Gene expression analysis in clear cell renal cell carcinoma using gene set enrichment analysis for biostatistical management

Abstract

We analyzed microarray data of gene expression in ccRCC comparing poorly differentiated and well differentiated tumour tissue samples. Using GSEA, we found a number of genes set candidates relevant to biological network processes with high complexity; conspicuously, these comprised members of the interleukin- and chemokine-family, cyclin-dependent kinases, angiogenic growth factors and transcriptional factors. This suggests that, in poorly differentiated aggressive ccRCC, there may be a limited number of gene sets that are responsible for the very aggressive biological behaviour. This comparison performed at a gene set level enables the identification of such congruency between different gene sets and whole data sets with respect to a specific biological question. GSEA embedded in the statistical workflow procedure for the suitable preparation of expression data may improve the analysis and avoid missing changes at the molecular level. A systematic approach such as GSEA is clearly needed to analyze raw data from microarray analyses, although these data can only be descriptive and the mass of raw data is derived from a relatively small number of tissue samples. However, consistent alterations of gene expression found in specific tumour entities may allow a better understanding of certain aspects of specific tumour biology. Therefore, the molecular characterization of individual tumours may potentially be useful for the better individual assessment of prognosis and, finally, the identification of biomarkers and targets of specific treatments may eventually help to improve treatment.