Wu Peiqiang, Zhang Jianlei, Guo Li, Chen Bohong, Xiong Lingxiao, Du Yuefeng
Department of Urology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, 710061, People's Republic of China.
Adv Appl Bioinform Chem. 2024 Nov 30;17:119-138. doi: 10.2147/AABC.S489131. eCollection 2024.
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.
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.
The 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.
We successfully created a robust model. The result of the study indicates that LAMP5 could contribute to cell proliferation and invasion in PCa.
前列腺癌（PCa）的发展在很大程度上取决于氧化应激（OS）水平的升高和抗氧化系统的不足。识别与氧化应激相关的基因对于指导PCa治疗和未来研究至关重要。
TCGA和GTEx数据库提供了批量RNA测序数据，GEO数据库提供了单细胞数据GSE141445。利用活性氧（ROS）标记，使用R包“Seurat”和“AUCell”进行与氧化应激相关的单细胞分析和聚类识别。使用“limma”R包识别正常和PCa样本中的差异表达基因（DEG）。采用LASSO回归分析建立复发评分（RS）模型。分别使用R包“maftools”和CIBERSORT方法探索基因突变和浸润性免疫细胞。在进行随机森林分析后，选择LAMP5进行进一步研究。
发现PCa模型是一个独立的预测因子。高风险和低风险评分组之间的肿瘤免疫微环境和基因突变频率存在显著差异。进一步研究表明，体外实验结果显示，PC-3和DU145细胞系中LAMP5的下调抑制了细胞增殖和侵袭。
我们成功创建了一个强大的模型。研究结果表明，LAMP5可能促进PCa中的细胞增殖和侵袭。
