Metabolomics,Unknown,Transcriptomics,Genomics,Proteomics

Dataset Information

Genotyping of human morphologically challenging cases of renal epithelial neoplasms by virtual karyotypes

ABSTRACT: Renal tumors with complex morphology require extensive workup for accurate classification. Chromosomal aberrations that define subtypes of renal epithelial neoplasms have been reported. We explored if whole-genome chromosome copy number and loss-of-heterozygosity analysis with single nucleotide polymorphism (SNP) arrays can be used to identify these aberrations in cases where morphology was unable to definitively classify these tumors. Experiment Overall Design: We analyzed 50 paraffin-embedded tissues representing tumors with classic morphology from clear cell renal cell carcinoma (RCC), papillary RCC, chromophobe RCC, and oncocytoma with Affymetrix GeneChip 10K 2.0 Mapping arrays. We then analyzed 25 tumors that could not be classified by morphology and classified them based on the presence of defining chromosomal anomalies identified in the "classic" cohort.

ORGANISM(S): Homo sapiens

SUBMITTER: Federico Monzon

PROVIDER: E-GEOD-11447 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

ACCESS DATA

Similar Datasets

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.

Dataset Information

Genotyping of human morphologically challenging cases of renal epithelial neoplasms by virtual karyotypes

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure