Establishment And Validation Of Coagulation Factor-based Nomogram For Predicting The Recurrence-free Survival Of Prostate Cancer

Abstract

Background: We aimed to establish and validate a coagulation-feature-based nomogram to predict recurrence-free survival for prostate cancer patients.Methods: The study contained 168 prostate cancer patients who had received radical prostatectomy between 2012 and 2018. The Kaplan-Meier plot and log-rank analysis were used to screen recurrence-free survival-related features. The nomogram was established by combining the significant coagulation features with clinicopathological characteristics by using Cox regression analysis. The accuracy and clinical significance of the nomogram model were assessed by receiver operating characteristic (ROC) curve, Kaplan-Meier plot, and calibration plot. Besides, we explored the correlation between coagulation pathway activity and patients’ prognosis based on public datasets by using gene set variation analysis (GSVA) analysis.Results: The results suggested that patients in the high-risk subgroup showed unfavorable prognoses than those in the low-risk subgroup classified by the nomogram model in both the training (log-rank P < 0.0001) and validation (log-rank P = 0.0004) cohorts. The nomogram model exhibited high discriminative accuracy in the training cohort [1-year area under the curve (AUC) of 0.74, and 3-year AUC of 0.69], which was confirmed in the internal validation cohort (C-index = 0.651). Besides, the calibration plots confirmed good concordance for the prediction of recurrence-free survival at 1 and 3 years. Besides, the subgroup analyses confirmed the usage of this model in different clinicopathological subgroups. Finally, GSVA analyses suggested that patients with higher coagulation pathway scores mostly had unfavorable prognoses than those with lower scores, a result consistent with the findings obtained above.Conclusions: In conclusion, we develop a practical nomogram model for the recurrence-free survival predicting of prostate cancer patients. This model may offer clinicians prognostic assessments and facilitate personalized treatment.