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. 2021 Mar 11:8:608369.
doi: 10.3389/fmolb.2021.608369. eCollection 2021.

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

Gongwei Long  1   2 , Wei Ouyang  1   2 , Yucong Zhang  1   2   3 , Guoliang Sun  1   2 , Jiahua Gan  1   2 , Zhiquan Hu  1   2 , Heng Li  1   2
Affiliations

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

Gongwei Long et al. Front Mol Biosci. .

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.

Keywords: DNA repair; biochemical recurrence; homologous recombination deficiency; nomogram; prostate cancer.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

FIGURE 1
FIGURE 1
Identification of prognostic DNA repair genes in prostate cancer (A) Univariate Cox regression analysis identified 67 DNA repair genes significantly associated with BCR (B) KEGG analysis of identified genes (C) GO analysis of identified genes (D) Parameter selection in the LASSO model (E) LASSO coefficient profiles of the prognostic genes.
FIGURE 2
FIGURE 2
Evaluation of the prognostic performance of the DRG signature in the training dataset and validation dataset (A) The time-dependent ROC for 1-, 2-, 3-, 4- and 5-years BCR predictions for the DRG signature in the training dataset (B) The Kaplan–Meier survival curves of the DRG signature. Patients from the training dataset were stratified into two groups according to the optimal cutoff value for the risk scores (C) The distribution of risk score, recurrence status, and gene expression panel in the training dataset (D) The time-dependent ROC for 1-, 2-, 3-, 4- and 5-years BCR predictions for the DRG signature in the validation dataset (E) The Kaplan–Meier survival curves of the DRG signature. Patients from the validation dataset were stratified into two groups according to the optimal cutoff values for the risk scores (F) The distribution of risk score, recurrence status, and gene expression panel in the validation dataset.
FIGURE 3
FIGURE 3
Clinical relevance of the DRG signature (A) The distribution of the DRG signature risk score according to different ages (B) The distribution of the DRG signature risk score according to different PSA (C) The distribution of the DRG signature risk score according to different pathologic T stage (D) The distribution of the DRG signature risk score according to different Gleason scores (E) The distribution of the DRG signature risk score according to different BCR status (F) The association between DRG signature and HRD score. *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001. Error bars were represented as Mean with SD.
FIGURE 4
FIGURE 4
Nomogram to predict BCR-free survival probability of patients with PCa (A) A prognostic nomogram predicting 1-, 3-, and 5-years BCR survival of PCa (B) The time-dependent ROC for 1-, 2-, 3-, 4- and 5-years BCR predictions for the nomogram in the training dataset (C) The time-dependent ROC for 1-, 2-, 3-, 4- and 5-years BCR predictions for the nomogram in the validation dataset (D) The Kaplan–Meier survival curves of the nomogram. Patients from the training dataset were stratified into two groups according to the optimal cutoff values for the risk scores (E) The Kaplan–Meier survival curves of the nomogram. Patients from the validation dataset were stratified into two groups according to the optimal cutoff values for the risk scores.
FIGURE 5
FIGURE 5
Comparison of the performance of the nomogram model, Walz’s model, and clinical model (A) ROC curves of the nomogram model, Walz’s model, and clinical model at 1–5 years in the training dataset (B) DCA curve to compare the performance of the nomogram model, Walz’s model, and clinical model at 1–5 years in the training dataset (C) ROC curves of the nomogram model, Walz’s model, and clinical model at 1–5 years in the validation dataset (D) DCA curve to compare the performance of the nomogram model, Walz’s model, and clinical model at 1–5 years in the validation dataset.
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