The aim of this study was to examine whether the degree to which cell lines model corresponding cells in vivo is an important aspect of their value as models in studying disease processes.
Materials and methods: The work presented here utilizes gene expression data from two published microarray datasets to compare the differences and similarities among the two systems in order to identify major transcriptional changes in the adaptation process from a tissue to a cell line.
Results: Gene ontology and pathway analyses of comparator gene lists showed that the cell cycle related genes were significantly up-regulated in cell lines and immune response related genes were significantly up-regulated in clinical specimens. Estrogen receptor analysis also indicated differences in the clustering patterns of cell lines relative to clinical specimens.
Conclusion: These findings suggest that significant differences in gene expression exist between clinical conditions and their respective cell line models and that these differences should be taken into account when extrapolating cell line results to in vivo systems.