Gene expression profiling provides a potent universal tool for improved molecular diagnosis and prognostic evaluation. This tool should be carefully handled in order to avoid data misinterpretation, wrong conclusions and generalizations. Here, we have demonstrated a way to identify a set of 18 genes that discriminate between the three major types of RCC, providing candidates for a molecular differential diagnosis.

The establishment of a diagnostic tool requires a robust platform, which is easy to handle, highly sensitive and specific. Whether or not microarrays will eventually enter the field of diagnostic applications remains open. However, microarrays are perfectly able to detect gene expression patterns that can subsequently be exploited for diagnosis using different platform technologies. For example, it is conceivable that array-based analyses lead to the identification of a set of highly diagnostic genes that are then used in diagnostic routines using other (e.g. RT-PCR-based) methods. To achieve this the number of target genes needs to be reduced, and highly specific gene expression patterns between the tissue types need to be identified.

We are aware of the fact that the current histopathology-based kidney tumor diagnosis is robust, and molecular methods are not likely to replace the clinical routine diagnosis of kidney cancer in the near future. However, in contrast to routine histopathology, microarray data yield important insights into the molecular differences between kidney tumors: Gene expression patterns can not only confirm current diagnostic procedures, but at the same time reveal further highly promising target genes for a more specific therapy directed against the different kidney tumor types. The prerequisites to achieve this aim are the "druggability" of the targets and the availability of highly specific compounds with low degrees of side effects. To this end, more effort is urgently needed to systematically exploit the huge amount of gene expression data for the therapy of cancer patients. The application of genome-wide microarrays for RCC studies may yield additional genes and ESTs that could be used for the identification of previously unrecognized kidney tumor subgroups and novel therapeutic targets.

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