A Jun, Zhang Baotong, Zhang Zhiqian, Hu Hailiang, Dong Jin-Tang
Department of Genetics and Cell Biology, College of Life Sciences, Nankai University, 94 Weijin Road, Tianjin 300071, China.
Department of Human Cell Biology and Genetics, School of Medicine, Southern University of Science and Technology, 1088 Xueyuan Road, Shenzhen 518055, China.
Cancers (Basel). 2021 Feb 22;13(4):917. doi: 10.3390/cancers13040917.
Molecular signatures predictive of recurrence-free survival (RFS) and castration resistance are critical for treatment decision-making in prostate cancer (PCa), but the robustness of current signatures is limited. Here, we applied the Robust Rank Aggregation (RRA) method to PCa transcriptome profiles and identified 287 genes differentially expressed between localized castration-resistant PCa (CRPC) and hormone-sensitive PCa (HSPC). Least absolute shrinkage and selection operator (LASSO) and stepwise Cox regression analyses of the 287 genes developed a 6-gene signature predictive of RFS in PCa. This signature included , , , , , and , and was named CRPC-derived prognosis signature (CRPCPS). Interestingly, three of these 6 genes constituted another signature capable of distinguishing CRPC from HSPC. The CRPCPS predicted RFS in 5/9 cohorts in the multivariate analysis and remained valid in patients stratified by tumor stage, Gleason score, and lymph node status. The signature also predicted overall survival and metastasis-free survival. The signature's robustness was demonstrated by the C-index (0.55-0.74) and the calibration plot in all nine cohorts and the 3-, 5-, and 8-year area under the receiver operating characteristic curve (0.67-0.77) in three cohorts. The nomogram analyses demonstrated CRPCPS' clinical applicability. The CRPCPS thus appears useful for RFS prediction in PCa.
预测无复发生存期(RFS)和去势抵抗的分子特征对于前列腺癌(PCa)的治疗决策至关重要,但目前特征的稳健性有限。在此,我们将稳健秩聚合(RRA)方法应用于PCa转录组图谱,并鉴定出287个在局限性去势抵抗性PCa(CRPC)和激素敏感性PCa(HSPC)之间差异表达的基因。对这287个基因进行最小绝对收缩和选择算子(LASSO)及逐步Cox回归分析,开发出一种预测PCa中RFS的6基因特征。该特征包括 、 、 、 、 和 ,并被命名为CRPC衍生预后特征(CRPCPS)。有趣的是,这6个基因中的3个构成了另一个能够区分CRPC和HSPC的特征。在多变量分析中,CRPCPS在5/9个队列中预测了RFS,并且在按肿瘤分期、Gleason评分和淋巴结状态分层的患者中仍然有效。该特征还预测了总生存期和无转移生存期。通过所有9个队列中的C指数(0.55 - 0.74)和校准图以及3个队列中3年、5年和8年的受试者工作特征曲线下面积(0.67 - 0.77)证明了该特征的稳健性。列线图分析证明了CRPCPS的临床适用性。因此,CRPCPS似乎对PCa中的RFS预测有用。