Research article

Cross-species global and subset gene expression profiling identifies genes involved in prostate cancer response to selenium

Michael Schlicht^1,2 , Brian Matysiak¹ , Tracy Brodzeller¹ , Xinyu Wen^1,3 , Hang Liu^1,3 , Guohui Zhou^1,2 , Rajiv Dhir⁴ , Martin J Hessner^5,6 , Peter Tonellato^3,6 , Mark Suckow⁷ , Morris Pollard⁷ and Milton W Datta^1,2

¹Department of Pathology, Medical College of Wisconsin, 8701 Watertown Plank Road, Milwaukee, WI, 53226, USA

²Department of Pathology, Winship Cancer Institute, Emory University School of Medicine, 1365-B Clifton Road NE, Atlanta, GA, 30322, USA

³Bioinformatics Program and Human and Molecular Genetics Center, Medical College of Wisconsin, 8701 Watertown Plank Road, Milwaukee, WI, 53226, USA

⁴Department of Pathology, University of Pittsburgh Medical Center, 200 Lothrop Street, Pittsburgh, PA, 15242, USA

⁵Department of Pediatrics and Human and Molecular Genetics Center, Medical College of Wisconsin, 8701 Watertown Plank Road, Milwaukee, WI, 53226, USA

⁶Department of Physiology, Medical College of Wisconsin, 8701 Watertown Plank Road, Milwaukee, WI, 53226, USA

⁷Walther Cancer Center, Lobund Laboratories, 400 Freiman Life Science Center, Notre Dame University, Notre Dame, IN, 46556, USA

author email corresponding author email

BMC Genomics 2004, 5:58doi:10.1186/1471-2164-5-58

Published:	20 August 2004

Abstract

Background

Gene expression technologies have the ability to generate vast amounts of data, yet there often resides only limited resources for subsequent validation studies. This necessitates the ability to perform sorting and prioritization of the output data. Previously described methodologies have used functional pathways or transcriptional regulatory grouping to sort genes for further study. In this paper we demonstrate a comparative genomics based method to leverage data from animal models to prioritize genes for validation. This approach allows one to develop a disease-based focus for the prioritization of gene data, a process that is essential for systems that lack significant functional pathway data yet have defined animal models. This method is made possible through the use of highly controlled spotted cDNA slide production and the use of comparative bioinformatics databases without the use of cross-species slide hybridizations.

Results

Using gene expression profiling we have demonstrated a similar whole transcriptome gene expression patterns in prostate cancer cells from human and rat prostate cancer cell lines both at baseline expression levels and after treatment with physiologic concentrations of the proposed chemopreventive agent Selenium. Using both the human PC3 and rat PAII prostate cancer cell lines have gone on to identify a subset of one hundred and fifty-four genes that demonstrate a similar level of differential expression to Selenium treatment in both species. Further analysis and data mining for two genes, the Insulin like Growth Factor Binding protein 3, and Retinoic X Receptor alpha, demonstrates an association with prostate cancer, functional pathway links, and protein-protein interactions that make these genes prime candidates for explaining the mechanism of Selenium's chemopreventive effect in prostate cancer. These genes are subsequently validated by western blots showing Selenium based induction and using tissue microarrays to demonstrate a significant association between downregulated protein expression and tumorigenesis, a process that is the reverse of what is seen in the presence of Selenium.

Conclusions

Thus the outlined process demonstrates similar baseline and selenium induced gene expression profiles between rat and human prostate cancers, and provides a method for identifying testable functional pathways for the action of Selenium's chemopreventive properties in prostate cancer.