Identification of Pathologic and Prognostic Genes in Prostate Cancer Based on Database Mining.
Identification of Pathologic and Prognostic Genes in Prostate Cancer Based on Database Mining.
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DOI：
10.3389/fgene.2022.854531
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发表时间：
2022
影响因子：
3.7
通讯作者：
Zhang J
中科院分区：
文献类型：
--
作者：
Liu K;Chen Y;Feng P;Wang Y;Sun M;Song T;Tan J;Li C;Liu S;Kong Q;Zhang J
背景资料：前列腺癌（PCa）是一种发生在泌尿系统的上皮性恶性肿瘤，发病率很高，是全球男性第二大常见癌症。因此，筛选出潜在的关键生物标志物对PCa的发病机制和预后至关重要。本研究旨在确定潜在的生物标志物，以揭示潜在的分子机制。 研究方法：应用R软件从TCGA-PRAD数据集中筛选出PCa组织与正常组织的差异表达基因（DEG）。进行加权基因共表达网络分析，主要是为了识别临床表现的统计学显著基因。基于STRING数据库结合Cytoscape软件进行蛋白质-蛋白质相互作用（PPI）网络分析和网络筛选。应用Cytoscape筛选Hub基因，结合逐步算法和多元考克斯回归分析构建风险模型。对不同临床表现的基因表达和与hub基因表达相关的生存率进行分析。此外，通过人类蛋白质图谱数据库验证了枢纽基因的蛋白质表达。 结果：从TCGA-PRAD数据集中共鉴定了1，621个DEG（870个下调基因和751个上调基因）。8个预后基因[BUB 1，KIF 2C，CCNA 2，CDC 20，CCNB 2，PBK，RRM 2和CDC 45]和4个枢纽基因（BUB 1，KIF 2C，CDC 20和PBK）与PCa的发病机制可能相关。构建了具有良好生存预测能力的预后模型，并通过GSE 21032中的数据集进行了验证。生存分析显示RRM 2的表达与PCa的预后有关，提示RRM 2可能在PCa的进展中起重要作用。 结论：提示RRM 2与PCa的预后相关，可作为PCa临床治疗的潜在靶点。
Background: Prostate cancer (PCa) is an epithelial malignant tumor that occurs in the urinary system with high incidence and is the second most common cancer among men in the world. Thus, it is important 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: Differentially expressed 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. Weighted gene co-expression network analysis was performed primarily to identify statistically significant genes for 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, the protein expression of hub genes was validated by the Human Protein Atlas database. Results: A total of 1,621 DEGs (870 downregulated genes and 751 upregulated genes) were identified from the TCGA-PRAD dataset. Eight prognostic genes [BUB1, KIF2C, CCNA2, CDC20, CCNB2, PBK, RRM2, and CDC45] and four hub genes (BUB1, KIF2C, CDC20, and PBK) potentially correlated 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.