Identification of candidate hub genes correlated with the pathogenesis, diagnosis, and prognosis of prostate cancer by integrated bioinformatics analysis

BACKGROUND: Prostate cancer (PCa) has the second highest morbidity and mortality rates in men. Concurrently, novel diagnostic and prognostic biomarkers of PCa remain crucial. METHODS: This study utilized integrated bioinformatics method to identify and validate the potential hub genes with high diagnostic and prognostic value for PCa. RESULTS: Four Gene Expression Omnibus (GEO) datasets including 123 PCa samples and 76 normal samples were screened and a total of 368 differentially expressed genes (DEGs), including 120 up-regulated DEGs and 248 down-regulated DEGs, were identified. Subsequent Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis showed that the DEGs were majorly enriched in focal adhesion, chemical carcinogenesis, drug metabolism, and cytochrome P450 pathways. Then, 11 hub genes were identified from the protein-protein interaction (PPI) network of the DEGs; 7 of the 11 genes showed the ability of distinguishing PCa from normal prostate based on receiver operating characteristic (ROC) curve analysis. And 5 of the 11 genes were correlated with clinical attributes. Lower CAV1, KRT5, SNAI2 and MYLK expression level were associated with higer Gleason score, advanced pathological T stage and N stage. Lower KRT5 and MYLK expression level were significantly correlated with poor disease-free survival, and lower KRT5 and PTGS2 expression level were significantly related to biochemical recurrence (BCR) status of PCa patients. CONCLUSIONS: In conclusion, CAV1, KRT5, SNAI2, and MYLK show potential clinical diagnostic and prognostic value and could be used as novel candidate biomarkers and therapeutic targets for PCa.