Open Access Highly Accessed Research article

Integrative genome-wide expression profiling identifies three distinct molecular subgroups of renal cell carcinoma with different patient outcome

Manfred Beleut15*, Philip Zimmermann2, Michael Baudis3, Nicole Bruni4, Peter Bühlmann4, Oliver Laule2, Van-Duc Luu1, Wilhelm Gruissem2, Peter Schraml1* and Holger Moch1

Author Affiliations

1 Institute of Surgical Pathology, University Hospital Zurich, Schmelzbergstrasse 12, 8091, Zurich, Switzerland

2 Department of Biology, ETH Zurich, Universitätstrasse 2, 8092, Zurich, Switzerland

3 Institute of Molecular Life Sciences, University of Zurich, Winterthurerstrasse 190, 8057, Zurich, Switzerland

4 Seminar for Statistics, ETH Zurich, Rämistrasse 101, 8092, Zurich, Switzerland

5 PAREQ Research AG, Wagistrasse 14, 8952, Schlieren, Switzerland

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BMC Cancer 2012, 12:310  doi:10.1186/1471-2407-12-310

Published: 23 July 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.

DNA-microarray; SNP-array; RCC subgroups; Tissue microarray; Outcome