Homo sapiens
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA133357 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:Expression data from RCC cell lines to study metastasis progression
| PRJNA133355 | ENA
Project description:Array comparative genomic hybridization data from RCC cell lines to study metastasis progression
| PRJNA133127 | ENA
Project description:Study the role of miRNAs in metastasis progression of RCC cell lines
| PRJNA133133 | ENA
Project description:In order to address the progression, metastasis, and clinical heterogeneity of renal cell cancer (RCC), transcriptional profiling with oligonucleotide microarrays (22,283 genes) was done on 49 RCC tumors, 20 non-RCC renal tumors, and 23 normal kidney samples. Samples were clustered based on gene expression profiles and specific gene sets for each renal tumor type were identified. Gene expression was correlated to disease progression and a metastasis gene signature was derived. Gene signatures were identified for each tumor type with 100% accuracy. Differentially expressed genes during early tumor formation and tumor progression to metastatic RCC were found. Subsets of these genes code for secreted proteins and membrane receptors and are both potential therapeutic or diagnostic targets. A gene pattern ("metastatic signature") derived from primary tumors was very accurate in classifying tumors with and without metastases at the time of surgery. A previously described "global" metastatic signature derived by another group from various non-RCC tumors was validated in RCC. Unlike previous studies, we describe highly accurate and externally validated gene signatures for RCC subtypes and other renal tumors. Interestingly, the gene expression of primary tumors provides us information about the metastatic status in the respective patients and has the potential, if prospectively validated, to enrich the armamentarium of diagnostic tests in RCC. We validated in RCC, for the first time, a previously described metastatic signature and further showed the feasibility of applying a gene signature across different microarray platforms. Transcriptional profiling allows a better appreciation of the molecular and clinical heterogeneity in RCC. We used the following tissue samples to obtain transcriptional profiling of kidney tumors using Affymetrix HGU-133A chips: 23 Normal, 32 clear cell RCC (cRCC), 11 papillary RCC (pRCC), 6 chromophobe RCC (chrRCC), 12 Oncocytoma (OC), and 8 transitional cell carcinoma (TCC). The supplementary file 'GSE15641_mas5_data.txt' contains MAS5 signal values for the Samples included in Series GSE15641. This dataset is part of the TransQST collection.
2009-04-28 | E-GEOD-15641 | biostudies-arrayexpress
Project description:In order to address the progression, metastasis, and clinical heterogeneity of renal cell cancer (RCC), transcriptional profiling with oligonucleotide microarrays (22,283 genes) was done on 49 RCC tumors, 20 non-RCC renal tumors, and 23 normal kidney samples. Samples were clustered based on gene expression profiles and specific gene sets for each renal tumor type were identified. Gene expression was correlated to disease progression and a metastasis gene signature was derived. Gene signatures were identified for each tumor type with 100% accuracy. Differentially expressed genes during early tumor formation and tumor progression to metastatic RCC were found. Subsets of these genes code for secreted proteins and membrane receptors and are both potential therapeutic or diagnostic targets. A gene pattern ("metastatic signature") derived from primary tumors was very accurate in classifying tumors with and without metastases at the time of surgery. A previously described "global" metastatic signature derived by another group from various non-RCC tumors was validated in RCC. Unlike previous studies, we describe highly accurate and externally validated gene signatures for RCC subtypes and other renal tumors. Interestingly, the gene expression of primary tumors provides us information about the metastatic status in the respective patients and has the potential, if prospectively validated, to enrich the armamentarium of diagnostic tests in RCC. We validated in RCC, for the first time, a previously described metastatic signature and further showed the feasibility of applying a gene signature across different microarray platforms. Transcriptional profiling allows a better appreciation of the molecular and clinical heterogeneity in RCC.
2009-04-28 | GSE15641 | GEO
Project description:In this study, we characterize the fusion protein produced by the EPC1-PHF1 translocation in Low Grade Endometrial Stromal Sarcoma (LG-ESS) and Ossifying FibroMyxoid Tumors (OFMT). We express the fusion protein and necessary controls in K562 Cells. The fusion protein assembles a mega-complex harboring both NuA4/TIP60 and PRC2 subunits and enzymatic activities and leads to mislocalization of chromatin marks in the genome, linked to aberrant gene expression.
2020-12-03 | GSE162543 | GEO
Project description: Not available
| S-EPMC2531291 | biostudies-literature
Project description:RNA extracted at 240min. Samples are rRNA depleted. Expression measurement of Homo sapiens genes.
2019-07-31 | GSE114179 | GEO
Project description:In this study, we characterize the fusion protein produced by the EPC1-PHF1 translocation in Low Grade Endometrial Stromal Sarcoma (LG-ESS) and Ossifying FibroMyxoid Tumors (OFMT). We express the fusion protein and necessary controls in K562 Cells. The fusion protein assembles a mega-complex harboring both NuA4/TIP60 and PRC2 subunits and enzymatic activities and leads to mislocalization of chromatin marks in the genome, linked to aberrant gene expression.
2020-11-25 | GSE162118 | GEO