Premium
Gene expression analysis in clear cell renal cell carcinoma using gene set enrichment analysis for biostatistical management
Author(s) -
Maruschke Matthias,
Reuter D.,
Koczan D.,
Hakenberg O. W.,
Thiesen H.J.
Publication year - 2011
Publication title -
bju international
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.773
H-Index - 148
eISSN - 1464-410X
pISSN - 1464-4096
DOI - 10.1111/j.1464-410x.2010.09794.x
Subject(s) - renal cell carcinoma , gene chip analysis , gene expression profiling , microarray analysis techniques , microarray , tissue microarray , clear cell renal cell carcinoma , computational biology , gene expression , biology , bioinformatics , oncology , gene , pathology , medicine , immunohistochemistry , genetics
To improve the workflow for standardizing the statistical interpretation provides an opportunity for the analysis of gene expression in clear cell renal cell carcinoma (ccRCC). RCC as a solid tumour entity represents a very suitable tumour model for such investigations. Although it is possible to investigate expression 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 available to compare the results of microarray analyses. We used the gene set enrichment analysis (GSEA) method to analyze microarray data from RCC tissue. The present study aimed to analyze differential expression profiles and establish biomarkers suitable 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, concomitant metastatic disease and short survival.