Identification of Prostate Cancer Risk Genetics Biomarkers Based on Intergraded Bioinformatics Analysis - Liang, Xiangdong; Wang, Yanchao; Pei, Long; Tan, Xiaoliang; Dong, Chunhui (Frontiers in Surgery, 2022)
Background: Prostate cancer (PCa) is one of the most popular cancer types in men. Nevertheless, the pathogenic mechanisms of PCa are poorly understood. Hence, we aimed to identify the potential genetic biomarker of PCa in the present study. Methods: High-throughput data set GSE46602 was obtained from the Comprehensive Gene Expression Database (GEO) for screening differentially expressed genes (DEGs). The common DEGs were further screened out using TCGA dataset. Functional enrichment analysis includes GO and KEGG to study related mechanisms and related mechanisms.Cox regression and lasso regression analysis were carried out to compress the target genes and constructed high-risk and low-risk gene model. Survival analyses were performed based on gene risk signature model. The CIBERSORT algorithm was performed to clarify the correlation of high and low risk gene model in risk and infiltration of immune cells in PCa. Results: A total of 385 common differentially expressed genes were obtained. The results of functional enrichment analysis show that common DEGs plays an important role in PCa. A three-gene signature model (KCNK3, AK5, and ARHGEF38) was established and the model was significantly associated with cancer-related pathways, overall survival (OS), tumor microenvironment (TME) related immune cells in PCa. Conclusion: This new risk model may contribute to further investigation in the immune-related pathogenesis in progression of PCa.