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整合分析揭示了基于 microRNA 的乳头状肾细胞癌预后预测特征。

Integrated Analysis Revealed the MicroRNA-Based Prognostic Predicting Signature for Papillary Renal Cell Carcinoma.

机构信息

Department of Urology, Head and Neck Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, China.

Institute of Urology, Anhui Medical University, Hefei, China.

出版信息

DNA Cell Biol. 2021 Mar;40(3):532-542. doi: 10.1089/dna.2019.5306. Epub 2021 Feb 22.

Abstract

Renal cell carcinoma (RCC) is one of the most frequently occurring tumors worldwide. Herein, we established a microRNA (miRNA) predicting signature to assess the prognosis of papillary-type RCC (PRCC) patients. miR-1293, miR-34a, miR-551b, miR-937, miR-299, and miR-3199-2 were used in building the overall survival (OS)-related signature, whereas miR-7156, miR-211, and miR-301b were used to construct the formula of recurrence-free survival (RFS) with the help of LASSO Cox regression analysis. The Kaplan-Meier and receiver operating characteristic curves indicated good discrimination and efficiency of the two signatures. Functional annotation for the downstream genes of the OS/RFS-related miRNAs exposed the potential mechanisms of PRCC. Notably, the multivariate analyses suggested that the two signatures were independent risk factors for PRCC patients and had better prognostic capacity than any other classifier. In addition, the nomogram indicated synthesis effects and showed better predictive performance than clinicopathologic features and our signatures. We validated the OS and RFS prediction formulas in clinical samples and met our expectations. Finally, we established two novel miRNA-based OS and RFS predicting signatures for PRCC, which are reliable tools for assessing the prognosis of PRCC patients.

摘要

肾细胞癌(RCC)是全球最常见的肿瘤之一。在此,我们建立了一个 microRNA(miRNA)预测特征,以评估乳头状 RCC(PRCC)患者的预后。miR-1293、miR-34a、miR-551b、miR-937、miR-299 和 miR-3199-2 用于构建总生存(OS)相关特征,而 miR-7156、miR-211 和 miR-301b 则用于构建无复发生存(RFS)公式,借助 LASSO Cox 回归分析。Kaplan-Meier 和接收者操作特征曲线表明这两个特征具有良好的区分度和效率。对 OS/RFS 相关 miRNA 的下游基因进行功能注释揭示了 PRCC 的潜在机制。值得注意的是,多变量分析表明,这两个特征是 PRCC 患者的独立危险因素,其预后能力优于任何其他分类器。此外,列线图表明综合效应,并显示出比临床病理特征和我们的特征更好的预测性能。我们在临床样本中验证了 OS 和 RFS 预测公式,符合我们的预期。最后,我们建立了两个新的基于 miRNA 的 OS 和 RFS 预测特征,可作为评估 PRCC 患者预后的可靠工具。

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