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Logo of bmccancBioMed Centralsearchsubmit a manuscriptregisterthis articleBMC Cancer
BMC Cancer. 2012; 12: 310.
Published online Jul 23, 2012. doi:  10.1186/1471-2407-12-310
PMCID: PMC3488567
Integrative genome-wide expression profiling identifies three distinct molecular subgroups of renal cell carcinoma with different patient outcome
Manfred Beleut,corresponding author1,5 Philip Zimmermann,2 Michael Baudis,3 Nicole Bruni,4 Peter Bühlmann,4 Oliver Laule,2 Van-Duc Luu,1 Wilhelm Gruissem,2 Peter Schraml,corresponding author1 and Holger Moch1
1Institute of Surgical Pathology, University Hospital Zurich, Schmelzbergstrasse 12, 8091, Zurich, Switzerland
2Department of Biology, ETH Zurich, Universitätstrasse 2, 8092, Zurich, Switzerland
3Institute of Molecular Life Sciences, University of Zurich, Winterthurerstrasse 190, 8057, Zurich, Switzerland
4Seminar for Statistics, ETH Zurich, Rämistrasse 101, 8092, Zurich, Switzerland
5PAREQ Research AG, Wagistrasse 14, 8952, Schlieren, Switzerland
corresponding authorCorresponding author.
Manfred Beleut: manfred.beleut/at/; Philip Zimmermann: phz/at/; Michael Baudis: mbaudis/at/; Nicole Bruni: Nbruni/at/; Peter Bühlmann: buehlmann/at/; Oliver Laule: ola/at/; Van-Duc Luu: Vanducluu/at/; Wilhelm Gruissem: wgruissem/at/; Peter Schraml: Peter.Schraml/at/; Holger Moch: Holger.Moch/at/
Received January 12, 2012; Accepted June 26, 2012.
Renal cell carcinoma (RCC) is characterized by a number of diverse molecular aberrations that differ among individuals. Recent approaches to molecularly classify RCC were based on clinical, pathological as well as on single molecular parameters. As a consequence, gene expression patterns reflecting the sum of genetic aberrations in individual tumors may not have been recognized. In an attempt to uncover such molecular features in RCC, we used a novel, unbiased and integrative approach.
We integrated gene expression data from 97 primary RCC of different pathologic parameters, 15 RCC metastases as well as 34 cancer cell lines for two-way nonsupervised hierarchical clustering using gene groups suggested by the PANTHER Classification System. We depicted the genomic landscape of the resulted tumor groups by means of Single Nuclear Polymorphism (SNP) technology. Finally, the achieved results were immunohistochemically analyzed using a tissue microarray (TMA) composed of 254 RCC.
We found robust, genome wide expression signatures, which split RCC into three distinct molecular subgroups. These groups remained stable even if randomly selected gene sets were clustered. Notably, the pattern obtained from RCC cell lines was clearly distinguishable from that of primary tumors. SNP array analysis demonstrated differing frequencies of chromosomal copy number alterations among RCC subgroups. TMA analysis with group-specific markers showed a prognostic significance of the different groups.
We propose the existence of characteristic and histologically independent genome-wide expression outputs in RCC with potential biological and clinical relevance.
Keywords: DNA-microarray, SNP-array, RCC subgroups, Tissue microarray, Outcome
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