Department of Urology Surgery, The Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei Province, P. R. China.
Medicine (Baltimore). 2024 Aug 23;103(34):e39406. doi: 10.1097/MD.0000000000039406.
Prostate cancer is a malignant tumor originating from the prostate gland, significantly affecting patients' quality of life and survival rates. Public data was utilized to identify differentially expressed genes (DEGs). Weighted gene co-expression network analysis was constructed to classify gene modules. Functional enrichment analysis was performed through Kyoto Encyclopedia of Genes and Genomes and gene ontology annotations, with results visualized using the Metascape database. Additionally, gene set enrichment analysis evaluated gene expression profiles and related pathways, constructed a protein-protein interaction network to predict core genes, analyzed survival data, plotted heatmaps and radar charts, and predicted microRNAs for core genes through miRTarBase. Two prostate cancer datasets (GSE46602 and GSE55909) were analyzed, identifying 710 DEGs. Gene ontology and Kyoto Encyclopedia of Genes and Genomes analyses revealed that DEGs were primarily involved in organic acid metabolism and the P53 signaling pathway. Gene set enrichment analysis and Metascape analyses further confirmed the significance of these pathways. After constructing the weighted gene co-expression network analysis network, 3 core genes (DDX21, NOP56, plasmacytoma variant translocation 1 [PVT1]) were identified. Survival analysis indicated that core genes are closely related to patient prognosis. Through comparative toxicogenomics database and miRNA prediction analysis, PVT1 was considered to play a crucial role in the development of prostate cancer. The PVT1 gene is highly expressed in prostate cancer and has the potential to become a diagnostic biomarker and therapeutic target for prostate cancer.
前列腺癌是一种起源于前列腺的恶性肿瘤,严重影响患者的生活质量和生存率。本研究利用公共数据识别差异表达基因(DEGs)。构建加权基因共表达网络分析对基因模块进行分类。通过京都基因与基因组百科全书和基因本体论注释进行功能富集分析,使用 Metascape 数据库可视化结果。此外,通过基因集富集分析评估基因表达谱和相关途径,构建蛋白质-蛋白质相互作用网络预测核心基因,分析生存数据,绘制热图和雷达图,并通过 miRTarBase 预测核心基因的 microRNAs。分析了两个前列腺癌数据集(GSE46602 和 GSE55909),鉴定出 710 个 DEGs。基因本体论和京都基因与基因组百科全书分析表明,DEGs 主要参与有机酸代谢和 P53 信号通路。基因集富集分析和 Metascape 分析进一步证实了这些途径的重要性。构建加权基因共表达网络分析网络后,鉴定出 3 个核心基因(DDX21、NOP56、浆细胞瘤变异易位 1 [PVT1])。生存分析表明,核心基因与患者预后密切相关。通过比较毒理学基因组数据库和 miRNA 预测分析,认为 PVT1 在前列腺癌的发生发展中起关键作用。PVT1 基因在前列腺癌中高表达,有可能成为前列腺癌的诊断生物标志物和治疗靶点。