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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

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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.

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Project description:Recent large-scale transcriptome analyses have yielded large numbers of transcripts, including long non-coding RNAs (lncRNAs) which were found aberrant in various diseases, especially in cancers. But few lncRNAs that may be involved in renal cell carcinoma (RCC) haven't yet been reported.So we aimed to reveal the expression pattern of lncRNAs in 5 paired RCC tumor samples and the matched adjacent normal tissues by microarray.

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Identification of RCC subtype-specific microRNAs – meta-analysis of high-throughput RCC tumor microRNA expression data [miRNA-Seq]

Project description:Renal cell carcinoma (RCC) is one of the most common cancers worldwide with nearly non-symptomatic course till advanced stage of disease. RCC can be distinguished into three subtypes: papillary (pRCC), chromophobe (chRCC) and clear cell renal cell carcinoma (ccRCC) representing up to 75% of all RCC cases. Detection and RCC monitoring tools are limited to standard imaging techniques, in combination with non-RCC specific morphological and biochemical read-outs. RCC subtype identification relays mainly on results of pathological examination of tumor slides. Molecular, clinically applicable and ideally non-invasive tools aiding RCC management are still non-existent, although molecular characterization of RCC is relatively advanced. Hence many research efforts concentrate on identification of molecular markers that will assist with RCC sub-classification and monitoring. Due to stability and tissue-specificity miRNAs are promising candidates for such biomarkers. Here we performed a meta-analysis study, utilized seven available NGS and seven microarray RCC studies in order to identify subtype-specific expression of miRNAs. We concentrated on four potentially oncocytoma-specific miRNAs (miRNA-424-5p, miRNA-146b-5p, miRNA-183-5p, miRNA-218-5p), two pRCC (miRNA-127-3p, miRNA-139-5p) and eight ccRCC specific miRNAs (miRNA-200c-3p, miRNA-362-5p, miRNA-363-3p and miRNA-204-5p, 21-5p, miRNA-224-5p, miRNA-155-5p, miRNA-210-3p) and validated their expression in an independent sample set. Additionally, we found ccRCC-specific miRNAs to be differentially expressed in ccRCC Fuhrman grades and identified alterations in their isoform composition in tumor tissue. Our results revealed that changes in expression of selected miRNA might be potentially utilized as a tool aiding ccRCC subclass discrimination and propose a miRNA panel aiding RCC subtype distinction.

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