Urzúa et al. BMC Genomics 2010, 11(Suppl 5):S1 http://www.biomedcentral.com/1471-2164/11/S5/S1 length of estrous cycles increases while the monthly cycle frequency decreases in ageing mice [6]. The num- ber of ovulations roughly reaches 500 during the repro- ductive life of women while in mice this number can be achieved earlier than middle age due to multiple ovula- tions in a single cycle [7] as judged by the litter size observed in mice [5]. Cysts, invaginations and cell layer- ing are also common observations in the mouse and human ovaries [7]. Mouse inbred strains display measurable traits that are described as continuous phenotypes in the Mouse Phe- nome [8] and the Mouse Tumor Biology [9] databases. The natural variability observed in mice strains offers the opportunity to study disease susceptibility in a genetically defined background. In simple terms, pheno- typic variability could be due to the interplay of gene transcripts and/or proteins expressed at different relative abundances across individuals in a tissue or cell type implicated in a phenotype. Thus, if a correlation exists between a continuous phenotype and gene expression, a measure of each gene’s contribution to the observed phenotype can be inferred. DNA microarrays are suited to measure transcript levels for hundreds or thousands of genes simultaneously, and thus such contribution can be addressed in a wide-genome format. A number of statistical approaches have been recently formulated to correlate DNA microarray data with phenotypic covariates [10]. In an attempt to gain novel information linking repro- ductive parameters with ovarian tumorigenesis we describe here a correlation analysis between sponta- neous ovarian tumors, reproductive phenotypes and gene expression profiles obtained with NIA15k-DNA microarrays from ovaries of four mouse inbred strains. Using a linear regression approach with control of mul- tiple testing, “ovarian tumor frequency” (OTF) and “number of litters” (NL) were the top-correlated of 5 analyzed phenotypes. About one hundred genes were coincident between OTF and NL. The enriched biologi- cal functions in this overlapped sub-set were “RNA- binding/mRNA-processing” and “protein folding”. The relevant information concerning the significant genes was mined and the relationship between ovarian func- tion and ovarian tumorigenesis at the molecular level is discussed. Results Consistency of microarray data and Q-PCR assays The robustness and reliability of the mouse NIA-15K cDNA microarray platform has been demonstrated in our previous work [11,12] and by others [13-15]. Experi- mental design employed a common reference RNA and replicate dye-swap. Raw data was subjected to print-tip loess normalization, a numerical correction based on Page 2 of 11 local deviations across the microarray surface aimed to counteract the inherent noise of these devices [16]. Further adjustment consisted of inter-slide scale normal- ization, after which statistical comparisons were per- formed. The complete normalized dataset for 14,586 cDNA clones in 23 microarrays corresponding to 4 sam- ples (i.e. 6 replicates for 3 samples and 5 replicates for 1 sample) is available as a Supplementary spreadsheet (Urzua_complete_dataset.xls) and has been deposited to the GEO database (http://www.ncbi.nlm.nih.gov/geo/) with the accession code GSE18045. One of the 24 hybri- dization experiments was discarded because not meeting with minimal image quality parameters. Figure 1, panels A through C, shows microarray data comparison for expression of genes Tsc22, Col3a1 and Fubp1 in 2 strains assayed each in 6 microarray replicates. These genes were selected because they were present as multi- ple cDNA clones in the NIA-15K collection, so that intra-slide consistency could also be evaluated. The expression change (ec), defined as the difference between log2 ratio averages of all clones in the 6x2 replicate arrays, was similar among the 3 genes. How- ever, the adjusted p value was significant for the Fubp1 gene only. Additionally, quantitative-PCR (Q-PCR) con- firmation assays were performed for seven genes assessed in a previous mouse ovarian study, and for which primer pairs were available. Three of these genes showed statistical significance after an ANOVA test across the 4 mouse strains (see next section). The CT values for test and reference samples were corrected with the 18S-rRNA as internal control transcript and then converted to log2-based ratios to compare with microarray results. As shown in Figure 1D, the squared correlation between Q-PCR and microarray platforms was R2 = 0.749, a value over the range observed in a recent large-scale study aimed to validate microarray data using Q-PCR [17]. Microarray data roughly ranged from -4.0 to +1.5 while the correspondent Q-PCR results ranged from -4.9 to +3.4. Except for one data pair, out of 28 comparisons, the ratio direction (up- or down-regulation) more than the absolute value, was consistent between both methodologies. Additional file 1, Table 1 (Urzua_Suppl_Results) shows detailed microarray log2 ratio values and CT values for the seven genes in all test and reference samples. Tumor and reproductive parameters correlated with ovarian gene expression A preliminary ANOVA analysis resulted in 628 cDNA clones (4.27 % of the whole dataset) with statistically significant differences (adjusted p<0.05) between 2 of any of the four mouse ovarian tissue profiles (data not shown). These results simply indicate that transcrip- tional differences indeed occur among ovarian tissue