 and α2 mRNA expressing tumors were associated with a significantly lower survival rate irrespective of their treatment compared to subjects with IL-13Rα1 and α2 mRNA negative tumors. We further observed that IL-13Rα2 gene expression is associated with GBM resistance to temozolomide (TMZ) chemotherapy. The expression of IL-13Rα2 gene did not seem to correlate with the expression of genes for other chains involved in the formation of IL-13R complex (IL-13Rα1 or IL-4Rα) in GBM. However, a positive correlation was observed between IL-4Rα and IL-13Rα1 gene expression. The microarray data of IL-13Rα2 gene expression was verified by RNA-Seq data. In depth analysis of TCGA data revealed that immunosuppressive genes (such as FMOD, CCL2, OSM, etc.) were highly expressed in IL-13Rα2 positive tumors, but not in IL-13Rα2 negative tumors. These results indicate a direct correlation between high level of IL-13R mRNA expression and poor patient prognosis and that immunosuppressive genes associated with IL-13Rα2 may play a role in tumor progression. These findings have important implications in understanding the role of IL-13R in the pathogenesis of GBM and potentially other cancers.

PATZ1 is a new prognostic marker of glioblastoma associated with the stem-like phenotype and enriched in the proneural subtype

Glioblastoma (GBM), the most malignant of the brain tumors, has been classified on the basis of molecular signature into four subtypes: classical, mesenchymal, proneural and neural, among which the mesenchymal and proneural subtypes have the shortest and longest survival, respectively. Here we show that the transcription factor PATZ1 gene is upregulated in gliomas compared to normal brain and, among GBMs, is particularly enriched in the proneural subtype and co-localize with stemness markers. Accordingly, in GBM-derived glioma-initiating stem cells (GSCs) PATZ1 is overexpressed compared to differentiated tumor cells and its expression significantly correlates with the characteristic stem cell capacity to grow as neurospheres in vitro. Interestingly, survival analysis demonstrated that PATZ1 lower levels informed poor prognosis in GBM and, specifically, in the proneural subgroup, suggesting it may serve a role as diagnostic and prognostic biomarker for intra-subtype heterogeneity of proneural GBM. We also show that PATZ1 suppresses the expression of the mesenchyme-inducer CXCR4, and that PATZ1 and CXCR4 are inversely correlated in GSC and proneural GBM. Overall these findings support a central role of PATZ1 in regulating malignancy of GBM.

Efficient Mitochondrial Glutamine Targeting Prevails Over Glioblastoma Metabolic Plasticity

Purpose: Glioblastoma (GBM) is the most common and malignant form of primary human brain tumor in adults, with an average survival at diagnosis of 18 months. Metabolism is a new attractive therapeutic target in cancer; however, little is known about metabolic heterogeneity and plasticity within GBM tumors. We therefore aimed to investigate metabolic phenotyping of primary cultures in the context of molecular tumor heterogeneity to provide a proof of concept for personalized metabolic targeting of GBM.Experimental Design: We have analyzed extensively several primary GBM cultures using transcriptomics, metabolic phenotyping assays, and mitochondrial respirometry.Results: We found that metabolic phenotyping clearly identifies 2 clusters, GLN^High and GLN^Low, mainly based on metabolic plasticity and glutamine (GLN) utilization. Inhibition of glutamine metabolism slows the in vitro and in vivo growth of GLN^High GBM cultures despite metabolic adaptation to nutrient availability, in particular by increasing pyruvate shuttling into mitochondria. Furthermore, phenotypic and molecular analyses show that highly proliferative GLN^High cultures are CD133^neg and display a mesenchymal signature in contrast to CD133^pos GLN^Low GBM cells.Conclusions: Our results show that metabolic phenotyping identified an essential metabolic pathway in a GBM cell subtype, and provide a proof of concept for theranostic metabolic targeting. Clin Cancer Res; 23(20); 6292-304. ©2017 AACR.

RNA processing as an alternative route to attack glioblastoma

Genomic analyses have become an important tool to identify new avenues for therapy. This is especially true for cancer types with extremely poor outcomes, since our lack of effective therapies offers no tangible clinical starting point to build upon. The highly malignant brain tumor glioblastoma (GBM) exemplifies such a refractory cancer, with only 15 month average patient survival. Analyses of several hundred GBM samples compiled by the TCGA (The Cancer Genome Atlas) have produced an extensive transcriptomic map, identified prevalent chromosomal alterations, and defined important driver mutations. Unfortunately, clinical trials based on these results have not yet delivered an improvement on outcome. It is, therefore, necessary to characterize other regulatory routes known for playing a role in tumor relapse and response to treatment. Alternative splicing affects more than 90% of the human coding genes and it is an important source for transcript variation and gene regulation. Mutations and alterations in splicing factors are highly prevalent in multiple cancers, demonstrating the potential for splicing to act as a tumor driver. As a result, numerous genes are expressed as cancer-specific splicing isoforms that are functionally distinct from the canonical isoforms found in normal tissue. These include genes that regulate cancer-critical pathways such as apoptosis, DNA repair, cell proliferation, and migration. Splicing defects can even induce genomic instability, a common characteristic of cancer, and a driver of tumor evolution. Importantly, components of the splicing machinery are targetable; multiple drugs can inhibit splicing factors or promote changes in splicing which could be exploited to begin improving clinical outcomes. Here, we review the current literature and present a case for exploring RNA processing as therapeutic route for the treatment of GBM.

Blocking epithelial-to-mesenchymal transition in glioblastoma with a sextet of repurposed drugs: the EIS regimen

This paper outlines a treatment protocol to run alongside of standard current treatment of glioblastoma- resection, temozolomide and radiation. The epithelial to mesenchymal transition (EMT) inhibiting sextet, EIS Regimen, uses the ancillary attributes of six older medicines to impede EMT during glioblastoma. EMT is an actively motile, therapy-resisting, low proliferation, transient state that is an integral feature of cancers’ lethality generally and of glioblastoma specifically. It is believed to be during the EMT state that glioblastoma’s centrifugal migration occurs. EMT is also a feature of untreated glioblastoma but is enhanced by chemotherapy, by radiation and by surgical trauma. EIS Regimen uses the antifungal drug itraconazole to block Hedgehog signaling, the antidiabetes drug metformin to block AMP kinase (AMPK), the analgesic drug naproxen to block Rac1, the anti-fibrosis drug pirfenidone to block transforming growth factor-beta (TGF-beta), the psychiatric drug quetiapine to block receptor activator NFkB ligand (RANKL) and the antibiotic rifampin to block Wnt- all by their previously established ancillary attributes. All these systems have been identified as triggers of EMT and worthy targets to inhibit. The EIS Regimen drugs have a good safety profile when used individually. They are not expected to have any new side effects when combined. Further studies of the EIS Regimen are needed.

A Heterogeneous Network Based Method for Identifying GBM-Related Genes by Integrating Multi-Dimensional Data

The emergence of multi-dimensional data offers opportunities for more comprehensive analysis of the molecular characteristics of human diseases and therefore improving diagnosis, treatment, and prevention. In this study, we proposed a heterogeneous network based method by integrating multi-dimensional data (HNMD) to identify GBM-related genes. The novelty of the method lies in that the multi-dimensional data of GBM from TCGA dataset that provide comprehensive information of genes, are combined with protein-protein interactions to construct a weighted heterogeneous network, which reflects both the general and disease-specific relationships between genes. In addition, a propagation algorithm with resistance is introduced to precisely score and rank GBM-related genes. The results of comprehensive performance evaluation show that the proposed method significantly outperforms the network based methods with single-dimensional data and other existing approaches. Subsequent analysis of the top ranked genes suggests they may be functionally implicated in GBM, which further corroborates the superiority of the proposed method. The source code and the results of HNMD can be downloaded from the following URL: http://bioinformatics.ustc.edu.cn/hnmd/ .

FBLN4 as candidate gene associated with long-term and short-term survival with primary glioblastoma

Background

Glioblastoma multiforme (GBM) is the most common malignant and lethal type of primary central nervous system tumor in humans. In spite of its high lethality, a small percentage of patients have a relatively good prognosis, with median survival times of 36 months or longer. The identification of clinical subsets of GBM associated with distinct molecular genetic profiles has made it possible to design therapies tailored to treat individual patients.

Methods

We compared microarray data sets from long-term survivors (LTSs) and short-term survivors (STSs) to screen for prognostic biomarkers in GBM patients using the WebArrayDB platform. We focused on FBLN4, IGFBP-2, and CHI3L1, all members of a group of 10 of the most promising, differentially regulated gene candidates. Using formalin-fixed paraffin-embedded GBM samples, we corroborated the relationship between these genes and patient outcomes using methylation-specific polymerase chain reaction (PCR) for MGMT methylation status and quantitative reverse transcription PCR for expression of these genes.

Results

Expression levels of the mRNAs of these 3 genes were higher in the GBM samples than in normal brain samples and these 3 genes were significantly upregulated in STSs compared to the levels in LTS samples (P<0.01). Furthermore, Kaplan–Meier analysis showed that the expression patterns of FBLN4 and IGFBP-2 serve as independent prognostic indicators for overall survival (P<0.01 and P<0.05, respectively).

Conclusion

To our knowledge, this is the first report describing FBLN4 as a prognostic factor for GBM patient survival, demonstrating that increased GBM survival time correlates with decreased FBLN4 expression. Understanding FBLN4 expression patterns could aid in the creation of powerful tools to predict clinical prognoses of GBM patients.

A quantitative study of shape descriptors from glioblastoma multiforme phenotypes for predicting survival outcome

OBJECTIVE

Predicting the survival outcome of patients with glioblastoma multiforme (GBM) is of key importance to clinicians for selecting the optimal course of treatment. The goal of this study was to evaluate the usefulness of geometric shape features, extracted from MR images, as a potential non-invasive way to characterize GBM tumours and predict the overall survival times of patients with GBM.

METHODS

The data of 40 patients with GBM were obtained from the Cancer Genome Atlas and Cancer Imaging Archive. The T₁ weighted post-contrast and fluid-attenuated inversion-recovery volumes of patients were co-registered and segmented into delineate regions corresponding to three GBM phenotypes: necrosis, active tumour and oedema/invasion. A set of two-dimensional shape features were then extracted slicewise from each phenotype region and combined over slices to describe the three-dimensional shape of these phenotypes. Thereafter, a Kruskal-Wallis test was employed to identify shape features with significantly different distributions across phenotypes. Moreover, a Kaplan-Meier analysis was performed to find features strongly associated with GBM survival. Finally, a multivariate analysis based on the random forest model was used for predicting the survival group of patients with GBM.

RESULTS

Our analysis using the Kruskal-Wallis test showed that all but one shape feature had statistically significant differences across phenotypes, with p-value < 0.05, following Holm-Bonferroni correction, justifying the analysis of GBM tumour shapes on a per-phenotype basis. Furthermore, the survival analysis based on the Kaplan-Meier estimator identified three features derived from necrotic regions (i.e. Eccentricity, Extent and Solidity) that were significantly correlated with overall survival (corrected p-value < 0.05; hazard ratios between 1.68 and 1.87). In the multivariate analysis, features from necrotic regions gave the highest accuracy in predicting the survival group of patients, with a mean area under the receiver-operating characteristic curve (AUC) of 63.85%. Combining the features of all three phenotypes increased the mean AUC to 66.99%, suggesting that shape features from different phenotypes can be used in a synergic manner to predict GBM survival.

CONCLUSION

Results show that shape features, in particular those extracted from necrotic regions, can be used effectively to characterize GBM tumours and predict the overall survival of patients with GBM. Advances in knowledge: Simple volumetric features have been largely used to characterize the different phenotypes of a GBM tumour (i.e. active tumour, oedema and necrosis). This study extends previous work by considering a wide range of shape features, extracted in different phenotypes, for the prediction of survival in patients with GBM.