Department of Urology, The First Affiliated Hospital of Anhui Medical University and Institute of Urology and Anhui Province Key Laboratory of Genitourinary Diseases, Anhui Medical University, Jixi Road 218th, Shushan District, Hefei, Anhui, 230022, People's Republic of China.
Guangdong and Shenzhen Key Laboratory of Male Reproductive Medicine and Genetics, Institute of Urology, Peking University Shenzhen Hospital, Shenzhen-Peking University-the Hong Kong University of Science and Technology Medical Center, Shenzhen 518000, China.
Int J Med Sci. 2021 Jan 1;18(1):284-294. doi: 10.7150/ijms.49412. eCollection 2021.
Recurrence is a major problem for prostate cancer patients, thus, identifying prognosis-related markers to evaluate clinical outcomes is essential. Here, we established a fifteen-miRNA-based recurrence-free survival (RFS) predicting signature based on the miRNA expression profile extracted from The Cancer Genome Atlas (TCGA) database by the LASSO Cox regression analysis. The median risk score generated by the signature in both the TCGA training and the external Memorial Sloan-Kettering Cancer Center (MSKCC) validation cohorts was employed and the patients were subclassified into low- and high-risk subgroups. The Kaplan-Meier plot and log-rank analyses showed significant survival differences between low- and high-risk subgroups of patients (TCGA, log-rank < 0.001 & MSKCC, log-rank = 0.045). In addition, the receiver operating characteristic curves of both the training and external validation cohorts indicated the good performance of our model. After predicting the downstream genes of these miRNAs, the miRNA-mRNA network was visualized by Cytoscape software. In addition, pathway analyses found that the differences between two groups were mainly enriched on tumor progression and drug resistance-related pathways. Multivariate analyses revealed that the miRNA signature is an independent indicator of RFS prognosis for prostate cancer patients with or without clinicopathological features. In summary, our novel fifteen-miRNA-based prediction signature is a reliable method to evaluate the prognosis of prostate cancer patients.
复发是前列腺癌患者的主要问题，因此，确定与预后相关的标志物来评估临床结果至关重要。在这里，我们通过 LASSO Cox 回归分析，基于从癌症基因组图谱 (TCGA) 数据库中提取的 miRNA 表达谱，建立了一个基于 15 个 miRNA 的无复发生存 (RFS) 预测特征。该特征在 TCGA 训练队列和外部纪念斯隆-凯特琳癌症中心 (MSKCC) 验证队列中生成的中位数风险评分用于将患者分为低风险和高风险亚组。Kaplan-Meier 图和对数秩分析显示，患者的低风险和高风险亚组之间存在显著的生存差异 (TCGA，对数秩 < 0.001 和 MSKCC，对数秩 = 0.045)。此外，两个训练和外部验证队列的受试者工作特征曲线表明了我们模型的良好性能。预测这些 miRNA 的下游基因后，使用 Cytoscape 软件可视化 miRNA-mRNA 网络。此外，通路分析发现两组之间的差异主要富集在肿瘤进展和耐药相关通路。多变量分析表明，miRNA 特征是有或无临床病理特征的前列腺癌患者 RFS 预后的独立指标。总之，我们的新型基于 15 个 miRNA 的预测特征是评估前列腺癌患者预后的可靠方法。
