Establishment of a Novel Combined Nomogram for Predicting the Risk of Progression Related to Castration Resistance in Patients With Prostate Cancer

Abstract

Background: The emergence of castration resistance is fatal for patients with prostate cancer (PCa); however, there is still a lack of effective means to detect the early progression. In this study, a novel combined nomogram was established to predict the risk of progression related to castration resistance.Methods: The castration-resistant prostate cancer (CRPC)-related differentially expressed genes (DEGs) were identified by R packages “limma” and “WGCNA” in GSE35988-GPL6480 and GSE70768-GPL10558, respectively. Relationships between DEGs and progression-free interval (PFI) were analyzed using the Kaplan–Meier method in TCGA PCa patients. A multigene signature was built by lasso-penalized Cox regression analysis, and assessed by the receiver operator characteristic (ROC) curve and Kaplan–Meier curve. Finally, the univariate and multivariate Cox regression analyses were used to establish a combined nomogram. The prognostic value of the nomogram was validated by concordance index (C-index), calibration plots, ROC curve, and decision curve analysis (DCA).Results: 15 CRPC-related DEGs were identified finally, of which 13 genes were significantly associated with PFI and used as the candidate genes for modeling. A two-gene (KIFC2 and BCAS1) signature was built to predict the risk of progression. The ROC curve indicated that 5-year area under curve (AUC) in the training, testing, and whole TCGA dataset was 0.722, 0.739, and 0.731, respectively. Patients with high-risk scores were significantly associated with poorer PFI (p < 0.0001). A novel combined nomogram was successfully established for individualized prediction integrating with T stage, Gleason score, and risk score. While the 1-year, 3-year, and 5-year AUC were 0.76, 0.761, and 0.762, respectively, the good prognostic value of the nomogram was also validated by the C-index (0.734), calibration plots, and DCA.Conclusion: The combined nomogram can be used to predict the individualized risk of progression related to castration resistance for PCa patients and has been preliminarily verified to have good predictive ability.

Comprehensively analysis of splicing factors to construct prognosis prediction classifier in prostate cancer

“…BCAS1 has been found to be highly expressed in breast cancer cell lines 28 . Consistent with our results, Shuqiang Li reported that it can serve as a biomarker to predict castration‐resistant PCa 29 . RBM47 has been shown to inhibit the progression of several malignancies, including thyroid carcinoma, nasopharyngeal carcinoma, lung cancer and colorectal cancer, whereas its role in PCa has not been characterized 30–33 .…”

Identifying patients with rapid progression from hormone-sensitive to castration-resistant prostate cancer: a retrospective study

“…In this study, a novel nomogram was established for predicting the probability of specific progression times to CRPC, by integrating five proteins (ARHGEF1, UTP23, ANAPC4, DPT, and CMAS) and two clinical features (tPSA and GS), based on their expression levels and regression coefficients, as demonstrated by the multivariate Cox regression analysis. As a comprehensive scoring system, of which prediction ability was comfirmed by the overall C-index and the AUC values, our results suggest a better discrimination capability than previously published transcriptomic signatures consisting of two to 22 genes 22-25 .…”

Identification of a 6-gene signature associated with ferroptosis for predicting the prognosis in prostate cancer

“…Besides, we compared our risk model with other signatures. Li et al [18] constructed a 2-gene signature PFI prediction model. We determined the risk scores of all samples and performed the ROC and DCA based on their risk score (Fig.…”