Development and internal validation of a novel model and markers to identify the candidates for lymph node metastasis in patients with prostate cancer


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Abstract

BackgroundHigh-grade prostate cancer (PCa) has a poor prognosis, and up to 15% of patients worldwide experience lymph node invasion (LNI). To further improve the prediction lymph node invasion in prostate cancer, we adopted risk scores of the genes expression based on the nomogram in guidelines.MethodsWe analyzed 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 signaling pathways. Univariate and multivariate logistic regression analyses were performed to identify the independent risk factors for the presence of LNI.ResultsWe found that patients with actual LNI and predicted LNI had the worst survival outcomes. 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. Multivariate analyses identified that the risk score (odds ratio [OR] = 1.05 for 1% increase, 95% confidence interval [CI]: 1.04–1.07, P < .001), serum PSA level, clinical stage, primary biopsy Gleason grade (OR = 2.52 for a grade increase, 95% CI: 1.27–5.22, P = .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 area under the curve (AUC) value of 90.2%.ConclusionsIn 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.

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