| Popis: |
Prostate cancer (PoC) is currently one of the most frequent cancers in males. Though a lot of work has been done on it, the biomarkers in the progression of prostate cancer have not been fully identified. In this research, we used the GSE70466 and GSE104131 for RNA-Seq gene expression datasets to reveal the important biomarkers and related pathways of prostate cancer. Furthermore, Differentially Expressed Genes (DEGs) were extracted from the dataset with the R statistical language tools, and to investigate the functional enrichment of the DEGs, an online repository Enrichr was used. Then, another database, STRING was used in this work to generate the protein-protein interaction (PPI) network and represented the network using the well-known Cytoscape software. After that, a Cystoscape plug-in MCODE identified clusters from the PPI network. In the end, some drug compounds that may be useful in the therapy of PoC have been revealed. A total of 83 common DEGs (43 upregulated and 40 downregulated) were exposed using some statistical criteria. Overexpressed DEGs were engaged in the metabolism of glutamine family amino acids and alpha-amino acids, while underexpressed DEGs were mostly involved in the regulation of the transmembrane receptor protein serine/threonine kinase signaling pathway and positive regulation of potassium ion transport. In addition, the overexpressed DEGs were highly involved with Arginine and Proline metabolism, whereas the underexpressed DEGs were connected with Proteoglycans in cancer, according to the KEGG pathway analysis. A total of 154 nodes and 2068 connections were used to construct the PPI. Using the connectivity method, we conclude 20 genes (INS; VEGFA; CDH1; IGF1; ITGB1; IGF1R; CAV1; ESR1; KDR; TGFB1; CD44; MMP9; PIK3R1; SHC1; IGF2; SMAD4; TGFBR1; ENG; IRS1; VWF) as hub genes. Finally, we identified and validated two important biomarkers as well as certain pharmacological compounds that could be effective in the therapy of PoC. |
