BMC Cancer 2008, 8:270 http://www.biomedcentral.com/1471-2407/8/270 second-strand cDNA synthesis, the double-stranded cDNA was purified and used as a template for the subse- quent in-vitro transcription reaction. This reaction was carried out in the presence of T7-RNA polymerase and a biotinylated nucleotide analogue/ribonucleotide mix for complementary RNA (cRNA) amplification and biotin labeling. The biotinylated cRNA targets were then cleaned up, fragmented, and hybridized to the GeneChip expres- sion array. The chip was reacted with streptavidin-phyco- erythrin and then with biotinylated anti-streptavidin antibody (Vector Laboratories, Burlingame, CA). Arrays were scanned by GeneArray scanner G2500A (Hewlett Packard, Palo Alto, CA), visually inspected for hybridiza- tion imperfections and analyzed using Affymetrix Micro- array Suite software version 5.0 by scaling to an average intensity of 250. The data were analyzed with GeneSpring (Silicon Genetics, Redwood City, CA) using the MAS5 algorithm [17]. First, values were set below the cutoff to the cut off: 0.01. Than, Gene-expression data were normalized "per chip" and "per gene". For "per chip" normalization, all expres- sion data on a chip were normalized to the 50th percentile of the measurements taken from all values on that chip. "Per gene" normalization divide each gene in the selected samples by the median of the gene's measurements in the respective control group. It was performed here according the type of comparison being made. When expression profiles were compared between carcinoma and papillary adenocarcinoma tumors, the expression of a given gene was normalized to the median of the expression level of the wild-type mammary-gland samples. This enabled the relation of gene expression levels in the tumor types also to that detected in the wild-type gland. For comparison between tumors and wild-type mammary gland, the expression of a given gene was normalized to the median of the data obtained from the expression of all genes an all chips. The normalized data were log-trans- formed. The significant (p < 0.05) differences in gene expression, based on the individual values obtained from each tumor sample, between the two types of tumors or between each tumor type and the wild-type mammary gland were calculated using one-tailed t-test statistical analysis. Gene-ontology analysis, hierarchical clustering and biological-pathway analysis Genes with different levels of expression between tumor phenotypes were categorized into Cellular component, Molecular function and Biological process categories using the GO Slim annotation tool in GeneSpring. Genes assembled in each functional annotation were further sep- arated into GO Slim terms. Hierarchical clustering was performed on genes exhibiting a twofold, significant (P ≤ 0.05) difference in their expres- sion between phenotypes. These genes were clustered into four groups according to their assembly in the gene tree and condition tree and were annotated by GO using DAVID software [18]. The rate of enrichment in the rele- vant genes was determined for each term. The genes exhibiting differential expression in mammary carcino- mas vs. papillary adenocarcinomas were assembled by the DAVID software into metabolic pathways using the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis with some modifications. Validation of array analysis Semi-quantitative RT-PCR was applied to confirm the dif- ferent expression patterns of selected genes in the different types of tumors. RNA, extracted from individual tumors and mammary glands, was reversed-transcribed and ana- lyzed using the primers listed in Additional file 1 (data sheet A). The number of PCR cycles was calibrated for the exponential segment of the reaction. The DNA was blot- ted onto a nylon membrane and reacted with the relevant 32P-labeled probe. The membranes were exposed to film and signals were visualized and quantitated using GelPro software. Histopathology and immunofluorescence staining Mammary and tumor biopsies were fixed with Bouin's solution, dehydrated in a graded series (50 to 100%) of ethanol, cleared in xylene and embedded in paraffin. Sec- tions of 4 μm were processed for hematoxylin & eosin (H&E) staining. For immunohistochemical analysis of Cav-1, sections were treated as previously described [14] using anti-Cav-1 antibody (Cell Signaling, Danvers, MA) diluted 1:125. Signals were generated with EnVision rea- gent (Dako, Glostrup, Denmark) containing the HRP- labeled secondary antibody and diaminobenzidine (DAB) substrate (Vector Laboratories, Burlingame, CA). For immunofluorescence analysis of β-catenin, sections were reacted with anti-β-catenin antibody (Sigma, St. Louis, MO, diluted 1:2,000) and signals were generated with anti-rabbit IgG conjugated to FITC (Sigma) diluted 1:2,000. For double-staining of Cav-1 and SMA, sections were reacted overnight at 4°C with a mixture of mouse anti-SMA monoclonal antibody (Dako, diluted 1:200) and rabbit anti-Cav-1. Signals were generated after incu- bation for 1 h with a mixture of the appropriate secondary antibodies: donkey anti-rabbit IgG conjugated to Cy™3 (Jackson Immunoresearch Laboratories. W. Baltimore Pike, PA) or donkey anti-mouse IgG labeled with Alexa Fluor® 488 (Molecular Probes, Eugene, OR), both diluted 1:300. Nuclear staining was performed with DAPI (Qbio- gen, Irvine, CA). Stained sections were mounted in Fluor- omount (DBS, Pleasanton, CA) and visualized using a Leica fluorescent microscope (Vertrieb, Bensheim, Ger- Page 3 of 19 (page number not for citation purposes)