HG-U133b gene arrays (Affymetrix, Santa Clara, CA) in accordance with standard protocols. The U-133 set included almost 45,000 probe sets, which included approximately 33,000 well-substantiated human genes. The background correction, probe summarization, and data normalization was performed with the GCOS 1.2 (Affymetrix, Santa Clara, CA) that used Affymetrix default analysis settings and global scaling as a normalizing method. Gene expression data from the two chips were combined with a scaling methodology. The gene expression data and the details of the data processing and methodology were deposited at Gene Expression Omnibus (Accession#GSE-6344). To combine HG-U133 set A and B chip data we used the 100 normalizing probe sets, which scaled the set B to set A and created a combo chip with 44760 probe sets. After the combo chip was made we used ‘‘trimmed mean’’ to scale the chips and filter out the genes with detection call ‘‘Absent in ALL’’ the chips and absolute fold change less than 2 in all of the possible pairwise comparisons. This resulted in 27,763 probe sets. We performed ANOVA on this probe set to determine statistical significance. Probes with p- value,0.05 totaled 13,729 genes. We used this gene set for our final analysis. Bioinformatics methods Pathway analysis. Pathway analysis using the SigPathway package has been described elsewhere [68]. The SigPathway package [8] version 1.1.3 is available as a Bioconductor package (http://www.bioconductor.org) and employs two statistical parameters (NTK and NEK) to rank the pathways. NTK is a measure of the degree that a given pathway differs from the other pathways. NEK is a measure of the degree that the pathway composite expression differs between phenotypes. Pathways were ranked according to the average of the rank-orders of NTK and NEK; false discovery rate (q value) was calculated to adjust for multiple testing. The rank must be high in both rank-orders to minimize false positives. Cluster analysis. Cluster 3.0 [69] was used to perform hierarchical cluster analyses using uncentered correlation and average linkage. Cluster analyses were performed on the most differentially expressed genes that were related to three pathway categories: 1) greater than two-fold difference for renal function- related genes (Fig. 1a), 2) greater than seven-fold difference for metabolic genes (Fig. 1b) and 3) greater than four-fold difference for immune genes (Fig. 1c); the statistical cutoff was P,0.05. Hierarchical cluster analyses were also performed on real time qPCR data on validated genes (Fig. 2a), adipogenesis genes (Fig. 4a), and EMT genes (Fig. 7a). Genetree methodology in GeneSpring (version 7.3, Agilent Technologies, Santa Clara, CA) was applied for cluster analysis that used Pearson correlation and average linkage on four DTFs (Fig. 3a), TFCP2L1, and its regulated genes (Fig. 3c). Fold changes and statistical significance of genes by analysis of variance (ANOVA) were performed in GeneSpring (version 7.3, Agilent Technologies, Santa Clara, CA). Multiple testing correction was done by the Benjamini and Hochberg method. We analyzed an independent ccRCC gene expression database (GSE#15641) which was deposited at Gene Expression Omnibus (GEO) to validate our adipogenic and EMT signatures. The data was generated using gene array HG-U133A and preprocessed using MAS5 (Affymetrix, Santa Clara, Ca). We analyzed stage 1 and 2 ccRCC (n = 13) and normal samples (n = 23). Heatmaps were derived using our adipogenic and EMT signature gene lists (Fig. S2 and S3) through hierarchical clustering with uncentered correlation metric and average linkage method (Cluster 3.0). Signature in Renal Carcinoma RNA isolation and quantitative PCR Real time PCR (qPCR) was used to measure the changes in the mRNA levels of multiple genes from three different pathway categories in patient-matched renal samples. Total mRNA was isolated from tissue using Totally RNA (Ambion, Austin, TX), per the manufacturer’s protocol, followed by ethanol precipitation and Chromaspin column. The O.D. 260/280 ratio of the mRNA was at least 1.8 and the 18S/28S bands were verified on a 1% agarose gel. The RT step was achieved by synthesizing cDNA from 3 mg RNA using the High Capacity Reverse Transcription kit as per the manufacturer’s protocol (Applied Biosystems Foster City, CA). Applied Biosystems’ assays-on-demand assay mix of primers and TaqManH MGB probes (FAMTM dye-labeled) for the genes listed in Table S1 were used for qPCR in a high throughput TILDA platform. Data were normalized to POLR2A for each sample and DCT values were calculated in matched normal and tumor patients. Samples were also normalized to UBC and KPNA6 for verification. Data were presented as a heatmap using cluster 3.0: uncentered correlation and incomplete linkage were used (Fig. 2a). Immunohistochemistry IHC was performed on patient-matched renal samples to validate genes from three pathway categories at the protein level. Renal tissues were mounted on slides either from paraffin- embedded blocks for IHC or from frozen sections for Oil Red ‘O’ staining (Sigma-Aldrich, St. Louis, MO), per the manufactur- er’s protocol. Samples were blocked with Diluent that contained Background Reducing Components (Dakocytomation, Denmark) for 30 minutes and probed for specified antibodies that included: KNG1, AQP2, TFAP2B, ENO2 (Santa Cruz, Santa Cruz, CA), SCNN1B, TLR2, GATA2 (Novus, Littleton, CO), CXCR4 (BD Pharmingen, San Diego, CA), CYP2J2, ALDOB (Abcam, Cam- bridge, MA), DMRT2, TFCP2L1 (Lifespan Biosciences, Seattle, WA), Vimentin (Sigma-Aldrich), and ADFP (Acris Antibodies, Germany). Negative sections were prepared by incubating the slides in the absence of the primary antibody. The staining of the tissue sections was scored and verified by a pathologist (KW) based upon signal intensity (0-3+) and the percentage of positive cells (0#5%, 1+ = 5-20%, 2+ = 20-50%, and 3+$50%). Cases were excluded from the study if a section could not be assigned a score due to insufficient tumor tissue. Afterwards, the grading score was summed and reported as summed scores followed by Chi-square analysis (df = 3, p,0.01). Images were obtained at 20X using Scanscope XT and Imagescope software (Aperio Technologies, Vista, CA). Cell culture and differentiation experiments The human A498 ccRCC cell line and MDCK canine normal renal cells were purchased from ATCC (Manassas, VA) while KIJ- 265 (Stage 4) and KIJ-308 (Stage 2) cell lines and primary cells were established in the Copland laboratory and derived from human renal clear cell carcinoma and normal-matched tissues. Renal cells were maintained in complete renal media containing MEM (Cellgro Herndon, VA) supplemented with 10% fetal bovine serum (Hyclone Logan, UT), 1 mM sodium pyruvate, 10 mM HEPES, 1% non-essential amino acids and penicillin- streptomycin-amphotericin B (Cellgro Herndon, VA) at 37uC in a humidified atmosphere with 5% CO2. For differentiation experiments, cells were maintained for 2 weeks in complete renal media, adipogenic, or osteogenic media. For adipogenic differen- tiation, media was supplemented for 2 weeks with 0.1 mM dexamethasone, 10 mg/mL insulin, 100 mM indomethacin, and 500 mM isobutylmethylxanthine (Sigma-Aldrich, St. Louis, MO) PLoS ONE | www.plosone.org 12 May 2010 | Volume 5 | Issue 5 | e10696