BJU Int. 2011 Mar 16;
Maruschke M, Reuter D, Koczan D, Hakenberg OW, Thiesen HJ

OBJECTIVE: To improve the workflow for standardizing the statistical interpretation provides an chance for the analysis of gene face in clear cell renal cell carcinoma (ccRCC). RCC as a solid tumour entity represents a very apposite tumour model for such investigations. Even if it is possible to probe face profiles by microarray technologies, the main problem is how to adequately interpret the accumulated mass of data derived from microarray technologies. There is a clear lack of a defined, consistent and comparable biostatistical analysis system, with no specific biostatistical standard methodology being unfilled to compare the results of microarray analyses. We used the gene set enrichment analysis (GSEA) method to analyze microarray data from RCC tissue. The bestow study aimed to analyze differential face profiles and set up biomarkers apposite for prognostication at the time of renal surgery by comparing RCC patients with long-term survival data against RCC samples of patients with poorly differentiated (grade 3) RCC, correlated metastatic disease and small survival. PATIENTS AND METHODS: In the bestow study, a total of 29 ccRCC fresh-frozen tissue samples were used; 14 samples from grade 1 (G1) RCC patients without metastatic disease and 15 from grade 3 (G3) RCC patients with synchronous metastatic disease. Face profiling was performed with the Human Genome U133 Plus 2.0 Array (Affymetrix Corp., Santa Clara, CA, USA). Clinical data and long-term follow-up were obtained for all patients. The primary probe level analysis was performed using the Affymetrix MAS 5 algorithm. Additional statistical processing was carried out by GSEA, using the Molecular Signatures Database, MSigDB (http://www.broad.mit.edu/gsea/msigdb/index.jsp). After selecting gene sets with the peak leading edge subsets, a cluster and a additional analyses based on MSigDB data bank analysis was performed. RESULTS: In total, 15 poorly G3 ccRCC, 14 well diffferentiated G1 ccRCC and 14 normal renal tissue samples were analyzed for comparative gene face profiling. There were 12 of 15 G3 ccRCC patients who had synchronous metastatic disease at the time of surgery (pN+ and/or unfriendly metastases: pN+ only = 4, M+ only = 11 and pN+M+= 3). The GSEA identified 700 gene sets. Out of these, 120 sets with the peak leading edge subset were elected monitored by hierarchical clustering G1 vs G3. Comparative analysis using the the MSigDB data bank for pathway arrangement identified 16 gene sets that were differentially strongly over- or underexpressed in G3 vs G1 tumours and are involved in various aspects of tumour physiology, such as metastases and cell motility, signalling and cell proliferation, as well as gene products that are involved in the construction of the extracellular matrix and as cell go up markers. CONCLUSIONS: We analyzed microarray data of gene face in ccRCC comparing poorly differentiated and well differentiated tumour tissue samples. Using GSEA, we found a number of genes set candidates relevant to biological arrangement 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 accept to a specific biological question. GSEA embedded in the statistical workflow procedure for the apposite preparation of face data may improve the analysis and avoid missing changes at the molecular level. A systematic deal with such as GSEA is clearly needed to analyze raw data from microarray analyses, even if these data can only be descriptive and the mass of raw data is derived from a relatively small number of tissue samples. But, consistent alterations of gene face found in specific tumour entities may allow a better appreciative of fastidious aspects of specific tumour biology. Consequently, the molecular characterization of party tumours may potentially be useful for the better party assessment of scenario and, permanently, the identification of biomarkers and targets of specific treatments may eventually help to improve behavior.
HubMed – http://www.moneymakingmommy.com/