Beyond the Abstract - Putative biomarker genes for grading clear cell renal cell carcinoma, by Matthias Maruschke, MD, et al

BERKELEY, CA ( - Renal cell carcinoma (RCC) is the most common renal malignancy showing an increasing incidence with the highest mortality of urogenital malignancies.[1]

Clear cell RCC (ccRCC), the RCC subtype with the highest rate of progression and mortality, accounts for 70-80% of all RCCs.[2]

Currently, various targeted RCC combination therapies studied in clinical trials and in routine application encounter the perspective to assign RCC tumour subtypes to the treatment regime most appropriate to the histopathology and molecular nature of the individual tumour to be treated.[3,4] The aim of this study was to identify gene sets in order to distinguish RNA expression levels from ccRCC histopathologies of grade 1 from grade 3. This analysis indicated the presence of genes involved in tumour progression from normal kidney to grade G1 and G3 ccRCC tumour formation. At present, these current data sets derived from analyzing 14 grade 1 (G1) ccRCC, 15 grade 3 (G3) ccRCC and 14 normal kidney tissues thereof are utilized to interrogate what individual gene expressions or a combination of aberrant expression signatures are capable of reliably discriminating aggressive and/or metastatic tumours of grade 3 (G3) from non-metastatic ccRCC tumours of grade 1 (G1).


Material and methods
Microarray Hybridization
Expression profiling was performed and with the oligonucleotide microarray Human Genome U133 Plus 2.0 Array (Affymetrix Corp., Santa Clara, CA, USA) that interrogates 47.000 transcripts using 54.000 probe sets in a genome-wide manner. Array hybridization was performed according to the supplier’s instructions using the “GeneChipR Expression 3`Amplifikation One-Cycle Target Labeling and Control reagents.”

Data Processing
The MAS5-algorithm gave probe set intensity values from the raw chip-level fluorescence data according to the standard data processing procedure (Affymetrix GeneChip Operating Software, GCOS, version 1.4). Final data extraction was done with the Data Mining Tool 3.1 (Affymetrix, Santa Clara, CA, USA).

To identify differentially expressed genes between the phenotypes G1 and G3 of ccRCC, the Event Ratio- (ER) -Algorithm developed by Aris et al. (2004) was employed.[5] This scheme accounts for both of the two noise sources inherent to gene expression profiling data, technical noise as well as biological noise. It allows narrow filtering of the expression data in order to drastically reduce the number of false positives in the resulting list of differentially expressed probe sets.

Cluster Analysis
Cluster analysis was done using the R/Bioconductor software packages. Hierarchical clusterings were conducted by selecting genes with an event-ratio score above 0.7 for the comparison of the G1 and G3 phenotypes, and an event-ratio score above 0.9 for the other phenotype combinations. For better scalability of the resulting heatmap displays, log2 of the data were used.

Gene expression results
Among the probe sets of differentially expressed genes, a number of potentially disease-relevant genes were found. Comparing G1 ccRCC and normal renal tissue (event-ratio score > 0.9), there were 158 genes up- and 340 down-regulated. Comparing G3 ccRCC with normal renal tissue, there were 124 genes up- and 412 down-regulated. Comparing G1 and G3 ccRCC, there were 24 genes up- and 49 down-regulated (event-ratio score > 0.7).

Hierarchical clustering of 73 probe sets derived from the comparison of G1 and G3 tumours by using the topmost ER scores provided relatively good separation of G1 and G3, reflecting the fact that the genes under consideration had been chosen for being different in these particular phenotypes. In particular, the data analysis was sufficiently robust to provide a reliably unanimous separation of G1 and G3. The data analysis was finally focused on investigating 24 gene sequences upregulated in G3-tumours in comparison to G1 ccRCC. After interrogating the Gene Cards database (, 12 candidate genes attracted our attention: Aldolase B (aldo B), CUB domain-containing protein1 (CDCP 1), aquaporin 9 (AQP 9), retinoic acid receptor responder (RARRES 1), solute carrier transporter family 12A8 (SLC12A8), melanotransferrin/P97 (MFI2), serum amyloid A1 (SAA1), interleukin-20RB (IL-20RB), interleukin receptor-2 (IL-2R2), PTHLH, ceruloplasmin (ferrooxidase, CP) and laminin (LAMB3).


The management of systemic renal cellular carcinoma (RCC) is limited in spite of great advances in the knowledge of molecular processes in tumour biology and new substances in “targeted” therapies. The background to search for molecular prognostic markers is driven by the objective to assign the most appropriate targeted therapy to each individual RCC patient.

The most informative candidate genes up- and down-regulated in ccRCC tissue have been selected by applying cross-comparison-analysis in conjunction with ER-scoring analysis of RNA expressions in G1-, G3-tumours and healthy renal tissue. The ER-scoring approach was taken to reduce technical and biological “noise” as well as the number of false positive candidates.

Sequences normally expressed in healthy renal tissue and down-regulated in tumour tissue decrease gradually from G1 to G3 or disappear completely. This feature possibly indicates a progressive loss coding DNA during the process of dedifferentiation to G3. On the other hand, the development of disease progression in ccRCC corresponded to an increasing expression of candidate genes for tumour progression, which are hardly expressed in healthy tissue, whose expression is slowly increased in G1-tumours and highly up-regulated in G3-tumours.

Of the 24 gene sequences we found up-regulated in G3 tissues there are several genes that encode for proteins with known roles in tumour metabolism, angiogenesis, migration, immunogenity and cell-cell interaction. While large quantities of data are being produced by genome-wide profiling and have yielded more insight into genetic alterations accompanying malignant transformation, the interpretation and understanding of pathophysiological relationships remains a huge challenge. However, the variance between different studies using microarray analysis makes reliable interpretation very difficult. A main problem in identifying candidate gene sets from raw data is the lack of a standard workflow. There is no defined let alone standardized bio-statistical method used; this would be needed in order to compare studies and results. We used ER-scoring to get a ranking list of a limited number of candidate genes providing the possibility of developing single markers for potential use in clinical diagnostic work-up in view of individual prognosis.

This study is the first to describe the up-regulation of CP on mRNA level in association with positive immunohistochemistry on protein level in ccRCC; more interesting is the differential positivity between G1 and G3 ccRCC. Thus, CP may have a potential role, in addition to other parameters, as a prognostic marker. However, further work is needed to validate our results and in order to establish potential utility in clinical settings.


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  4. Cella D, Michaelson MD, Bushmakin AG, et al: Health-related quality of life in patients with metastatic renal cell carcinoma treated with sunitinib vs interferon-alpha in a phase III trial: final results and geographical analysis. Br J Cancer. 2010 Feb 16;102(4):658-64. Epub 2010 Jan 26.
  5. Aris VM, Cody MJ, Cheng J, et al: Noise filtering and nonparametric analysis of microarray data underscores discriminating markers of oral, prostate, lung, ovarian and breast cancer. BMC Bioinformatics. 2004 Nov 29;5:185.


Written by:
M. Maruschke,1 D. Koczan,2 D. Reuter,2 B. Ziems,3 H. Nizze,4 O.W. Hakenberg,1 and H.-J. Thiesen2 as part of Beyond the Abstract on This initiative offers a method of publishing for the professional urology community. Authors are given an opportunity to expand on the circumstances, limitations etc... of their research by referencing the published abstract.

  1. Department of Urology, University of Rostock, Rostock, Germany,
  2. Institute of Immunology, University of Rostock, Rostock, Germany,
  3. Gesellschaft für Individualisierte Medizin mbH (IndyMed), Rostock, Germany
  4. Institute of Pathology, University of Rostock, Rostock, Germany,


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