AUTHOR=Liu Kun , Chen Yijun , Feng Pengmian , Wang Yucheng , Sun Mengdi , Song Tao , Tan Jun , Li Chunyang , Liu Songpo , Kong Qinghong , Zhang Jidong 

TITLE=Identification of Pathologic and Prognostic Genes in Prostate Cancer Based on Database Mining

JOURNAL=Frontiers in Genetics

VOLUME=Volume 13 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2022.854531

DOI=10.3389/fgene.2022.854531

ISSN=1664-8021

ABSTRACT=Background: Prostate cancer (PCa) is an epithelial malignant tumor that occurs in the urinary system with high incidence, which is the second most common male cancer in the world. Thus, it is urgent to screen out potential key biomarkers for the pathogenesis and prognosis of PCa. The present study aimed to identify potential biomarkers to reveal the underlying molecular mechanisms. 
Methods: Differential expression genes (DEGs) between PCa tissues and matched normal tissues from The Cancer Genome Atlas Prostate Adenocarcinoma (TCGA-PRAD) dataset were screened out by R software. WGCNA was performed to initially identify genes statistically significant to clinical manifestations. Protein-protein interaction (PPI) network analysis and network screening were performed based on the STRING database in conjunction with Cytoscape software. Hub genes were then screened out by cytoscape in conjunction with stepwise algorithm and multivariate Cox regression analysis to construct a risk model. Gene expression in different clinical manifestations and survival analysis correlated with the expression of hub genes were performed. Moreover, protein expression of hub genes was validated by HPA database.
Results: 870 downregulated genes and 751 upregulated genes were identified from TCGA-PRAD dataset. 8 prognostic genes (BUB1, KIF2C, CCNA2, CDC20, CCNB2, PBK, RRM2, CDC45) and 4 hub genes (BUB1, KIF2C, CDC20, PBK) potentially corelated with the pathogenesis of PCa were identified. A prognostic model with good predictive power for survival was constructed and was validated by the dataset in GSE21032. The survival analysis demonstrated that the expression of RRM2 was statistically significant to the prognosis of PCa, indicating that RRM2 may potentially play an important role in the PCa progression. 
Conclusion: The present study implied that RRM2 was associated with prognosis and could be used as a potential therapeutic target for PCa clinical treatment.