1. Gene signatures predict biochemical recurrence‐free survival in primary prostate cancer patients after radical therapy
- Author
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Han Gao, Liusu Wang, Jie Tian, Yongbei Zhu, Zhenyu Liu, Meiqing Pan, Xin Yang, Qiang Su, Chi Chen, and Jiangang Liu
- Subjects
Biochemical recurrence ,Oncology ,Male ,Cancer Research ,medicine.medical_specialty ,Multivariate analysis ,Bioinformatics ,Datasets as Topic ,Kaplan-Meier Estimate ,biochemical recurrence‐free survival ,Risk Assessment ,gene signature ,Disease-Free Survival ,LASSO‐Cox regression ,Prostate cancer ,primary prostate cancer ,Predictive Value of Tests ,Internal medicine ,medicine ,Biomarkers, Tumor ,Humans ,Radiology, Nuclear Medicine and imaging ,Research Articles ,RC254-282 ,Framingham Risk Score ,Receiver operating characteristic ,business.industry ,Gene Expression Profiling ,Univariate ,Prostatic Neoplasms ,Neoplasms. Tumors. Oncology. Including cancer and carcinogens ,radical therapy ,Nomogram ,Gene signature ,Prostate-Specific Antigen ,medicine.disease ,Gene Expression Regulation, Neoplastic ,Nomograms ,ROC Curve ,Kallikreins ,Neoplasm Grading ,Neoplasm Recurrence, Local ,business ,Transcriptome ,Follow-Up Studies ,Research Article - Abstract
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.
- Published
- 2021