Construction of Potential Gene Expression and Regulation Networks in Prostate Cancer Using Bioinformatics Tools

• Main Authors: Heyu Liu, Lirong Li, Yuan Fan, Yaping Lu, Changhong Zhu, Wei Xia
• Format: Article
• Language: English
• Published: Hindawi Limited 2021-01-01
• Series: Oxidative Medicine and Cellular Longevity
• Online Access: http://dx.doi.org/10.1155/2021/8846951

Objective. To identify the key genes involved in prostate cancer and their regulatory network. Methods. The dataset of mRNA/miRNA transcriptome sequencing was downloaded from The Cancer Genome Atlas/the Gene Expression Omnibus database for analysis. The “edgeR” package in the R environment was used to normalize and analyze differentially expressed genes (DEGs) and miRNAs (DEmiRNAs). First, the PANTHER online tool was used to analyze the function enrichment of DEGs. Next, a protein-protein interaction (PPI) network was constructed using STRING and Cytoscape tools. Finally, miRNA-gene regulatory networks were constructed using the miRTarBase. Results. We identified 4339 important DEGs, of which 2145 were upregulated (Up-DEGs) and 2194 were downregulated (Down-DEGs). Functional enrichment analysis showed that the Up-DEGs were related to the immune system and the cell cycle in prostate cancer, whereas the Down-DEGs were related to the nucleic acid metabolic process and metabolism pathways. Twelve core protein clusters were found in the PPI network. Further, the constructed miRNA-gene interaction network showed that 11 downregulated miRNAs regulated 16 Up-DEGs and 22 upregulated miRNAs regulated 22 Down-DEGs. Conclusion. We identified 4339 genes and 70 miRNAs that may be involved in immune response, cell cycle, and other key pathways of the prostate cancer regulatory network. Genes such as BUB1B, ANX1A1, F5, HTR4, and MUC4 can be used as biomarkers to assist in the diagnosis and prognosis of prostate cancer.