INTRODUCTION: The molecular and cellular mechanisms involved in prostate cancer progression towards a hormone-independent and highly invasive, metastatic phenotype, are not well understood. Cell lines with different metastatic potential, when analyzed by microarray techniques, offer valuable tools for identifying genes associated with the metastatic phenotype. OBJECTIVES: Gene expression profiles were compared for two rat prostate cancer cell lines with differing metastatic abilities in order to better characterized molecular underpinnings of the prostate cancer metastatic process. MATERIALS AND METHODS: Affymetrix arrays were used to analyze gene expression of two rat prostate cancer cell lines, MAT-LyLu and G. Microarray data were analyzed using pathway and functional group analysis. A selected set of genes was subjected to real-time polymerase chain reaction for validating the microarray data. RESULTS: Microarray data analysis revealed differential expression of genes from a number of signaling and metabolic pathways. Overexpression was detected in 48 genes and underexpression in 59 genes of the MAT-LyLu line compared to the standard G line. Genes were grouped into functional categories, including epithelial-extracellular matrix interaction, cell motility, cell proliferation, and transporters, among others. Many of these genes were not previously associated to prostate cancer metastasis. CONCLUSIONS: Many genes with altered expression associated with a metastatic prostate cancer phenotype were identified. Further validation of these genes in human prostate samples will determine their usefulness as biomarkers for early diagnosis of recurrence or metastasis of prostate cancer, as well as potential therapeutic targets for this disease.
INTRODUCTION: The molecular and cellular mechanisms involved in prostate cancer progression towards a hormone-independent and highly invasive, metastatic phenotype, are not well understood. Cell lines with different metastatic potential, when analyzed by microarray techniques, offer valuable tools for identifying genes associated with the metastatic phenotype. OBJECTIVES: Gene expression profiles were compared for two ratprostate cancer cell lines with differing metastatic abilities in order to better characterized molecular underpinnings of the prostate cancer metastatic process. MATERIALS AND METHODS: Affymetrix arrays were used to analyze gene expression of two ratprostate cancer cell lines, MAT-LyLu and G. Microarray data were analyzed using pathway and functional group analysis. A selected set of genes was subjected to real-time polymerase chain reaction for validating the microarray data. RESULTS: Microarray data analysis revealed differential expression of genes from a number of signaling and metabolic pathways. Overexpression was detected in 48 genes and underexpression in 59 genes of the MAT-LyLu line compared to the standard G line. Genes were grouped into functional categories, including epithelial-extracellular matrix interaction, cell motility, cell proliferation, and transporters, among others. Many of these genes were not previously associated to prostate cancer metastasis. CONCLUSIONS: Many genes with altered expression associated with a metastatic prostate cancer phenotype were identified. Further validation of these genes in human prostate samples will determine their usefulness as biomarkers for early diagnosis of recurrence or metastasis of prostate cancer, as well as potential therapeutic targets for this disease.