Expression profiling of @entity1 XXXX with functional taxonomic analysis.

@entity5 @entity1 @entity1358 @entity703
BACKGROUND: Molecular characterization has contributed to the understanding of the inception, progression, treatment and prognosis of @entity5 . Nucleic acid array-based technologies extend molecular characterization of @entity5 to thousands of gene products. To effectively discriminate between @entity5 sub-types, reliable laboratory techniques and analytic methods are required. RESULTS: We derived mRNA expression profiles from 21 @entity1 tissue samples (eight normal kidneys and 13 @entity703 ) and two pooled samples using the Affymetrix GeneChip platform. A panel of ten clustering algorithms combined with four data pre-processing methods identified a consensus cluster dendrogram in 18 of 40 analyses and of these 16 used a logarithmic transformation. Within the consensus dendrogram the expression profiles of the samples grouped according to tissue type; @entity1358 displayed distinctly different gene expression patterns. By using a rigorous statistical selection based method we identified 355 genes that showed significant (p < 0.001) gene expression changes in clear cell @entity703 compared to normal kidney. These genes were classified with a tool to conceptualize expression patterns called "Functional Taxonomy". Each @entity5 type had a distinct "signature," with a high number of genes in the categories of Metabolism, Signal Transduction, and Cellular and Matrix Organization and Adhesion. CONCLUSIONS: Affymetrix GeneChip profiling differentiated @entity1358 from one another and from normal kidney cortex. Clustering methods that used logarithmic transformation of data sets produced dendrograms consistent with the sample biology. Functional taxonomy provided a practical approach to the interpretation of gene expression data.
