Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine and Engineering, Beihang University, Beijing, China.
Clinical Laboratory Medicine, Beijing Shijitan Hospital, Capital Medical University, Beijing, China.
Cancer Med. 2021 Sep;10(18):6492-6502. doi: 10.1002/cam4.4092. Epub 2021 Aug 28.
This study evaluated the predictive value of gene signatures for biochemical recurrence (BCR) in primary prostate cancer (PCa) patients.
Clinical features and gene expression profiles of PCa patients were attained from Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) datasets, which were further classified into a training set (n = 419), a validation set (n = 403). The least absolute shrinkage and selection operator Cox (LASSO-Cox) method was used to select discriminative gene signatures in training set for biochemical recurrence-free survival (BCRFS). Selected gene signatures established a risk score system. Univariate and multivariate analyses of prognostic factors about BCRFS were performed using the Cox proportional hazards regression models. A nomogram based on multivariate analysis was plotted to facilitate clinical application. Kyoto Encyclopedia of Gene and Genomes (KEGG) and Gene Ontology (GO) analyses were then executed for differentially expressed genes (DEGs).
Notably, the risk score could significantly identify BCRFS by time-dependent receiver operating characteristic (t-ROC) curves in the training set (3-year area under the curve (AUC) = 0.820, 5-year AUC = 0.809) and the validation set (3-year AUC = 0.723, 5-year AUC = 0.733).
Clinically, the nomogram model, which incorporates Gleason score and the risk score, could effectively predict BCRFS and potentially be utilized as a useful tool for the screening of BCRFS in PCa.
本研究评估了基因特征对原发性前列腺癌（PCa）患者生化复发（BCR）的预测价值。
从基因表达综合数据库（GEO）和癌症基因组图谱（TCGA）数据库中获得 PCa 患者的临床特征和基因表达谱，并将其进一步分为训练集（n=419）和验证集（n=403）。最小绝对收缩和选择算子 Cox（LASSO-Cox）方法用于在训练集中选择用于生化无复发生存（BCRFS）的鉴别基因特征。选择的基因特征建立了风险评分系统。使用 Cox 比例风险回归模型对 BCRFS 的预后因素进行单因素和多因素分析。基于多因素分析绘制列线图以促进临床应用。然后对差异表达基因（DEGs）进行京都基因与基因组百科全书（KEGG）和基因本体论（GO）分析。
值得注意的是，风险评分可以通过时间依赖性接收器工作特征（t-ROC）曲线在训练集（3 年 AUC=0.820，5 年 AUC=0.809）和验证集（3 年 AUC=0.723，5 年 AUC=0.733）中显著识别 BCRFS。
临床上，包含 Gleason 评分和风险评分的列线图模型可以有效预测 BCRFS，并且可能被用作 PCa 中 BCRFS 筛查的有用工具。
