Unknown,Transcriptomics,Genomics,Proteomics

Dataset Information

Whole genome expression in meningioma

ABSTRACT: We performed expression profiling of 24 meningioma and two dura controls analyzing 55000 transcripts including 18300 known genes. We compared expression in meningioma vs. dura, expression of low grade (WHO I) vs. higher-grade (WHO II and WHO IIII) tumors and expression of meningothelial and syncytial meningioma vs. fibroblastic meningioma. Gene expression was analysed in 24 meningioma including eight of each WHO grade and two dura controls analyzing 55000 transcripts including 18300 known genes.

ORGANISM(S): Homo sapiens

SUBMITTER: Nicole Ludwig

PROVIDER: E-GEOD-12530 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Publications

Genome wide expression profiling identifies specific deregulated pathways in meningioma.

Keller Andreas A Ludwig Nicole N Backes Christina C Romeike Bernd F M BF Comtesse Nicole N Henn Wolfram W Steudel Wolf-Ingo WI Mawrin Christian C Lenhof Hans-Peter HP Meese Eckart E

International journal of cancer 20090101 2

Genome-wide expression signatures improve the understanding of tumor biology. We performed expression profiling of 24 meningioma including 8 of each WHO grade and 2 dura controls analyzing 55.000 transcripts including 18.300 known genes. We compared expression in meningioma vs. dura, expression of low grade (WHO I) vs. higher-grade (WHO II and WHO III) tumors and expression of meningothelial and syncytial meningioma vs. fibroblastic meningioma. Overall expression was significantly decreased in m ...[more]

PMID: 19003955

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Project description:Background: Current diagnosis and treatment of urinary bladder cancer (BC) has shown great progress with the utilization of microarrays. Purpose: Our goal was to identify common differentially expressed (DE) genes among clinically relevant subclasses of BC using microarrays. Methodology/Principal Findings: BC samples and controls, both experimental as well as publicly available datasets, were analyzed by whole genome microarrays. We grouped the samples according to their histology and defined the DE genes in each sample individually as well as in each tumor group, respectively. A dual analysis strategy was followed: First, experimental samples were analyzed and conclusions were extracted; and second, experimental sets were combined with publicly available microarray datasets and were further analyzed in search for common DE genes. The experimental dataset, identified 831 genes that were differentially expressed in all tumor samples, simultaneously. Moreover, 33 genes were up-regulated and 85 genes were down-regulated in all 10 BC samples compared to the 5 normal tissues, simultaneously. Hierarchical clustering partitioned tumor groups in accordance to their histology. K-means clustering of all genes and all samples, as well as clustering of tumor groups, presented 49 clusters. K-means clustering of common DE genes in all samples revealed 24 clusters. Genes manifested various differential patterns of expression, based on PCA. YY1 and NFκB were among the most common transcription factors that regulated the expression of the identified DE genes. Chromosome 1 contained 32 DE genes, followed by chromosomes 2 and 11, which contained 25 and 23 DE genes, respectively. Chromosome 21 had the least number of DE genes. GO analysis revealed the prevalence of transport and binding genes in the common down-regulated DE genes; the prevalence of RNA metabolism and processing genes in the up-regulated DE genes; as well as the prevalence of genes responsible for cell communication and signal transduction in the DE genes that were down-regulated in T1-Grade III tumors and up-regulated in T2/T3-Grade III tumors. Combination of all available samples revealed 17 common genes (BMP4, CRYGD, DBH, GJB1, KRT83, MPZ, NHLH1, TACR3, ACTC1, MFAP4, SPARCL1, TAGLN, TPM2, CDC20, LHCGR, TM9SF1 and HCCS) four of which participate in numerous pathways. Conclusions/Significance: The identification of the common DE genes among BC samples of different histology can provide further insight into the discovery of new putative markers. Oligos microarray chips (~57k genes) were obtained from GE HealthCare (IL) and AppliedMicroarrays (MA) (former Amersham Biosciences) (CodeLink 57k Human Whole Genome). Hybridization was performed with the CodeLink RNA amplification and Labeling kit as described by the manufacturer, utilizing the Cy5 fluorescent dye. Slides were scanned with a microarray scanner (ScanArray 4000XL). Images were generated with ScanArray microarray acquisition software (GSI Lumonics, USA). cRNAs from three experimental setups were used in single experiments with internal spikes as controls. The experimental setups consisted of 10 urinary BC samples of different histology and 5 control samples. The scanned images were further processed with the CodeLink Expression Analysis Software v5.0 from Amersham Biosciences (presently GE Health Care Inc.). The experimental setup was analyzed based on the reference-design as described previously. All tumor samples were compared against the mean value of the control samples. Raw microarray data are available as supplementary data and at the GEO microarray database. All microarray data are MIAME compliant. We used the following publicly available microarray datasets in our analysis: 1) GSE89 dataset (GDS183), comprised of 40 BC samples; 2) GSE3167 dataset (GDS1479), comprised of 60 samples (9 controls and 51 BC samples); 3) GSE7476 dataset, composed of 12 samples (3 controls and 9 BC samples) and 4) GSE12630 dataset, comprised of 19 BC samples. In total, our pooled microarray analysis was composed of 17 control samples (n=5, for the CodeLink platform; and n=12, for the rest microarray platforms) and 129 BC samples (n=10, for the CodeLink platform; and n=119, for the rest microarray platforms). Public data were used in their available normalized form, since background correction and normalization had already been performed.

Dataset Information

Whole genome expression in meningioma

Publications

Genome wide expression profiling identifies specific deregulated pathways in meningioma.

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