Construction and Validation of a Robust Cancer Stem Cell-Associated Gene Set-Based Signature to Predict Early Biochemical Recurrence in Prostate Cancer

Abstract

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, $P<0.001$ ). The results were further validated in the internal validation set (HR: 2.99, 95% CI: 1.34–6.70, $P=0.005$ ). Time-dependent ROC at 1 year suggested that the CSC gene signature ( $\text{AUC}=0.800$ ) 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.