Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Expression data from RCC paired tumors to study metastasis progression

ABSTRACT: 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.

ORGANISM(S): Homo sapiens

PROVIDER: GSE23629 | GEO | 2010/08/15

SECONDARY ACCESSION(S): PRJNA133357

REPOSITORIES: GEO

ACCESS DATA

Json Xml

Similar Datasets

Expression data from RCC paired tumors to study metastasis progression

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

Expression data from RCC cell lines to study metastasis progression

2010-08-15 | GSE23627 | GEO

Expression data from RCC cell lines to study metastasis progression

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

Array comparative genomic hybridization data from RCC cell lines to study metastasis progression

2010-08-17 | GSE23633 | GEO

Array comparative genomic hybridization data from RCC cell lines to study metastasis progression

2010-08-17 | E-GEOD-23633 | biostudies-arrayexpress

Study the role of miRNAs in metastasis progression of RCC cell lines

2010-08-19 | GSE23690 | GEO

Study the role of miRNAs in metastasis progression of RCC cell lines

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

Expression data from RCC paired tumors to study metastasis progression

Project description: Not available

| S-DIXA-D-1068 | biostudies-other

Gene signatures of progression and metastasis in renal cell cancer

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

miRNA expression of Tubulocystic Renal Cell Carcinoma (TC-RCC) cases

Project description:Eleven TC-RCC cases were selected on the basis of the criteria defined by the 2012 Vancouver Classification of Renal Neoplasia and WHO 2016 classification criteria, and re-reviewed by a panel of expert renal pathologists. Two of these cases, TC-RCC#9 and #10, had a mixed tubulocystic and papillary histology and were macro-dissected into separate PRCC and TC-RCC components. The molecular basis of TC-RCC, and miRNA expression profile, is unknown. We therefore used Affymetrix miRNA v.2. arrays to elucidate the expression of these samples

2017-07-19 | GSE101563 | GEO

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data