Open AccessGene signatures predict biochemical recurrence‐free survival in primary prostate cancer patients after radical therapy
Author(s) -
Su Qiang,
Liu Zhenyu,
Chen Chi,
Gao Han,
Zhu Yongbei,
Wang Liusu,
Pan Meiqing,
Liu Jiangang,
Yang Xin,
Tian Jie
Publication year - 2021
Publication title -
cancer medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.403
H-Index - 53
ISSN - 2045-7634
DOI - 10.1002/cam4.4092
Subject(s) - nomogram , receiver operating characteristic , univariate , proportional hazards model , kegg , biochemical recurrence , prostate cancer , oncology , multivariate statistics , medicine , lasso (programming language) , computational biology , cancer , gene , biology , gene expression , computer science , prostatectomy , transcriptome , genetics , machine learning , world wide web
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.