A risk signature with four autophagy-related genes for predicting survival of glioblastoma multiforme

Glioblastoma multiforme (GBM) is a devastating brain tumour without effective treatment. Recent studies have shown that autophagy is a promising therapeutic strategy for GBM. Therefore, it is necessary to identify novel biomarkers associated with autophagy in GBM. In this study, we downloaded autophagy-related genes from Human Autophagy Database (HADb) and Gene Set Enrichment Analysis (GSEA) website. Least absolute shrinkage and selection operator (LASSO) regression and multivariate Cox regression analysis were performed to identify genes for constructing a risk signature. A nomogram was developed by integrating the risk signature with clinicopathological factors. Time-dependent receiver operating characteristic (ROC) curve and calibration plot were used to evaluate the efficiency of the prognostic model. Finally, four autophagy-related genes (DIRAS3, LGALS8, MAPK8 and STAM) were identified and were used for constructing a risk signature, which proved to be an independent risk factor for GBM patients. Furthermore, a nomogram was developed based on the risk signature and clinicopathological factors (IDH1 status, age and history of radiotherapy or chemotherapy). ROC curve and calibration plot suggested the nomogram could accurately predict 1-, 3- and 5-year survival rate of GBM patients. For function analysis, the risk signature was associated with apoptosis, necrosis, immunity, inflammation response and MAPK signalling pathway. In conclusion, the risk signature with 4 autophagy-related genes could serve as an independent prognostic factor for GBM patients. Moreover, we developed a nomogram based on the risk signature and clinical traits which was validated to perform better for predicting 1-, 3- and 5-year survival rate of GBM.     

Gene regulation network analysis reveals core genes associated with survival in glioblastoma multiforme

Glioblastoma multiforme (GBM) is a very serious mortality of central nervous system cancer. The microarray data from GSE2223 , GSE4058 , GSE4290 , GSE13276 , GSE68848 and GSE70231 (389 GBM tumour and 67 normal tissues) and the RNA‐seq data from TCGA‐GBM dataset (169 GBM and five normal samples) were chosen to find differentially expressed genes (DEGs). RRA (Robust rank aggregation) method was used to integrate seven datasets and calculate 133 DEGs (82 up‐regulated and 51 down‐regulated genes). Subsequently, through the PPI (protein‐protein interaction) network and MCODE/ cytoHubba methods, we finally filtered out ten hub genes, including FOXM1, CDK4, TOP2A, RRM2, MYBL2, MCM2, CDC20, CCNB2, MYC and EZH2, from the whole network. Functional enrichment analyses of DEGs were conducted to show that these hub genes were enriched in various cancer‐related functions and pathways significantly. We also selected CCNB2, CDC20 and MYBL2 as core biomarkers, and further validated them in CGGA, HPA and CCLE database, suggesting that these three core hub genes may be involved in the origin of GBM. All these potential biomarkers for GBM might be helpful for illustrating the important role of molecular mechanisms of tumorigenesis in the diagnosis, prognosis and targeted therapy of GBM cancer.     

APRIL is increased in serum of patients with brain glioblastoma multiforme

A PRoliferation-Inducing Ligand (APRIL) is a cytokine with the ability to induce tumorigenesis. The aim of the study was to measure serum APRIL levels in patients with brain glioblastoma multiforme. Twenty five patients with brain tumor and a control group of 25 subjects took part in the study. APRIL was measured by the enzyme-linked immunosorbent method. The study showed increased APRIL levels in the serum of patients with brain glioblastoma multiforme compared to the control group (p < 0.05). However, there was no significant difference in the level of this cytokine between groups of patients divided according to their clinical state and tumor size (p > 0.05). Inflammation parameters such as C-reactive protein (CRP) and polymorphonuclear leucocytes (PMN) were also increased in patients with brain tumor compared to controls (p < 0.05). There was a significant correlation between APRIL and CRP and PMN (p < 0.05). Results from the study suggest that APRIL may play a role in the pathogenesis of brain glioblastoma multiforme. It is possible that anti-APRIL therapy might be useful in this disease. However, this cytokine cannot be regarded as a marker of tumor size or of severity of the clinical condition of patients.     

Expression and significance of SOX B1 genes in glioblastoma multiforme patients

The overall survival of glioblastoma multiforme (GBM) patients remains poor. To improve patient outcomes, effective diagnostic and prognostic biomarkers for GBM are needed. In this study, we first applied bioinformatic analyses to identify biomarkers for GBM, focusing on SOX (sex‐determining region on the Y chromosome (SRY)‐related high mobility group (HMG) box) B1 family members. The ONCOMINE, GEPIA, LinkedOmics and CCLE databases were used to assess mRNA expression levels of the SOX B1 family members in different cancers and normal tissue. Further bioinformatic analysis was performed using the ONCOMINE database in combination with the LinkedOmics data set to identify the prognostic value of SOX B1 family members for GBM. We found mRNA expression levels of all tested SOX B1 genes were significantly increased in GBM. In the LinkedOmics database, increased expression of SOX3 indicated a better overall survival. In GEPIA databases, increased expression of all SOX B1 family members suggested an improved overall survival, but none of them were statistically different. Then, Transwell assays and wound healing were employed to evaluate the motility and invasive captivity of U251 cells when silencing SOX2 and SOX3. We found exogenous inhibition of SOX2 appeared to reduce the migration and invasion of U251 cells in vitro. Collectively, our research suggested that SOX2 might serve as a cancer‐promoting gene to identify high‐risk GBM patients, and SOX3 had the potential to be a prognostic biomarker for GBM patients.     

Pathologic anatomy and CT correlations in the glioblastoma multiforme

6 cases of glioblastoma multiforme, 1 anaplastic astrocytoma, and 1 astrocytoma were studied pathologically post mortem. The specimens were compared to antemortem CT scans, when available. By use of whole brain sections, each glioblastoma was divided into a central area of necrosis, a surrounding rim of hypercellular neoplasm, and a peripheral zone of infiltration. The dimensions and areas of these three zones were quantified. The striking findings of the study were the often small diameter of the hypercellular region and the frequently narrow peripheral zone of detectable infiltrating cells. Comparison of the autopsy specimens with CT scans confirmed previous studies of the glioblastoma. The radiographic central area of low density is necrosis; the enhancing rim is a markedly hypercellular region, and the perilesional low density encompasses a population of infiltrating tumor cells.     

Image-driven modeling of the proliferation and necrosis of glioblastoma multiforme

Background

The heterogeneity of response to treatment in patients with glioblastoma multiforme suggests that the optimal therapeutic approach incorporates an individualized assessment of expected lesion progression. In this work, we develop a novel computational model for the proliferation and necrosis of glioblastoma multiforme.

Methods

The model parameters are selected based on the magnetic resonance imaging features of each tumor, and the proposed technique accounts for intrinsic cell division, tumor cell migration along white matter tracts, as well as central tumor necrosis. As a validation of this approach, tumor growth is simulated in the brain of a healthy adult volunteer using parameters derived from the imaging of a patient with glioblastoma multiforme. A mutual information metric is calculated between the simulated tumor profile and observed tumor.

Results

The tumor progression profile generated by the proposed model is compared with those produced by existing models and with the actual observed tumor progression. Both qualitative and quantitative analyses show that the model introduced in this work replicates the observed progression of glioblastoma more accurately relative to prior techniques.

Conclusions

This image-driven model generates improved tumor progression profiles and may contribute to the development of more reliable prognostic estimates in patients with glioblastoma multiforme.

     

Neuronatin in a subset of glioblastoma multiforme tumor progenitor cells is associated with increased cell proliferation and shorter patient survival

Glioblastoma multiforme is the most common and malignant primary brain tumor. Recent evidence indicates that a subset of glioblastoma tumor cells have a stem cell like phenotype that underlies chemotherapy resistance and tumor recurrence. We utilized a new "multidimensional" capillary isoelectric focusing nano-reversed-phase liquid chromatography platform with tandem mass spectrometry to compare the proteomes of isolated glioblastoma tumor stem cell and differentiated tumor cell populations. This proteomic analysis yielded new candidate proteins that were differentially expressed. Specifically, two isoforms of the membrane proteolipid neuronatin (NNAT) were expressed exclusively within the tumor stem cells. We surveyed the expression of NNAT across 10 WHO grade II and III gliomas and 23 glioblastoma (grade IV) human tumor samples and found NNAT was expressed in a subset of primary glioblastoma tumors. Through additional in vitro studies utilizing the U87 glioma cell line, we found that expression of NNAT is associated with significant increases in cellular proliferation. Paralleling the in vitro results, when NNAT levels were evaluated in tumor specimens from a consecutive cohort of 59 glioblastoma patients, the presence of increased levels of NNAT were found to be a an independent risk factor (P?=?0.006) for decreased patient survival through Kaplan-Meier and multivariate analysis. These findings indicate that NNAT may have utility as a prognostic biomarker, as well as a cell-surface target for chemotherapeutic agents.     

GSVD comparison of patient-matched normal and tumor aCGH profiles reveals global copy-number alterations predicting glioblastoma multiforme survival

Despite recent large-scale profiling efforts, the best prognostic predictor of glioblastoma multiforme (GBM) remains the patient's age at diagnosis. We describe a global pattern of tumor-exclusive co-occurring copy-number alterations (CNAs) that is correlated, possibly coordinated with GBM patients' survival and response to chemotherapy. The pattern is revealed by GSVD comparison of patient-matched but probe-independent GBM and normal aCGH datasets from The Cancer Genome Atlas (TCGA). We find that, first, the GSVD, formulated as a framework for comparatively modeling two composite datasets, removes from the pattern copy-number variations (CNVs) that occur in the normal human genome (e.g., female-specific X chromosome amplification) and experimental variations (e.g., in tissue batch, genomic center, hybridization date and scanner), without a-priori knowledge of these variations. Second, the pattern includes most known GBM-associated changes in chromosome numbers and focal CNAs, as well as several previously unreported CNAs in >3% of the patients. These include the biochemically putative drug target, cell cycle-regulated serine/threonine kinase-encoding TLK2, the cyclin E1-encoding CCNE1, and the Rb-binding histone demethylase-encoding KDM5A. Third, the pattern provides a better prognostic predictor than the chromosome numbers or any one focal CNA that it identifies, suggesting that the GBM survival phenotype is an outcome of its global genotype. The pattern is independent of age, and combined with age, makes a better predictor than age alone. GSVD comparison of matched profiles of a larger set of TCGA patients, inclusive of the initial set, confirms the global pattern. GSVD classification of the GBM profiles of an independent set of patients validates the prognostic contribution of the pattern.     

Establishment and partial characterization of a human tumor cell line,GBM-HSF,from a glioblastoma multiforme

This paper outlines the establishment of a new and stable cell line, designated GBM-HSF, from a malignant glioblastoma multiforme (GBM) removed from a 65-year-old Chinese woman. This cell line has been grown for 1 year without disruption and has been passaged over 50 times. The cells were adherently cultured in RPMI-1640 media with 10 % fetal bovine serum supplementation. Cells displayed spindle and polygonal morphology, and displayed multi-layered growth without evidence of contact inhibition. The cell line had a high growth rate with a doubling time of 51 h. The cells were able to grow without adhering to the culture plates, and 4.5 % of the total cells formed colonies in soft agar. The cell line has also been found to form tumors in nude mice and to be of a highly invasive nature. The cells were also partially characterized with RT-PCR. The RT-PCR revealed that Nestin, β-tubulin III, Map2, Klf4, Oct4, Sox2, Nanog, and CD26 were positively transcribed, whereas GFAP, Rex1, and CD133 were negatively transcribed in this cell line. These results suggest that the GBM-HSF cell line will provide a good model to study the properties of cancer stem cells and metastasis. It will also facilitate more detailed molecular and cellular studies of GBM cell division and pathology.     

Significant association of multiple human cytomegalovirus genomic Loci with glioblastoma multiforme samples

Viruses are appreciated as etiological agents of certain human tumors, but the number of different cancer types induced or exacerbated by viral infections is unknown. Glioblastoma multiforme (GBM)/astrocytoma grade IV is a malignant and lethal brain cancer of unknown origin. Over the past decade, several studies have searched for the presence of a prominent herpesvirus, human cytomegalovirus (HCMV), in GBM samples. While some have detected HCMV DNA, RNA, and proteins in GBM tissues, others have not. Therefore, any purported association of HCMV with GBM remains controversial. In most of the previous studies, only one or a select few viral targets were analyzed. Thus, it remains unclear the extent to which the entire viral genome was present when detected. Here we report the results of a survey of GBM specimens for as many as 20 different regions of the HCMV genome. Our findings indicate that multiple HCMV loci are statistically more likely to be found in GBM samples than in other brain tumors or epileptic brain specimens and that the viral genome was more often detected in frozen samples than in paraffin-embedded archival tissue samples. Finally, our experimental results indicate that cellular genomes substantially outnumber viral genomes in HCMV-positive GBM specimens, likely indicating that only a minority of the cells found in such samples harbor viral DNA. These data argue for the association of HCMV with GBM, defining the virus as oncoaccessory. Furthermore, they imply that, were HCMV to enhance the growth or survival of a tumor (i.e., if it is oncomodulatory), it would likely do so through mechanisms distinct from classic tumor viruses that express transforming viral oncoproteins in the overwhelming majority of tumor cells.     

Proteogenomics of glioblastoma associates molecular patterns with survival

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