BMC Genomics 2007, 8:117 http://www.biomedcentral.com/1471-2164/8/117 Santin 488 Carlson 140 9 2 20 11 Combined 203 cVFaielgncunarndeciae1grrgaemneosf itnhethoevleitrelarpatoufredifferentially expressed cervi- Venn diagram of the overlap of differentially expressed cervical cancer genes in the literature. Eight reports of differentially expressed cervical cancer genes from the literature were compared against each other and to our results. Seven of the studies were combined into one group (203 genes, no overlap between studies). Our results (Carlson, 140 genes) overlapped with Santin (488 genes) by 9 genes and with the rest by 11 genes. Santin had 20 genes in common with the 203 gene combined group. Only 2 genes were found in all three data sets, SLC2A1 and a serine pro- tease inhibitor (clone IDs 25389 and 2562939). Our results show comparable overlap with the literature, and provide additional evidence that the tissue samples analyzed are rep- resentative of previous reports on cervical cancer. mRNA expression correlation. The primary cell line was expected to have the highest correlation to cervical cancer over the other cell lines because it was more recently out of its in vivo environment. HeLa cultured as a monolayer had a surprisingly poor correlation to cervical cancer, given that it has been extensively used in cancer research [31-34]. HeLa was the poorest model of cervical cancer with a correlation of 0.08, consistent with HeLa cells' sep- aration from other cell lines in the SVD analysis. However, HeLa cells increased their correlation to cervical cancer (0.42) when cultured in an organotypic environment, which was not evident in the SVD analysis. This analysis determined that relatively simple changes to a cell culture, such as different types of media, can affect how well a cell line can model tissue. The structural environment had a large impact on how well a cell line models the in vivo environment. It is possible that culturing the primary, C4- I, and C4-II cell lines in an organotypic environment would further increase their correlation to both cervical cancer and normal cervix. Once we had identified a cell line with the highest corre- lation to cervical cancer (C4-I), we generated a list of 196 differentially expressed genes between the C4-I and pri- mary normal cell lines. This list was used to hierarchically cluster the normal cervix and cervical cancer biopsies. The resulting dendrogram [see Additional file 3] demonstrates that potential biomarkers can be derived from cell lines that can separate the normal and cancer biopsies as well as their own complete expression signatures (Figure 2A). Cell lines can therefore be used to find potential biomar- kers if it is known that they actually model the tissue rea- sonably well. This quantitative analysis provided information on how changes to the culture environment can change a cell line's performance as a model to cervical cancer. To pro- vide a deeper analysis of how well cell lines model tissue, the correlation of each Gene Ontology (GO) pathway was calculated to both cervical cancer and normal cervix for each cell line and growth condition. Pathway correlations of cell line models to cervical cancer The global correlation of cell line models to tissue pro- vided an analytical way to choose better overall models of cervical tissue; however, choosing an appropriate model for a specific pathway may be of more utility. Using GO annotations, the correlation for each pathway and for all cell lines and growth conditions was calculated against both cervical cancer and normal cervix. As shown in Fig- ure 4, cell lines varied dramatically in the extent to which they modeled cervical cancer for a particular pathway. As discussed above, the better overall models of both cervical cancer and normal cervix that we tested were the primary, C4-I, and C4-II cell lines, with global correlations to cervi- cal cancer of R = 0.52, 0.51, and 0.52, respectively. How- ever, when studying the "Regulation of Apoptosis" pathway (GO:42981) for example, the primary cell line retained a high correlation (R = 0.62) but the C4-I and C4- II cell lines perform poorly with correlations of 0.37 and 0.22, respectively (Figure 4A). Further analysis of the genes that resulted in a lower correlation for the cell line C4-II resulted in 8 genes in the "Anti-Apoptosis" pathway (GO:6916), a sub-pathway of "Regulation of Apoptosis". These genes included BAG1, BFAR, BIRC1, BIRC3, BIRC4, MALT1, PRKCZ, and TNFAIP3, and had a 2–7 fold differ- ence when compared to cervical cancer. This pathway failed to achieve significance when calculating individual pathway correlations, but was strong enough to lower the correlation for C4-II in the "Regulation of Apoptosis" pathway calculation. Interestingly, the HeLa cell line cul- tured in an organotypic environment had a correlation of 0.6 for the "Regulation of Apoptosis" pathway, whereas its global correlation to cervical cancer was 0.42. This path- way was not an exception, the organotypic HeLa cell line Page 4 of 13 (page number not for citation purposes)