Background. Postoperative early biochemical recurrence (BCR) was an essential indicator for recurrence and distant metastasis of prostate cancer (PCa). The aim of this study was to construct a cancer stem cell- (CSC-) associated gene set-based signature to identify a subgroup of PCa patients who are at high risk of early BCR. Methods. The PCa dataset from The Cancer Genome Atlas (TCGA) was randomly separated into discovery and validation set. Patients in discovery set were divided into early BCR group and long-term survival group. Propensity score matching analysis and differentially expressed gene selection were used to identify candidate CSC-associated genes. The LASSO Cox regression model was finally performed to filter the most useful prognostic CSC-associated genes for predicting early BCR. Results. By applying the LASSO Cox regression model, we built a thirteen-CSC-associated gene-based early BCR-predicting signature. In the discovery set, patients in high-risk group showed significantly poorer BCR free survival than that patients in low-risk group (HR: 4.91, 95% CI: 2.75–8.76, ). The results were further validated in the internal validation set (HR: 2.99, 95% CI: 1.34–6.70, ). Time-dependent ROC at 1 year suggested that the CSC gene signature () possessed better predictive value than any other clinicopathological features in the entire TCGA cohort. Additionally, survival decision curve analysis revealed a considerable clinical usefulness of the CSC gene signature. Conclusions. We successfully developed a CSC-associated gene set-based signature that can accurately predict early BCR in PCa cancer.

中文翻译：

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构建和验证稳健的癌症干细胞相关基因组特征以预测前列腺癌的早期生化复发

背景。术后早期生化复发（BCR）是前列腺癌（PCa）复发和远处转移的重要指标。本研究的目的是构建癌症干细胞 (CSC-) 相关的基于基因组的特征，以识别早期 BCR 高风险的 PCa 患者亚组。方法。来自癌症基因组图谱 (TCGA) 的 PCa 数据集被随机分成发现和验证集。发现组患者分为早期BCR组和长期生存组。倾向得分匹配分析和差异表达基因选择用于鉴定候选CSC相关基因。最后执行 LASSO Cox 回归模型以过滤最有用的预后 CSC 相关基因，以预测早期 BCR。结果。通过应用 LASSO Cox 回归模型，我们构建了一个基于 13 个 CSC 相关基因的早期 BCR 预测特征。在发现组中，高风险组患者的无 BCR 生存率明显低于低风险组患者（HR：4.91，95% CI：2.75-8.76，）。结果在内部验证集中得到进一步验证（HR：2.99，95% CI：1.34-6.70，）。1 年的时间依赖性 ROC 表明 CSC 基因特征 ()比整个 TCGA 队列中的任何其他临床病理学特征具有更好的预测价值。此外，生存决策曲线分析揭示了 CSC 基因特征的相当大的临床有用性。结论。我们成功开发了一种基于 CSC 相关基因集的特征，可以准确预测 PCa 癌的早期 BCR。