Development and Internal Validation of a Novel Model and Markers to Identify the Candidates for Lymph Node Metastasis in Patients with Prostate Cancer

Abstract

Background: The predictive nomogram has recently been recognized as a significant tool to predict LNI in patients with prostate cancer. However, it remains unclear whether incorporation of the genes expression in the nomogram of European Urology (2018) can further improve the LNI prediction. Methods: We analysed clinical data from 320 PCa patients from the Cancer Genome Atlas database. Weighted gene coexpression network analysis was used to identify the genes that were significantly associated with LNI in PCa(n=390). Analyses using the Gene Ontology and Kyoto Encyclopedia of Genes and Genomes databases were performed to identify the activated signalling pathways. Univariate and multivariate logistic regression analyses were performed to identify the independent risk factors for the presence of LNI. Regression coefficients were used to develop a nomogram to predict the probability of LNI. Findings: We found that patients with actual LNI and predicted LNI had the worst survival outcomes. A total of 2294 genes and 22 modules were associated with LNI in PCa. The 5 most significant modules and the 7 most significant genes (CTNNAL1, ENSA, MAP6D1, MBD4, PRCC, SF3B2, TREML1) were selected for further analysis. Pathways in the cell cycle, DNA replication, oocyte meiosis and 9 other pathways were dramatically activated during LNI in PCa. We used the 7 genes to generate a signature, and we found that PCa patients in the high-gene expression group had significantly shorter disease-free survival and overall survival than those in the low-gene expression group (all p<0.05). Multivariate analyses identified that the risk score (OR=1.05 for 1% increase, 95% CI: 1.04-1.07, p<0.001), serum PSA level, clinical stage, primary biopsy Gleason grade (OR=2.52 for a grade increase, 95% CI: 1.27-5.22, p=0.096) and secondary biopsy Gleason grade were independent predictors of LNI. A nomogram built using these predictive variables showed good calibration and a net clinical benefit, with an AUC value of 90.2%. Interpretation: Incorporation of genes risk scoring system can improve prediction of LNI help clinical decision-making. In clinical practice, the application of our nomogram might contribute significantly to the selection of patients who are good candidates for surgery with extended pelvic lymph node dissection. Funding Statement: This work was supported by the Natural Science Fund of Bengbu Medical College (BYKY17115). Declaration of Interests: The authors declare no competing interests.