12q amplification in liposarcoma
Ontology highlight
ABSTRACT:
PROVIDER: PRJEB64351 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| ERR11746200.fastq.gz | Fastqsanger.gz | |||
| ERR11746300.fastq.gz | Fastqsanger.gz | |||
| ERR11746400.fastq.gz | Fastqsanger.gz | |||
| ERR11746500.fastq.gz | Fastqsanger.gz | |||
| ERR11746600.fastq.gz | Fastqsanger.gz |
Items per page: 5 1 - 5 of 576 |
Similar Datasets
Project description:Dedifferentiated liposarcoma (DDLPS) is an aggressive mesenchymal cancer marked by amplification of MDM2, an inhibitor of the tumor suppressor TP53. DDLPS patients with higher MDM2 amplification have lower chemotherapy sensitivity and worse outcome than patients with lower MDM2 amplification. We hypothesized that MDM2 amplification levels may be associated with changes in DDLPS metabolism. Six patient-derived DDLPS cell line models were subject to comprehensive metabolomic (Metabolon) and lipidomic (SCIEX 5600 TripleTOF-MS) profiling to assess associations with MDM2 amplification and their responses to metabolic perturbations. Comparing metabolomic profiles between MDM2 higher and lower amplification cells yielded a total of 23 differentially abundant metabolites across both panels (FDR < 0.05, log2 FC < 0.75), including ceramides, glycosylated ceramides, and sphingomyelins. Disruption of lipid metabolism through statin administration resulted in a chemo-sensitive phenotype in MDM2 lower cell lines only, suggesting that lipid metabolism may be a large contributor to the more aggressive nature of MDM2 higher DDLPS tumors. This study is the first to provide comprehensive metabolomic and lipidomic characterization of DDLPS cell lines and provides evidence for MDM2-dependent differential molecular mechanisms that are critical factors in chemoresistance and could thus affect patient outcome.
2020-05-22 | ST001405 | MetabolomicsWorkbench
Project description:In order to identify new key molecules in the pathogenesis of myxoid liposarcoma, we performed comparative gene expression profiling in myxoid liposarcoma and fat tissue samples.
2014-10-28 | GSE62747 | GEO
Project description:Liposarcoma is the most common soft tissue sarcoma, accounting for about 20% of cases. Liposarcoma is classified into 5 histologic subtypes that fall into 3 biological groups characterized by specific genetic alterations. To identify genes that contribute to liposarcomagenesis and to better predict outcome for patients with the disease, we undertook expression profiling of liposarcoma. U133A expression profiling was performed on 140 primary liposarcoma samples, which were randomly split into training set (n=95) and test set (n=45). A multi-gene predictor for distant recurrence-free survival (DRFS) was developed using the supervised principal component method. Expression levels of the 588 genes in the predictor were used to calculate a risk score for each patient. In validation of the predictor in the test set, patients with low risk score had a 3-year DRFS of 83% vs. 45% for high risk score patients (P=0.001). The hazard ratio for high vs. low score, adjusted for histologic subtype, was 4.42 (95% confidence interval 1.26-15.55; P=0.021). The concordance probability for risk score was 0.732. Genes related to adipogenesis, DNA replication, mitosis, and spindle assembly checkpoint control were all highly represented in the multi-gene predictor. Three genes from the predictor, TOP2A, PTK7, and CHEK1, were found to be overexpressed in liposarcoma samples of all five subtypes and in liposarcoma cell lines. Knockdown of these genes in liposarcoma cell lines reduced proliferation and invasiveness and increased apoptosis. Thus, genes identified from this predictor appear to have roles in liposarcomagenesis and have promise as therapeutic targets. In addition, the multi-gene predictor will improve risk stratification for individual patients with liposarcoma.
2011-09-02 | GSE30929 | GEO
Project description:Liposarcoma is the most common soft tissue sarcoma, accounting for about 20% of cases. Liposarcoma is classified into 5 histologic subtypes that fall into 3 biological groups characterized by specific genetic alterations. To identify genes that contribute to liposarcomagenesis and to better predict outcome for patients with the disease, we undertook expression profiling of liposarcoma. U133A expression profiling was performed on 140 primary liposarcoma samples, which were randomly split into training set (n=95) and test set (n=45). A multi-gene predictor for distant recurrence-free survival (DRFS) was developed using the supervised principal component method. Expression levels of the 588 genes in the predictor were used to calculate a risk score for each patient. In validation of the predictor in the test set, patients with low risk score had a 3-year DRFS of 83% vs. 45% for high risk score patients (P=0.001). The hazard ratio for high vs. low score, adjusted for histologic subtype, was 4.42 (95% confidence interval 1.26-15.55; P=0.021). The concordance probability for risk score was 0.732. Genes related to adipogenesis, DNA replication, mitosis, and spindle assembly checkpoint control were all highly represented in the multi-gene predictor. Three genes from the predictor, TOP2A, PTK7, and CHEK1, were found to be overexpressed in liposarcoma samples of all five subtypes and in liposarcoma cell lines. Knockdown of these genes in liposarcoma cell lines reduced proliferation and invasiveness and increased apoptosis. Thus, genes identified from this predictor appear to have roles in liposarcomagenesis and have promise as therapeutic targets. In addition, the multi-gene predictor will improve risk stratification for individual patients with liposarcoma. 140 human liposarcoma specimens were profiled on Affymetrix U133A arrays per manufacturer's instructions.
2011-09-01 | E-GEOD-30929 | biostudies-arrayexpress
Project description:In order to identify new key molecules in the pathogenesis of myxoid liposarcoma, we performed comparative gene expression profiling in myxoid liposarcoma and fat tissue samples. Whole genome microarray analysis was performed on eight primary myxoid liposarcoma samples and an RNA pool of eight healthy fat tissue samples.
2014-10-28 | E-GEOD-62747 | biostudies-arrayexpress
Project description:The identification of genetic alterations in sarcoma may give precious clues for the diagnosis. The detection of MDM2 amplification has an important impact since it may orientate towards a dedifferentiated liposarcoma (DDLPS) or more rarely towards an intimal sarcoma. Here we describe a poorly differentiated sarcoma initially diagnosed as a DDLPS, mainly on the basis of FISH analysis that detected MDM2 amplification. The patient was treated by adjuvant radiotherapy and, when the tumor recurred a few years later, MDM2 amplification was not observed anymore. We report the clinical, immunohistological, genomic and molecular description of the primary tumors and his recurrences.
2022-04-08 | GSE200434 | GEO
Project description:miR-135b expression is higher in myxoid liposarcoma cell lines than in adipose-derived mesenchymal cell line, as well as in myxoid liposarcoma tumors than in adjacent normal prostate tissues.To further investigate the molecular mechanisms regulated by miR-135b, we performed mRNA microarray analysis of cell cultures from myxoid liposarcoma cell line after transfections with miR-135b mimic or negative control.
2017-02-09 | GSE73347 | GEO
Project description:Gene expression analysis of myxoid liposarcoma (MLS) tumors from five different patients
2022-04-08 | GSE167270 | GEO
Project description:miR-193b functions as a tumor suppressor in liposarcoma cells. Microarray was used to identify the targets of miR-193b. The functions of identified miR-193b targets were further investigated in liposarcoma cells.
2017-06-01 | GSE83690 | GEO
Project description:Myxoid liposarcoma (MLS) is the second most common type of liposarcoma, and today few model systems to study the disease exists. To be able to model the disease in vitro, cell-free scaffolds from MLS patient-derived xenograft (PDX) models were generated. The MLS scaffolds were then used as a 3D growth platform for MLS cell lines to study the cancer microenvironments impact on cellular heterogeneity using RNA sequencing. Key components in the microenvironment have also been shown to influence the fraction of cellular subpopulations, such as cancer stem cells and migratory cells but also to promote aggressive features of cancers. Therefore, to better understand and characterize these scaffolds, protein analyses were performed and links between scaffold compositions and the induced gene expression profiles of the cells grown therein could be made. This model system provides a better insight to the composition of the cell-free cancer microenvironment in a rare disease, which can lead to identification of novel malignancy inducing properties in MLS.
2024-05-08 | PXD023792 | Pride