Investigation of the molecular mechanisms underlying postoperative recurrence in prostate cancer by gene expression profiling.

The present study aimed to identify potential genes associated with prostate cancer (PCa) recurrence following radical prostatectomy (RP) in order to improve the prediction of the prognosis of patients with PCa. The GSE25136 microarray dataset, including 39 recurrent and 40 non-recurrent PCa samples, was downloaded from the Gene Expression Omnibus database. Differentially-expressed genes (DEGs) were identified using limma packages, and the pheatmap package was used to present the DEGs screened using a hierarchical cluster analysis. Furthermore, gene ontology functional enrichment analysis was used to predict the potential functions of the DEGs. Subsequently, Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses were performed to analyze pathway enrichment of DEGs in the regulatory network. Lastly, a protein-protein interaction (PPI) network of the DEGs was constructed using Cytoscape software to understand the interactions between these DEGs. A total of 708 DEGs were identified in the recurrent and non-recurrent PCa samples. Functional annotation revealed that these DEGs were primarily involved in cell adhesion, negative regulation of growth, and the cyclic adenosine monophosphate and mitogen-activated protein kinase (MAPK) signaling pathways. Furthermore, five key genes, including cluster of differentiation 22, insulin-like growth factor-1, inhibin β A subunit, MAPK kinase 5 and receptor tyrosine kinase like orphan receptor 1, were identified through PPI network analysis. The results of the present study have provided novel ideas for predicting the prognosis of patients with PCa following RP.