Study the role of miRNAs in metastasis progression of RCC cell lines
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ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE23690 | GEO | 2010/08/19
SECONDARY ACCESSION(S): PRJNA133133
REPOSITORIES: GEO
Similar Datasets
Project description:This study investigates for the miRNAs that drive or regulates Renal Cell Carcinoma (RCC) metastatic progression using two different RCC cell lines, the metastatic (LM2 and LM1) and non-metastatic (SN12C). The experiment is designed with three RCC cell lines one non-metastatic (SN12C) and two metastatic (LM1 and LM2). Each cell line has two biological replicates (exception SN12C has 3 replicates)
2010-08-19 | E-GEOD-23690 | biostudies-arrayexpress
Project description:This study investigates the molecular signatures that drive Renal Cell Carcinoma (RCC) metastatic conversion using the metastatic (LM2) and non-metastatic (SN12C) RCC cell lines.
2010-08-17 | GSE23633 | GEO
Project description:This study investigates the molecular signatures that drive Renal Cell Carcinoma (RCC) metastatic conversion using the metastatic (LM2) and non-metastatic (SN12C) RCC cell lines. We used microarrays to detail the global programme of gene expression underlying cellularisation and identified distinct classes of up-regulated genes during this process. To identify differences in gene expression associated with increasing metastatic activity, we subjected the three RCC cell types, SN12C, LM1, LM2 to gene expression profiling.
2010-08-15 | E-GEOD-23627 | biostudies-arrayexpress
Project description:This study investigates the molecular signatures that drive Renal Cell Carcinoma (RCC) metastatic conversion using the metastatic (LM2) and non-metastatic (SN12C) RCC cell lines. We used microarrays to detail the global programme of gene expression underlying cellularisation and identified distinct classes of up-regulated genes during this process.
2010-08-15 | GSE23627 | GEO
Project description:This study investigates the molecular signatures that drive Renal Cell Carcinoma (RCC) metastatic conversion using the metastatic (LM2) and non-metastatic (SN12C) RCC cell lines. Two-condition experiment, RCC Cell lines vs. Reference human genome DNA from normal male.
2010-08-17 | E-GEOD-23633 | biostudies-arrayexpress
Project description:This study investigates the molecular signatures that drive Renal Cell Carcinoma (RCC) metastatic conversion using the 16 paired Human tumor samples. We used microarrays to detail the global programme of gene expression underlying cellularisation and identified distinct classes of up-regulated genes during this process.
2010-08-15 | GSE23629 | GEO
Project description:This study investigates the molecular signatures that drive Renal Cell Carcinoma (RCC) metastatic conversion using the 16 paired Human tumor samples. We used microarrays to detail the global programme of gene expression underlying cellularisation and identified distinct classes of up-regulated genes during this process. To identify differences in gene expression associated with increasing metastatic activity, we subjected 16 paired human tumor samples to gene expression profiling.
2010-08-15 | E-GEOD-23629 | 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:The leading cause of death in human patients with metastatic renal cell carcinoma (RCC) and malignant cancer in general is the dissemination of the primary tumor to secondary sites. The mechanisms by which RCC colonize the lung microenvironment during metastasis remain largely unknown. To investigate the mechanisms of lung colonization by tumor cells, we grafted human RCC cells with different lung metastatic activities in mice. Gene expression profiling of the mouse lung stromal compartment revealed a gene signature enriched for neutrophil-specific functions, induced preferentially by poorly metastatic cells. Analysis of the gene expression patterns in tumor cells and clinical specimens showed an inverse correlation between metastatic activity and the levels of a number of chemokines, including CXL5 ad IL8. Enforced depletion of CXCL5 and IL8 in tumor cells allowed us to establish a functional link between lung neutrophil infiltration, the secretion of chemokines by cancer cells and metastatic activity. Finally, we showed that human neutrophils displayed a higher cytotoxic activity toward poorly metastatic cells relative to highly metastatic cells. Together, these results support a model in which neutrophils recruited to the lung by tumor-secreted chemokines build an antimetastatic barrier and loss of those neutrophil chemokines in tumor cells is a critical rate-limiting step during lung metastatic seeding. Total 12 samples are analysed from two cell lines (SN12C and LM2) and Normal tissue. There are four biological replicates for each cell line and four biological replicates from normal lung tissue (as reference).
2012-05-29 | E-GEOD-29688 | 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. 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