Identification of genes associated with ovarian cancer metastasis using microarray expression analysis

Although the transition from early- to advanced-stage ovarian cancer is a critical determinant of survival, little is known about the molecular underpinnings of ovarian metastasis. We hypothesize that microarray analysis of global gene expression patterns in primary ovarian cancer and metastatic omental implants can identify genes that underlie the metastatic process in epithelial ovarian cancer. We utilized Affymetrix U95Av2 microarrays to characterize the molecular alterations that underlie omental metastasis from 47 epithelial ovarian cancer samples collected from multiple sites in 20 patients undergoing primary surgical cytoreduction for advanced-stage (IIIC/IV) serous ovarian cancer. Fifty-six genes demonstrated differential expression between ovarian and omental samples (P < 0.01), and twenty of these 56 differentially expressed genes have previously been implicated in metastasis, cell motility, or cytoskeletal function. Ten of the 56 genes are involved in p53 gene pathways. A Bayesian statistical tree analysis was used to identify a 27-gene expression pattern that could accurately predict the site of tumor (ovary versus omentum). This predictive model was evaluated using an external data set. Nine of the 27 predictive genes have previously been shown to be involved in oncogenesis and/or metastasis, and 10/27 genes have been implicated in p53 pathways. Microarray findings were validated by real-time quantitative PCR. We conclude that gene expression patterns that distinguish omental metastasis from primary epithelial ovarian cancer can be identified and that many of the genes have functions that are biologically consistent with a role in oncogenesis, metastasis, and p53 gene networks.