Integrated Network Analysis to Determine CNN1, MYL9, TAGLN, and SORBS1 as Potential Key Genes Associated with Prostate Cancer.

BACKGROUND: Prostate cancer (PCa) is challenging to treat. It is necessary to screen for related biological markers to accurately predict the prognosis and recurrence of prostate cancer. METHODS: Three data sets, GSE28204, GSE30521, and GSE69223, from the Gene Expression Omnibus (GEO) database were integrated into this study. After the identification of differentially expressed genes (DEGs) between PCa and normal prostate tissues, network analyses including protein-protein interaction (PPI) network, and weighted gene co-expression network analysis (WGCNA) were used to select hub genes. Gene Ontology (GO) term analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed to annotate the functions of DEGs and hub modules of the networks. Survival analysis was performed to validate the correlation between the key genes and PCa relapse. RESULTS: In total, 867 DEGs were identified, including 201 upregulated and 666 downregulated genes. Three hub modules of the PPI network and one hub module of the weighted gene co-expression network were determined. Moreover, four key genes (CNN1, MYL9, TAGLN, and SORBS1) were significantly associated with PCa relapse (p < 0.05). CONCLUSIONS: CNN1, MYL9, TAGLN, and SORBS1 may be potential biomarkers for PCa development.