Gene signatures predict biochemical recurrence‐free survival in primary prostate cancer patients after radical therapy

• Published in: Cancer medicine (Malden, MA) MA), 2021-09, Vol.10 (18), p.6492-6502
• Main Authors: Su, Qiang, Liu, Zhenyu, Chen, Chi, Gao, Han, Zhu, Yongbei, Wang, Liusu, Pan, Meiqing, Liu, Jiangang, Yang, Xin, Tian, Jie
• Format: Article
• Language: English
• Subjects: biochemical recurrence‐free survival; Bioinformatics; Biomarkers, Tumor - genetics; Datasets; Datasets as Topic; Disease-Free Survival; Follow-Up Studies; Gene expression; Gene Expression Profiling; Gene Expression Regulation, Neoplastic; gene signature; Genomes; Humans; Kallikreins - blood; Kaplan-Meier Estimate; LASSO‐Cox regression; Male; Medical prognosis; Metastasis; Multivariate analysis; Neoplasm Grading; Neoplasm Recurrence, Local - blood; Neoplasm Recurrence, Local - diagnosis; Neoplasm Recurrence, Local - epidemiology; Neoplasm Recurrence, Local - genetics; Nomograms; Patients; Predictive Value of Tests; primary prostate cancer; Prostate cancer; Prostate-Specific Antigen - blood; Prostatic Neoplasms - blood; Prostatic Neoplasms - genetics; Prostatic Neoplasms - mortality; Prostatic Neoplasms - therapy; radical therapy; Regression analysis; Risk Assessment - methods; Risk Assessment - statistics & numerical data; ROC Curve; Survival; Transcriptome
• ISSN: 2045-7634; 2045-7634
• DOI: 10.1002/cam4.4092

Background This study evaluated the predictive value of gene signatures for biochemical recurrence (BCR) in primary prostate cancer (PCa) patients. Methods 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). Results 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). Conclusions 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. In this work, we established the risk score consisting of six gene signatures, which can predict biochemical recurrence in primary prostate cancer. Clinically, the nomogram model, which incorporates the 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.