Transcription profiling of renal cell carcinoma.

Deutsches Krebsforschungszentrum, Heidelberg.
Verhandlungen der Deutschen Gesellschaft für Pathologie 02/2002; 86:153-64.
Source: PubMed


Our aim was to prepare a comprehensive catalogue of the changes in gene expression accompanying the development and progression of renal cell carcinoma, and to correlate these with histo-pathological, cytogenetic and clinical findings.
mRNA samples from paired neoplastic and non-cancerous human kidney tissue were labeled and hybridized in duplicate against high-density cDNA arrays. Two array technologies were used: 31,500-element transcriptome-wide nylon arrays for hybridization with 37 radioactively labelled sample pairs, and 4200-element kidney- and cancer-specific glass microarrays for hybridization with 19 fluorescently labelled sample pairs.
We identified more than 1700 cDNA clones that show differential transcription levels in kidney tumor tissue compared to normal kidney tissue. The functional classification of 389 annotated genes provided views of the changes in the activities of specific biological processes in renal cancer. Among the biological processes with a large proportion of up-regulated genes we found cell adhesion, signal transduction, and nucleotide metabolism. Down-regulated processes included small molecule transport, ion homeostasis, and oxygen and radical metabolism. Furthermore, we explored the feasibility of molecular diagnosis for renal cell tumors using cDNA microarrays on glass slides, investigating the association of transcription levels with tumor type, progression, and a putative prognostic variable. The experimental data is available from the GEO gene expression database (; accession no. GSE3), and a comprehensive presentation of the results is available in the web supplement (
Transcription profiling using high-density cDNA arrays is a powerful method with the potential to improve cancer diagnosis and prognosis. The identification and classification of differentially transcribed genes, as described in our study, is the beginning of a more complete understanding of kidney cancer.

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    • "The Kidney and Dilution data sets available in the vsn and affydata Bioconductor packages (Huber et al., 2002b; Bioconductor, 2006) were selected for the analysis. The Kidney data set is a cDNA slide with 8704 spots of two adjacent tissue samples from a nephrectomy, published by Huber et al. (2002a). For each spot, a background estimate from a surrounding region was subtracted. "
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