RESUMO
BACKGROUND: Metastasis poses the greatest threat to the lives of individuals with prostate cancer. Therefore, it is imperative to identify the underlying mechanism driving metastasis. Doing so would facilitate the detection of new diagnostic biomarkers and the advancement of treatment options for patients. METHODS: Metastasis-related modules were identified through weighted gene co-expression network analysis based on microarray GSE6919. Hub genes were confirmed by quantitative real-time PCR across different prostate cell lines and clinic samples. Pivotal genes were determined through integration of RNA and transcription factor-target associated interactions. To predict drugs with potential to suppress tumor metastasis, we applied molecular networks using the DrugBank database. Drug repositioning analysis and confirmation of drug screen were conducted using the compound library. Confirmation of selective cytotoxicity of cupric oxide was carried out via invasion, transwell and apoptosis assays. RESULTS: We identified five metastasis-related modules. Of these modules, two were identified to represent core dysfunction modules in which five hub genes were determined for each module. Five of these 10 genes correlating with prostate cancer progression. Furthermore, our analysis revealed that there are 36 drugs with the potential to be active against tumor metastasis. Finally, we identified four compounds that have not previously been reported to have any association with cancer therapy. Of these, cupric oxide was determined to have the best chemotherapeutic potential in treating prostate cancer metastasis. CONCLUSIONS: By combining bioinformatics methods with compound library screening, this study proposes a valuable approach to drug discovery. Cupric oxide showed the potential in the treatment of prostate cancer metastasis and deserves further study.