Department of Urology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Ji'nan, 250012, PR China.
Department of Medicine, Center for Molecular Medicine (CMM) and Bioclinicum, Karolinska Institute and Karolinska University Hospital Solna, Solna 171 64, Sweden.
Int J Med Sci. 2021 Jan 1;18(4):953-963. doi: 10.7150/ijms.50704. eCollection 2021.
RNA binding protein (RBPs) dysregulation has been reported in various malignant tumors and plays a pivotal role in tumor carcinogenesis and progression. However, the underlying mechanisms in renal cell carcinoma (RCC) are still unknown. In the present study, we performed a bioinformatics analysis using data from TCGA database to explore the expression and prognostic value of RBPs. We identified 125 differently expressed RBPs between tumor and normal tissue in RCC patients, including 87 upregulated and 38 downregulated RBPs. Eight RBPs (RPL22L1, RNASE2, RNASE3, EZH2, DDX25, DQX1, EXOSC5, DDX47) were selected as prognosis-related RBPs and used to construct a risk score model. In the risk score model, the high-risk subgroup had a poorer overall survival (OS) than the low-risk subgroup, and we divided the 539 RCC patients into two groups and conducted a time-dependent receiver operating characteristic (ROC) analysis to further test the prognostic ability of the eight hub RBPs. The area under the curve (AUC) of the ROC curve was 0.728 in train-group and 0.688 in test-group, indicating a good prognostic model. More importantly, we established a nomogram based on the selected eight RBPs. The eight selected RBPS have predictive value for RCC patients, with potential applications in clinical decision-making and individualized treatment.
RNA 结合蛋白 (RBPs) 的失调已在各种恶性肿瘤中得到报道,并在肿瘤的发生和发展中发挥关键作用。然而,在肾细胞癌 (RCC) 中的潜在机制仍不清楚。在本研究中,我们使用 TCGA 数据库中的数据进行了生物信息学分析,以探讨 RBPs 的表达和预后价值。我们鉴定了 RCC 患者肿瘤组织和正常组织之间的 125 个差异表达的 RBPs,其中包括 87 个上调和 38 个下调的 RBPs。有 8 个 RBPs(RPL22L1、RNASE2、RNASE3、EZH2、DDX25、DQX1、EXOSC5、DDX47)被选为与预后相关的 RBPs,并用于构建风险评分模型。在风险评分模型中,高风险亚组的总生存期 (OS) 明显差于低风险亚组,我们将 539 名 RCC 患者分为两组,并进行时间依赖性接受者操作特征 (ROC) 分析,以进一步测试八个关键 RBPs 的预后能力。训练组的 ROC 曲线下面积 (AUC) 为 0.728,测试组为 0.688,表明该预后模型具有良好的性能。更重要的是,我们基于选定的八个 RBPs 建立了一个列线图。这八个选定的 RBPs 对 RCC 患者具有预测价值,可能在临床决策和个体化治疗中有应用前景。