Integrating Radiosensitivity Gene Signature Improves Prostate Cancer Outcome Prediction

Abstract Background This study aimed to establish a nomogram that combine 31-gene signature (31-GS), radiosensitivity index (RSI) and radiation resistance related gene index (RRRI) for predicting recurrence and in prostate cancer (PCa) patients.Methods The transcriptome data of PCa were obtained from GEO and TCGA to validate the predictive potential of three sets of published biomarkers, the 31-GS, RSI and RRRI. To adjust these markers for the characteristics of prostate cancer, we integrated four new PCa-associated radiosensitivity predictive indexes based on 31-GS, RSI and RRRI by the Cox analysis and Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis. A time-dependent receiver operating characteristic (ROC) curve, decision curve analyses (DCA), integrated discrimination improvement (IDI), and net reclassification improvement (NRI) were used to compare the radiosensitivity predictive ability of these four gene signatures. a nomogram was built to improve the recurrence prediction capability.Results We validated and compared the predictive potential of two published predictive indexes. Based on the 31-GS, RSI and RRRI, we integrated four PCa-associated radiosensitivity predictive indexes: 14Genes, RSI, RRRI, 20Genes. Among them, a 14-gene radiosensitivity predictive index showed the most promising predictive performance and discriminative capacity. The area under receiver operating characteristic curve for 14-gene is the highest in both TCGA and GSE cohort.Conclusions This study successfully established a Radiosensitivity related nomogram which had an excellent performance in predicting recurrence in patients with PCa. For patient who received radiation therapy, 20Genes and RRRI model can be used in predicting RFS while 20Genes is more radiotherapy-specific but needs further external validation.