Abstract
BACKGROUND: Prostate cancer (PCa) development largely depends on increased levels of oxidative stress (OS) and a deficient anti-oxidative system. Identifying genes associated with oxidative stress is critical in order to direct PCa therapy and future research. METHODS: The TCGA and GTEx databases provided the bulk RNA-seq data, and the GEO database provided the single-cell data GSE141445. Utilizing reactive oxygen species (ROS) markers, single-cell analysis and cluster identification related to oxidative stress were conducted using the R packages "Seurat" and "AUCell". The differentially expressed genes (DEGs) in normal and PCa samples were identified with the "limma" R package. LASSO regression analysis was used to build a recurrence score (RS) model. The R packages "maftools" and the CIBERSORT method were employed to explore genetic mutation and the infiltrating immune cell, respectively. LAMP5 was chosen for further investigation after random forest analysis was performed. RESULTS: The RS model for PCa was found to be an independent predictor. The tumor immune microenvironment and the frequency of gene mutations differed significantly between the high- and low-risk score groups. Further investigation revealed that downregulation of LAMP5 in PC-3 and DU145 cell lines suppressed cell proliferation and invasion, as demonstrated by the results of in vitro experiments. CONCLUSION: We successfully created a robust RS model. The result of the study indicates that LAMP5 could contribute to cell proliferation and invasion in PCa.