An integrated genomic analysis of human glioblastoma multiforme.
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ABSTRACT:
SUBMITTER: Parsons DW
PROVIDER: S-EPMC2820389 | biostudies-literature | 2008 Sep
REPOSITORIES: biostudies-literature
Publications
An integrated genomic analysis of human glioblastoma multiforme.
Parsons D Williams DW Jones Siân S Zhang Xiaosong X Lin Jimmy Cheng-Ho JC Leary Rebecca J RJ Angenendt Philipp P Mankoo Parminder P Carter Hannah H Siu I-Mei IM Gallia Gary L GL Olivi Alessandro A McLendon Roger R Rasheed B Ahmed BA Keir Stephen S Nikolskaya Tatiana T Nikolsky Yuri Y Busam Dana A DA Tekleab Hanna H Diaz Luis A LA Hartigan James J Smith Doug R DR Strausberg Robert L RL Marie Suely Kazue Nagahashi SK Shinjo Sueli Mieko Oba SM Yan Hai H Riggins Gregory J GJ Bigner Darell D DD Karchin Rachel R Papadopoulos Nick N Parmigiani Giovanni G Vogelstein Bert B Velculescu Victor E VE Kinzler Kenneth W KW
Science (New York, N.Y.) 20080904 5897
Glioblastoma multiforme (GBM) is the most common and lethal type of brain cancer. To identify the genetic alterations in GBMs, we sequenced 20,661 protein coding genes, determined the presence of amplifications and deletions using high-density oligonucleotide arrays, and performed gene expression analyses using next-generation sequencing technologies in 22 human tumor samples. This comprehensive analysis led to the discovery of a variety of genes that were not known to be altered in GBMs. Most n ...[more]
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Project description: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.
| S-EPMC3255835 | biostudies-literature
Project description:BackgroundGliomas are the most common intracranial tumors and are classified as I-IV. Among them, glioblastoma multiforme (GBM) is the most common invasive glioma with a poor prognosis. New molecular biomarkers that can predict clinical outcomes in GBM patients must be identified, which will help comprehend their pathogenesis and supply personalized treatment. Our research revealed four powerful survival indicators in GBM by reanalyzing microarray data and genetic sequencing data in public databases. Moreover, it unraveled new potential therapeutic targets which could help improve the survival time and quality of life of GBM patients.Materials and methodsTo identify prognostic signatures in GBMs, we analyzed the gene profiling data of GBM and standard brain samples from the Gene Expression Omnibus, including four datasets and RNA sequencing data from The Cancer Genome Atlas (TCGA) containing 152 glioblastoma tissues. We performed the differential analysis, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, weighted gene co-expression network analysis (WGCNA) and Cox regression analysis.ResultsAfter differential analysis in GSE12657, GSE15824, GSE42656 and GSE50161, overlapping differentially expressed genes were identified. We identified 110 up-regulated DEGs and 75 down-regulated DEGs in the GBM samples. Significantly enriched subclasses of the GO classification of these genes included mitotic sister chromatid separation, mitotic nuclear division and so on. In KEGG pathway analysis, the most abundant terms were ECM-receptor interaction and protein digestion and absorption. WGCNA analysis was performed on these 185 DEGs in 152 glioblastoma samples obtained from TCGA, and gene co-expression networks were constructed. We then performed a multivariate Cox analysis and established a Cox proportional hazards regression model using the top 20 genes significantly correlated with survival time. We identified a four-protein prognostic signature that could divide patients into high-risk and low-risk groups. Increased expression of SLC12A5, CCL2, IGFBP2, and PDPN was associated with increased risk scores. Finally, the K-M curves confirmed that these genes could be used as independent predictors of survival in patients with glioblastoma.ConclusionOur analytical study identified a set of potential biomarkers that could predict survival and may contribute to successful treatment of GBM patients.
| S-EPMC7136556 | biostudies-literature
Project description:BackgroundTo elucidate molecular features associated with disproportionate survival of glioblastoma (GB) patients, we conducted deep genomic comparative analysis of a cohort of patients receiving standard therapy (surgery plus concurrent radiation and temozolomide); "GB outliers" were identified: long-term survivor of 33 months (LTS; n = 8) versus short-term survivor of 7 months (STS; n = 10).MethodsWe implemented exome, RNA, whole genome sequencing, and DNA methylation for collection of deep genomic data from STS and LTS GB patients.ResultsLTS GB showed frequent chromosomal gains in 4q12 (platelet derived growth factor receptor alpha and KIT) and 12q14.1 (cyclin-dependent kinase 4), and deletion in 19q13.33 (BAX, branched chain amino-acid transaminase 2, and cluster of differentiation 33). STS GB showed frequent deletion in 9p11.2 (forkhead box D4-like 2 and aquaporin 7 pseudogene 3) and 22q11.21 (Hypermethylated In Cancer 2). LTS GB showed 2-fold more frequent copy number deletions compared with STS GB. Gene expression differences showed the STS cohort with altered transcriptional regulators: activation of signal transducer and activator of transcription (STAT)5a/b, nuclear factor-kappaB (NF-κB), and interferon-gamma (IFNG), and inhibition of mitogen-activated protein kinase (MAPK1), extracellular signal-regulated kinase (ERK)1/2, and estrogen receptor (ESR)1. Expression-based biological concepts prominent in the STS cohort include metabolic processes, anaphase-promoting complex degradation, and immune processes associated with major histocompatibility complex class I antigen presentation; the LTS cohort features genes related to development, morphogenesis, and the mammalian target of rapamycin signaling pathway. Whole genome methylation analyses showed that a methylation signature of 89 probes distinctly separates LTS from STS GB tumors.ConclusionWe posit that genomic instability is associated with longer survival of GB (possibly with vulnerability to standard therapy); conversely, genomic and epigenetic signatures may identify patients where up-front entry into alternative, targeted regimens would be a preferred, more efficacious management.
| S-EPMC5464466 | biostudies-literature
Project description:Glioblastoma multiforme (GBM) is the most aggressive and deadly brain tumor. It is primarily diagnosed in the elderly and has a 5-year survival rate of less than 6% even with the most aggressive therapies. The lack of biomarkers has made the development of immunotherapy for GBM challenging. Human endogenous retroviruses (HERVs) are a group of viruses with long terminal repeat (LTR) elements, which are believed to be relics from ancient viral infections. Recent studies have found that those repetitive elements play important roles in regulating various biological processes. The differentially expressed LTR elements from HERVs are potential biomarkers for immunotherapy to treat GBM. However, the understanding of the LTR element expression in GBM is greatly lacking. We obtained 1077.4 GB of sequencing data from public databases. These data were generated from 111 GBM tissue studies, 30 GBM cell lines studies, and 45 normal brain tissues studies. We analyzed repetitive elements that were differentially expressed in GBM and normal brain samples. We found that 48 LTR elements were differentially expressed (p-value < 0.05) between GBM and normal brain tissues, of which 46 were HERV elements. Among these 46 elements, 34 significantly changed HERVs belong to the ERV1 superfamily. Furthermore, 43 out of the 46 differentially expressed HERV elements were upregulated. Our results indicate significant differential expression of many HERV LTR elements in GBM and normal brain tissues. Expression levels of these elements could be developed as biomarkers for GBM treatments.
| S-EPMC8067472 | biostudies-literature
Project description:Copy number analysis of human GBM samples were performed, and a high frequency of deletions of the PTPRD gene on chromosome 9p23-24.1 were identified. Keywords: SNP microarray, glioblastoma multiforme, copy number, amplification, deletion
2009-01-05 | GSE13021 | GEO
Project description:Our goals are to develop a computational histopathology pipeline for characterizing tumor types that are being generated by The Cancer Genome Atlas (TCGA) for genomic association. TCGA is a national collaborative program where different tumor types are being collected, and each tumor is being characterized using a variety of genome-wide platforms. Here, we have developed a tumor-centric analytical pipeline to process tissue sections stained with hematoxylin and eosin (H&E) for visualization and cell-by-cell quantitative analysis. Thus far, analysis is limited to Glioblastoma Multiforme (GBM) and kidney renal clear cell carcinoma tissue sections. The final results are being distributed for subtyping and linking the histology sections to the genomic data.A computational pipeline has been designed to continuously update a local image database, with limited clinical information, from an NIH repository. Each image is partitioned into blocks, where each cell in the block is characterized through a multidimensional representation (e.g., nuclear size, cellularity). A subset of morphometric indices, representing potential underlying biological processes, can then be selected for subtyping and genomic association. Simultaneously, these subtypes can also be predictive of the outcome as a result of clinical treatments. Using the cellularity index and nuclear size, the computational pipeline has revealed five subtypes, and one subtype, corresponding to the extreme high cellularity, has shown to be a predictor of survival as a result of a more aggressive therapeutic regime. Further association of this subtype with the corresponding gene expression data has identified enrichment of (i) the immune response and AP-1 signaling pathways, and (ii) IFNG, TGFB1, PKC, Cytokine, and MAPK14 hubs.While subtyping is often performed with genome-wide molecular data, we have shown that it can also be applied to categorizing histology sections. Accordingly, we have identified a subtype that is a predictor of the outcome as a result of a therapeutic regime. Computed representation has become publicly available through our Web site.
| S-EPMC3271112 | biostudies-literature
Project description:Although patients with Glioblastoma multiforme (GBM) have grave prognosis, significant variability in patient outcome is observed. The objective of this study is to identify a molecular signature for GBM prognosis. We subjected 355 mRNA and microRNA expression profiles to elastic net-regulated Cox regression for identification of an integrated RNA signature for GBM prognosis. A prognostic index (PI) was generated for patient stratification. Survival comparison was conducted by Kaplan-Meier method and a general multivariate Cox regression procedure was applied to evaluate the independence of the PI. The abilities and efficiencies of signatures to predict GBM patient outcome was assessed and compared by the area under the curve (AUC) of the receiver-operator characteristic (ROC). An integrated RNA prognostic signature consisted by 4 protective mRNAs, 12 risky mRNAs, and 1 risky microRNA was identified. Decreased survival was associated with being in the high-risk group (hazard ratio?=?2.864, P<0.0001). The prognostic value of the integrated signature was validated in five independent GBM expression datasets (n?=?201, hazard ratio?=?2.453, P<0.0001). The PI outperformed the known clinical factors, mRNA-only, and miRNA-only prognostic signatures for GBM prognosis (area under the ROC curve for the integrated RNA, mRNA-only, and miRNA-only signatures were 0.828, 0.742, and 0.757 at 3 years of overall survival, respectively, P<0.0001 by permutation test). We describe the first, to our knowledge, robust transcriptome-based integrated RNA signature that improves the current GBM prognosis based on clinical variables, mRNA-only, and miRNA-only signatures.
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Project description:Copy number analysis of human GBM samples were performed, and a high frequency of deletions of the PTPRD gene on chromosome 9p23-24.1 were identified. Keywords: SNP microarray, glioblastoma multiforme, copy number, amplification, deletion Genomic DNA from 58 GBM tumor samples were hybridized to Affymetrix 250K NspI Gene Chip Arrays and analyzed by dChip using the hg17 genome assembly.
2009-01-04 | E-GEOD-13021 | biostudies-arrayexpress
Project description:BACKGROUND: SOX2 is a key gene implicated in maintaining the stemness of embryonic and adult stem cells. SOX2 appears to re-activate in several human cancers including glioblastoma multiforme (GBM), however, the detailed response program of SOX2 in GBM has not yet been defined. RESULTS: We show that knockdown of the SOX2 gene in LN229 GBM cells reduces cell proliferation and colony formation. We then comprehensively characterize the SOX2 response program by an integrated analysis using several advanced genomic technologies including ChIP-seq, microarray profiling, and microRNA sequencing. Using ChIP-seq technology, we identified 4883 SOX2 binding regions in the GBM cancer genome. SOX2 binding regions contain the consensus sequence wwTGnwTw that occurred 3931 instances in 2312 SOX2 binding regions. Microarray analysis identified 489 genes whose expression altered in response to SOX2 knockdown. Interesting findings include that SOX2 regulates the expression of SOX family proteins SOX1 and SOX18, and that SOX2 down regulates BEX1 (brain expressed X-linked 1) and BEX2 (brain expressed X-linked 2), two genes with tumor suppressor activity in GBM. Using next generation sequencing, we identified 105 precursor microRNAs (corresponding to 95 mature miRNAs) regulated by SOX2, including down regulation of miR-143, -145, -253-5p and miR-452. We also show that miR-145 and SOX2 form a double negative feedback loop in GBM cells, potentially creating a bistable system in GBM cells. CONCLUSIONS: We present an integrated dataset of ChIP-seq, expression microarrays and microRNA sequencing representing the SOX2 response program in LN229 GBM cells. The insights gained from our integrated analysis further our understanding of the potential actions of SOX2 in carcinogenesis and serves as a useful resource for the research community.
| S-EPMC3022822 | biostudies-literature
Project description:Glioblastoma multiforme is the most common primary intracranial malignancy, but its etiology and pathogenesis are still unclear. With the deepening of human genome research, the research of glioma subtype screening based on core molecules has become more in-depth. In the present study, we screened out differentially expressed genes (DEGs) through reanalyzing the glioblastoma multiforme (GBM) datasets GSE90598 from the Gene Expression Omnibus (GEO), the GBM dataset TCGA-GBM and the low-grade glioma (LGG) dataset TCGA-LGG from the Cancer Genome Atlas (TCGA). A total of 150 intersecting DEGs were found, of which 48 were upregulated and 102 were downregulated. These DEGs from GSE90598 dataset were enriched using the overrepresentation method, and multiple enriched gene ontology (GO) function terms were significantly correlated with neural cell signal transduction. DEGs between GBM and LGG were analyzed by gene set enrichment analysis (GSEA), and the significantly enriched Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways involved in synapse signaling and oxytocin signaling pathways. Then, a protein-protein interaction (PPI) network was constructed to assess the interaction of proteins encoded by the DEGs. MCODE identified 2 modules from the PPI network. The 11 genes with the highest degrees in module 1 were designated as core molecules, namely, GABRD, KCNC1, KCNA1, SYT1, CACNG3, OPALIN, CD163, HPCAL4, ANK3, KIF5A, and MS4A6A, which were mainly enriched in ionic signaling-related pathways. Survival analysis of the GSE83300 dataset verified the significant relationship between expression levels of the 11 core genes and survival. Finally, the core molecules of GBM and the DrugBank database were assessed by a hypergeometric test to identify 10 drugs included tetrachlorodecaoxide related to cancer and neuropsychiatric diseases. Further studies are required to explore these core genes for their potentiality in diagnosis, prognosis, and targeted therapy and explain the relationship among ionic signaling-related pathways, neuropsychiatric diseases and neurological tumors.
| S-EPMC7943725 | biostudies-literature