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Identification of key genes and pathways in castrate-resistant prostate cancer by integrated bioinformatics analysis
Affiliation:1. Department of Epidemiology and Health Statistics, Xiangya School of Public Health, Central South University, Shangmayuanling 238, Kaifu District, Changsha, Hunan 410078, People’s Republic of China;2. Department of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha 410008, People’s Republic of China
Abstract:ObjectiveTo identify hub genes and pathways involved in castrate-resistant prostate cancer (CRPC).MethodsThe gene expression profiles of GSE70768 were downloaded from Gene Expression Omnibus (GEO) datasets. A total of 13 CRPC samples and 110 tumor samples were identified. The differentially expressed genes (DEGs) were identified, and the gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway (KEGG) enrichment analysis was performed. Protein-protein interaction (PPI) network module analysis was constructed and performed in Cytoscape software. Weighted correlation network analysis (WGCNA) was conducted to determine hub genes involved in the development and progression of CRPC. The gene expression profiles of GSE80609 were used for validation.ResultsA total of 1738 DEGs were identified, consisting of 962 significantly down-regulated DEGs and 776 significantly upregulated DEGs for the subsequent analysis. GO term enrichment analysis suggested that DEGs were mainly enriched in the extracellular matrix organization, extracellular exosome, extracellular matrix, and extracellular space. KEGG pathway analysis found DEGs significantly enriched in the focal adhesion pathway. PPI network demonstrated that the top 10 hub genes were ALB, ACACB, KLK3, CDH1, IL10, ALDH1A3, KLK2, ALDH3B2, HBA1, COL1A1. Also, WGCNA identified the top 5 hub genes in the turquoise module, including MBD4, BLZF1, PIP5K2B, ZNF486, LRRC37B2. Plus, the Venn diagram demonstrated that HBA1 was the key gene in both GSE70768 and GSE80609 datasets.ConclusionsThese newly identified genes and pathways could help urologists understand the differences in the mechanism between CRPC and PCa. Besides, it might be promising targets for the treatment of CRPC.
Keywords:Castrate-Resistant prostate cancer  Bioinformatics  Genes  Pathways  Gene expression omnibus datasets
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