Abstract
Background: Biochemical recurrence (BCR) is an indicator of prostate cancer (PCa)-specific recurrence and mortality. However, there is a lack of an effective prediction model that can be used to predict prognosis and to determine the optimal method of treatment for patients with BCR. Hence, the aim of this study was to construct a protein-based nomogram that could predict BCR in PCa.Methods: Protein expression data of PCa patients was obtained from The Cancer Proteome Atlas (TCPA) database. Clinical data on the patients was downloaded from The Cancer Genome Atlas (TCGA) database. Lasso and Cox regression analyses were conducted to select the most significant prognostic proteins and formulate a protein signature that could predict BCR. Subsequently, Kaplan–Meier survival analysis and Cox regression analyses were conducted to evaluate the performance of the prognostic protein-based signature. Additionally, a nomogram was constructed using multivariate Cox regression analysis.Results: We constructed a 5-protein-based prognostic prediction signature that could be used to identify high-risk and low-risk groups of PCa patients. The survival analysis demonstrated that patients with a higher BCR showed significantly worse survival than those with a lower BCR (p < 0.0001). The time-dependent receiver operating characteristic curve showed that the signature had an excellent prognostic efficiency for 1, 3, and 5-year BCR (area under curve in training set: 0.691, 0.797, 0.808 and 0.74, 0.739, 0.82 in the test set). Univariate and multivariate analyses indicated that this 5-protein signature could be used as independent prognosis marker for PCa patients. Moreover, the concordance index (C-index) confirmed the predictive value of this 5-protein signature in 3, 5, and 10-year BCR overall survival (C-index: 0.764, 95% confidence interval: 0.701–0.827). Finally, we constructed a nomogram to predict BCR of PCa.Conclusions: Our study identified a 5-protein-based signature and constructed a nomogram that could reliably predict BCR. The findings might be of paramount importance for the prediction of PCa prognosis and medical decision-making.Subjects: Bioinformatics, oncology, urology.
Highlights
Prostate cancer (PCa) is the second leading cause of tumor death among American males and accounts for 20% of newlydiagnosed cancers, with 31,620 deaths reported in 2019 [1]
Radical prostatectomy (RP) is considered as an effective method of therapy for PCa patients, recent studies have revealed that ∼20–40% of patients suffer from biochemical recurrence (BCR) after radical prostatectomy (RP) [2]
BCR is characterized by a recurrent prostate specific antigen (PSA) concentration of more than 0.2 μg/L and is an indicator for distant metastasis or PCa-specific mortality [2, 3]. 32–45% of BCR patients with post-RP are predicted to die from PCa within 15 years [4]
Summary
Prostate cancer (PCa) is the second leading cause of tumor death among American males and accounts for 20% of newlydiagnosed cancers, with 31,620 deaths reported in 2019 [1]. Patients with similar clinicopathological features may reach infinitely different clinical endpoints [9]. It has been well-acknowledged that biochemical processes from DNA to protein are influenced by many complicated biological factors, proteins can be used to directly determine the functions of genes. To date an increasing number of nuanced BCR risk stratification systems have been developed as gene biomarkers [12], while little emphasis has been placed on the potential function of protein-based signatures for the prediction of BCR in PCa. Biochemical recurrence (BCR) is an indicator of prostate cancer (PCa)-specific recurrence and mortality.
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