Identification of a DNA Repair Gene Signature and Establishment of a Prognostic Nomogram Predicting Biochemical-Recurrence-Free Survival of Prostate Cancer.

Gongwei Long,Yucong Zhang,Jiahua Gan,Guoliang Sun,Heng Li,Wei Ouyang,Zhiquan Hu

doi:10.3389/fmolb.2021.608369

Abstract

Background: The incidence of prostate cancer (PCa) is high and increasing worldwide. The prognosis of PCa is relatively good, but it is important to identify the patients with a high risk of biochemical recurrence (BCR) so that additional treatment could be applied. Method: Level 3 mRNA expression and clinicopathological data were obtained from The Cancer Genome Atlas (TCGA) to serve as training data. The GSE84042 dataset was used as a validation set. Univariate Cox, lasso Cox, and stepwise multivariate Cox regression were applied to identify a DNA repair gene (DRG) signature. The performance of the DRG signature was assessed based on Kaplan–Meier curve, receiver operating characteristic (ROC), and Harrell’s concordance index (C-index). Furtherly, a prognostic nomogram was established and evaluated likewise. Results: A novel four DRG signature was established to predict BCR of PCa, which included POLM, NUDT15, AEN, and HELQ. The ROC and C index presented good performance in both training dataset and validation dataset. The patients were stratified by the signature into high- and low-risk groups with distinct BCR survival. Multivariate Cox analysis revealed that the DRG signature is an independent prognostic factor for PCa. Also, the DRG signature high-risk was related to a higher homologous recombination deficiency (HRD) score. The nomogram, incorporating the DRG signature and clinicopathological parameters, was able to predict the BCR with high efficiency and showed superior performance compared to models that consisted of only clinicopathological parameters. Conclusion: Our study identified a DRG signature and established a prognostic nomogram, which were reliable in predicting the BCR of PCa. This model could help with individualized treatment and medical decision making.

Highlights

Prostate cancer (PCa) is one of the most frequently diagnosed neoplasm all over the world, with an estimated 191,930 new cases and 33,330 death in 2020 in the United States (Siegel et al, 2020)
Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene ontology (GO) analyses were used to clarify the biological processes and pathways related to these significant genes (Figures 1B,C), which revealed that these genes were primarily involved in Fanconi anemia, DNA damage response, and DNA repair pathways
Results showed that for predicting biochemical recurrence (BCR)-free survival in the The Cancer Genome Atlas (TCGA) dataset at 1st, 2nd, 3rd, 4th, and 5th year, the DNA repair gene (DRG) risk score had AUC values of 0.827, 0.774, 0.810, 0.720, and 0.691 (Figure 2A)

Summary

Introduction

Prostate cancer (PCa) is one of the most frequently diagnosed neoplasm all over the world, with an estimated 191,930 new cases and 33,330 death in 2020 in the United States (Siegel et al, 2020). The HRD is a predictive marker for therapy with PARP inhibition (PARPi) such as Olaparib in PCa and other kinds of cancers (Kaufman et al, 2015; Mateo et al, 2015; Robson et al, 2017; Mateo et al, 2020). These issues indicated that DDR defects could be powerful prognostic factors in PCa. The incidence of prostate cancer (PCa) is high and increasing worldwide. The prognosis of PCa is relatively good, but it is important to identify the patients with a high risk of biochemical recurrence (BCR) so that additional treatment could be applied

Methods

Results

Discussion

Conclusion