Metabolomics,Unknown,Transcriptomics,Genomics,Proteomics

Dataset Information

Genotyping of human primary glioblastoma cell lines

ABSTRACT: Genomic abnormalities that are associated with mesecnhmal phenotype of GBM were detected in glioblastoma cell lines. Experiment Overall Design: Whole genomic DNA of GBM cell lines (W-98, D431, F-502, and S-496) and the fibroblast cell line were evaluated for allelic imbalances and chromosomal copy number abnormalities by using a highdensity single-nucleotide polymorphic (SNP) array analysis (Affymetrix XbaI 131 Array).

ORGANISM(S): Homo sapiens

SUBMITTER: Cho-Lea Tso

PROVIDER: E-GEOD-5082 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Publications

Primary glioblastomas express mesenchymal stem-like properties.

Tso Cho-Lea CL Shintaku Peter P Chen James J Liu Qinghai Q Liu Jason J Chen Zugen Z Yoshimoto Koji K Mischel Paul S PS Cloughesy Timothy F TF Liau Linda M LM Nelson Stanley F SF

Molecular cancer research : MCR 20060901 9

Glioblastoma is the most common and aggressive primary brain cancer. Recent isolation and characterization of brain tumor-initiating cells supports the concept that transformed neural stem cells may seed glioblastoma. We previously identified a wide array of mesenchymal tissue transcripts overexpressed in a broad set of primary glioblastoma (de novo) tumors but not in secondary glioblastoma (derived from lower-grade) tumors, low-grade astrocytomas, or normal brain tissues. Here, we extend this o ...[more]

PMID: 16966431

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Project description:A SNP microarray and FISH-based procedure to detect allelic imbalances in multiple myeloma: an integrated genomics approach reveals a wide dosage effect on gene and microRNA expression Multiple myeloma (MM) is characterized by marked genomic instability. Beyond structural rearrangements, a relevant role in its biology is represented by allelic imbalances leading to significant variations in ploidy status. To better elucidate the genomic complexity of MM, we analyzed a panel of 45 patients using combined FISH and microarray approaches. Using a self-developed procedure to infer exact local copy numbers for each sample, we identified a significant fraction of patients showing marked aneuploidy. A conventional clustering analysis showed that aneuploidy, chromosome 1 alterations, hyperdiploidy and recursive deletions at 1p and chromosomes 13, 14 and 22 were the main aberrations driving samples grouping. Then, we integrated mapping information with gene and microRNAs expression profiles: a multiclass analysis of the identified clusters showed a marked gene-dosage effect, particularly concerning 1q transcripts, also confirmed by correlating gene expression levels and local copy number alterations. A wide dosage effect affected also microRNAs, indicating that structural abnormalities in MM closely reflect in their expression imbalances. Finally, we identified several loci in which genes and microRNAs expression correlated with loss-of-heterozygosity occurrence. Our results provide insights into the composite network linking genome structure and gene/microRNA transcriptional features in MM. Keywords: Integrated genomics approach based on SNP microarray and FISH procedures to detect allelic imbalances in multiple myeloma. Pathological bone marrow specimens from 41 MM and four plasma cell leukemia (PCL) patients at diagnosis. 250 nanograms of genomic DNA was processed and, in accordance with the manufacturer's protocols, 40 micrograms of fragmented biotin-labelled DNA were hybridized on GeneChip Human Mapping 50K XbaI Arrays (Affymetrix Inc.). The arrays were scanned using the GeneChip Scanner 3000 7G. The images were acquired using Affymetrix GeneChip® Operating Software (GCOS version 1.4). Copy number values for individual SNPs were extracted and converted from CEL files into signal intensities using GTYPE 4.1 and Affymetrix Copy Number Analysis Tool (CNAT 4.0.1) softwares. Genomic Smoothing analysis was performed by using the smoothing window of 0 Mb, and inferred copy number states were derived from a Hidden Markov Model (HMM) based algorithm implemented in CNAT 4.0.1. Circular Binary Segmentation (Ohlsen et al., 2004) was applied using DNAcopy package for R Bioconductor on raw data. FBN procedure was finally applied to infer exact local copy number as described in the mentioned Reference.

Dataset Information

Genotyping of human primary glioblastoma cell lines

Publications

Primary glioblastomas express mesenchymal stem-like properties.

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