[Bioinformatics-based identification of the key genes associated with prostate cancer]

To identify the key genes associated with the pathogenesis of PCa using the bioinformatics approach for a deeper insight into the molecular mechanisms underlying the development and progression of PCa.

The microarray datasets GSE70770, GSE32571 and GSE46602 were downloaded from the Gene Expression Omnibus (GEO) database, and differentially expressed genes (DEG) in the normal prostate tissue and PCa were identified with the GEO2R tool, followed by functional enrichment analysis. A protein-protein interaction (PPI) network of DEGs was constructed by STRING and visualized with the Cytoscape software.

A total of 235 DEGs were identified, including 61 up-regulated and 174 down-regulated genes, which were mainly enriched in focal adhesion kinase (FAK), ECM-receptor interaction, and other signaling pathways. From the PPI network were screened out 12 highly connected hub genes, including MYH11, TPM1, TPM2, SMTN, MYL9, VCL, ACTG1, CNN1, CALD1, ACTC1, MYLK and SORBS1, which were shown by hierarchical cluster analysis to be capable of distinguishing prostate cancer from non-cancer tissue.

A total of 235 DEGs and 12 hub genes were identified in this study, which may contribute to a further understanding of the molecular mechanisms of the development and progression of PCa, and provide new candidate targets for the diagnosis and treatment of the malignancy.