Comprehensive analysis of biomarkers for prostate cancer based on weighted gene co-expression network analysis

BACKGROUND: Prostate cancer (PCa) is one of the leading causes of cancer-related death. In the present research, we adopted a comprehensive bioinformatics method to identify some biomarkers associated with the tumor progression and prognosis of PCa. METHODS: Differentially expressed genes (DEGs) analysis and weighted gene co-expression network analysis (WGCNA) were applied for exploring gene modules correlative with tumor progression and prognosis of PCa. Clinically Significant Modules were distinguished, and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were used to Annotation, Visualization and Integrated Discovery (DAVID). Protein–protein interaction (PPI) networks were used in selecting potential hub genes. RNA-Seq data and clinical materials of prostate cancer from The Cancer Genome Atlas (TCGA) database were used for the identification and validation of hub genes. The significance of these genes was confirmed via survival analysis and immunohistochemistry. RESULTS: 2688 DEGs were filtered. Weighted gene co-expression network was constructed, and DEGs were divided into 6 modules. Two modules were selected as hub modules which were highly associated with the tumor grades. Functional enrichment analysis was performed on genes in hub modules. Thirteen hub genes in these hub modules were identified through PPT networks. Based on TCGA data, 4 of them (CCNB1, TTK, CNN1, and ACTG2) were correlated with prognosis. The protein levels of CCNB1, TTK, and ACTG2 had a degree of differences between tumor tissues and normal tissues. CONCLUSION: Four hub genes were identified as candidate biomarkers and potential therapeutic targets for further studies of exploring molecular mechanisms and individual therapy on PCa.