Expression profiling of metastatic renal cell carcinoma using gene set enrichment analysis

Objective: To identify complex changes in cell biology occurring during metastatic progression of renal cell carcinoma using a novel gene expression analysis algorithm.

Methods: Whole genome expression profiling was carried out on 32 snap-frozen samples of clear-cell renal cell carcinoma metastases, 29 primary tumors (14 low grade, 15 high grade) and 14 samples of normal kidney tissue using oligonucleotide microarrays. These data were analyzed with the gene set enrichment analysis method, which is able to detect even small, but significant, expression changes in functionally connected genes that cannot be shown by gene-by-gene comparisons.

Results: There were 95 gene sets (pathways) with significant upregulation in metastases compared with normal kidney tissue (P < 0.01), and 77 gene sets with significant downregulation, respectively. Low-grade and high-grade tumors showed deregulation of various pathways that have previously not been described in renal cell carcinoma. There were significant changes of genes involved in cell cycle control, apoptosis, cell motility, metabolism, cell adhesion and cytoskeleton. Some promising new potential therapy targets were identified in renal cell carcinoma metastases; for example, aurora-kinase A and flap structure-specific endonuclease 1.

Conclusion: Expression profiling of metastatic renal cell carcinoma using the gene set enrichment analysis pathway analysis method provides new and detailed insights in alterations occurring in renal cell carcinoma during malignant transformation and progression. These data can help to develop new and specifically targeted renal cell carcinoma therapies.