Copy number analysis of high-grade osteosarcoma
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE33153 | GEO | 2012/03/28
SECONDARY ACCESSION(S): PRJNA156639
REPOSITORIES: GEO
Similar Datasets
Project description:We performed copy number analysis of high-grade osteosarcoma samples. in order to detect osteosarcoma drivers, we integrated these data with genome-wide gene expression data. We performed two different methods - a non-paired and a paired integrative analysis. Copy number analysis was performed on 32 pre-treatment high-grade osteosarcoma diagnostic biopsies, of which 29 overlapped with the 84 samples used for gene expression analyses.
2012-03-27 | E-GEOD-33153 | biostudies-arrayexpress
Project description:We performed genome-wide gene expression data of high-grade osteosarcoma samples. In order to detect osteosarcoma drivers, we integrated these data with copy number data. We performed two different methods - a non-paired and a paired integrative analysis.
2012-03-28 | GSE33382 | GEO
Project description:We performed genome-wide gene expression data of high-grade osteosarcoma samples. In order to detect osteosarcoma drivers, we integrated these data with copy number data. We performed two different methods - a non-paired and a paired integrative analysis. Genome-wide gene expression analysis was performed on 84 pre-treatment high-grade osteosarcoma diagnostic biopsies, of which 29 overlapped with the 32 samples used for copy number analysis. Two different sets of control samples were used for comparison: osteoblasts (n=3) and mesenchymal stem cells (n=12, GEO accession number GSE28974).
2012-03-27 | E-GEOD-33382 | biostudies-arrayexpress
Project description:High-grade osteosarcoma is a tumor with a complex genomic profile, occurring primarily in adolescents with a second peak at middle age. The extensive genomic alterations obscure the identification of genes driving tumorigenesis during osteosarcoma development. In order to identify such driver genes, we integrated DNA copy number profiles (Affymetrix SNP 6.0) of 32 diagnostic biopsies with 84 expression profiles (Illumina Human-6 v2.0) of high-grade osteosarcoma as compared with its putative progenitor cells, i.e. mesenchymal stem cells (n=12) or osteoblasts (n=3). In addition, we performed paired analyses between copy number and expression profiles of a subset of 29 patients for which both DNA and mRNA profiles were available. Integrative analyses were performed in Nexus Copy Number software and statistical language R. Paired analyses were performed on all probes detecting significantly differentially expressed genes in corresponding LIMMA analyses. For both non-paired and paired analyses, copy number aberration frequency was set to >35%. Non-paired and paired integrative analyses resulted in 45 and 101 genes, respectively, which were present in both analyses using different control sets. Paired analyses detected >90% of all genes found with the corresponding non-paired analyses. Remarkably, approximately twice as many genes as found in the corresponding non-paired analyses were detected. Affected genes were intersected with differentially expressed genes in osteosarcoma cell lines, resulting in 31 new osteosarcoma driver genes. Cell division related genes, such as MCM4 and LATS2, were overrepresented and genomic-instability was predictive for metastasis-free survival, suggesting that deregulation of the cell cycle is a driver of osteosarcomagenesis. This SuperSeries is composed of the SubSeries listed below.
2012-03-28 | GSE33383 | GEO
Project description:High-grade osteosarcoma is a tumor with a complex genomic profile, occurring primarily in adolescents with a second peak at middle age. The extensive genomic alterations obscure the identification of genes driving tumorigenesis during osteosarcoma development. In order to identify such driver genes, we integrated DNA copy number profiles (Affymetrix SNP 6.0) of 32 diagnostic biopsies with 84 expression profiles (Illumina Human-6 v2.0) of high-grade osteosarcoma as compared with its putative progenitor cells, i.e. mesenchymal stem cells (n=12) or osteoblasts (n=3). In addition, we performed paired analyses between copy number and expression profiles of a subset of 29 patients for which both DNA and mRNA profiles were available. Integrative analyses were performed in Nexus Copy Number software and statistical language R. Paired analyses were performed on all probes detecting significantly differentially expressed genes in corresponding LIMMA analyses. For both non-paired and paired analyses, copy number aberration frequency was set to >35%. Non-paired and paired integrative analyses resulted in 45 and 101 genes, respectively, which were present in both analyses using different control sets. Paired analyses detected >90% of all genes found with the corresponding non-paired analyses. Remarkably, approximately twice as many genes as found in the corresponding non-paired analyses were detected. Affected genes were intersected with differentially expressed genes in osteosarcoma cell lines, resulting in 31 new osteosarcoma driver genes. Cell division related genes, such as MCM4 and LATS2, were overrepresented and genomic-instability was predictive for metastasis-free survival, suggesting that deregulation of the cell cycle is a driver of osteosarcomagenesis. This SuperSeries is composed of the following subset Series: GSE28974: Genome-wide gene expression profiling of mesenchymal stem cells GSE33153: Copy number analysis of high-grade osteosarcoma GSE33382: Genome-wide gene expression analysis of high-grade osteosarcoma For data processing, we refer to the individual series. We performed both non-paired and paired integrative analyses on SNP and gene expression data. Non-paired integrative analysis was performed by importing lists of differentially expressed genes into the Copy Number module of Nexus software version 5 (BioDiscovery, CA). Based on the length of the gene list, Nexus software performs a Fisher's exact test in order to determine whether the number of differentially expressed genes in a specific region with a significant copy number alteration is larger than expected by chance. Genes present in such regions of copy number alteration with FDR-adjusted P-values (Q-bounds in Nexus software) < 0.05 were returned from this integrative analysis. Nexus software only reports genes which are both gained and overexpressed, or both deleted and downregulated. For the paired integrative analysis, copy number data of all autosomal overlapping genes between the copy number and gene expression arrays were exported from Nexus software, and converted into a binary file containing all genes with a gain (1) and no gain (0), and a similar binary file for losses. As in the non-paired integrative analysis, we did not apply any restrictions on the size of copy number alterations. Gene expression data of each probe for each sample were normalized against average gene expression of the corresponding probes over all control samples (either expression data from 12 MSCs, or from 3 osteoblasts). This was performed by subtracting the average expression of the control samples from the expression levels of the sample of interest, since these are log-transformed expression values. For both analyses, only genes that were significantly differentially expressed between the 84 osteosarcoma samples and the specific control set were analyzed, in order to make sure that no genes returned from the integrative analysis were not significantly differentially expressed. Subse quently, genes that overlapped between the copy number binary files and that matched the fold change of expression (upregulation for genes with gains, and downregulation for genes with losses) were returned.
2012-03-27 | E-GEOD-33383 | biostudies-arrayexpress
Project description:Osteosarcoma (OS) is a very aggressive bone tumor characterized by highly abnormal complex karyotypes.This a-CGH is a part of an expriment whose aim was to identify, genomic imbalance, DNA methylation and gene expression profiles in a panel osteosarcoma tumors. Keywords: comparative genomic hybridization
2009-09-08 | GSE12830 | GEO
Project description:We performed genome-wide gene expression data of high-grade osteosarcoma cell lines, as well as on mesenchymal stem cells, and osteoblasts, and performed global test analysis in order to determine the most significantly affected KEGG pathways. Genome-wide gene expression analysis was performed on 19 high-grade osteosarcoma cell lines. Significantly differentially expressed genes were determined between osteosarcoma cells and two different sets of control samples - osteoblasts [n=3, GEO accession number GSE33382] and mesenchymal stem cells [n=12, GEO accession number GSE28974]. Global test was applied to the different analyses, in order to determine the most affected signaling pathways in osteosarcoma cells.
2013-06-06 | E-GEOD-42351 | biostudies-arrayexpress
Project description:We performed genome-wide gene expression data of high-grade osteosarcoma cell lines, as well as on mesenchymal stem cells, and osteoblasts, and performed global test analysis in order to determine the most significantly affected KEGG pathways.
2013-06-06 | GSE42351 | GEO
Project description:Osteosarcoma is a rare, highly malignant tumor of the bone that presents with a highly complex and abnormal karyotype. Only a few of the genes, which are targets of genetic alteration are known. An integrated genome-wide genomic and gene expression profiling analysis was performed on human osteosarcoma tissues and osteosarcoma cell lines in order to identify and map, in a high-resolution fashion, the most drastic chromosomal changes and point out genes which could contribute to tumorigenesis by having altered expression levels of critical oncogenes and tumor suppressors. Combined gene expression and aCGH analysis on the same samples enables direct comparison between DNA and mRNA level and offers a general strategy to identify and prioritize potential targets while the parallel analysis on cell lines offers a reliable models for further functional studies.
2016-01-01 | GSE12789 | GEO
Project description:Osteosarcomas are the most common primary malignant tumours of bone, and almost all conventional osteosarcomas are high-grade tumours showing complex genomic aberrations. We have integrated genome-wide genetic and epigenetic profiles from the EuroBoNeT panel of 19 human osteosarcoma cell lines based on microarray technologies. The cell lines showed complex patterns of DNA copy number changes, where copy number gains were significantly associated with gene-rich regions of the genome and losses with gene-poor areas. Integration of the datasets showed that the mRNA levels were regulated by either alterations in DNA copy number or DNA methylation. Using a recurrence threshold of 6/19 (> 30 %) cell lines, 348 genes were identified as having alterations of two data types (gain or hypo-methylation/over-expression, loss or hyper-methylation/under-expression). These genes are involved in embryonic skeletal system development and morphogenesis, as well as remodelling of extracellular matrix. Several genes were hyper-methylated and under-expressed compared to normal osteoblasts, and expression could be reactivated by demethylation using 5-Aza-2M-bM-^@M-^Y-deoxycytidine treatment for all four genes tested. Globally, there was as expected a significant positive association between gain and over-expression, loss and under-expression as well as hyper-methylation and under-expression, but gain was also associated with hyper-methylation and under-expression, suggesting that hyper-methylation may oppose the effects of increased copy number for some genes. Integrative analysis of genome-wide genetic and epigenetic alterations identified mechanistic dependencies and relationships between DNA copy number and DNA methylation in terms of regulating mRNA expression levels in osteosarcomas, contributing to better understanding of osteosarcoma biology. Comparison of DNA copy number changes in 19 osteosarcoma cell lines
2012-12-06 | E-GEOD-36003 | biostudies-arrayexpress