Lymphatic Dissemination in Prostate Cancer: Features of the Transcriptomic Profile and Prognostic Models

Radical prostatectomy is the gold standard treatment for prostate cancer (PCa); however, it does not always completely cure PCa, and patients often experience a recurrence of the disease. In addition, the clinical and pathological parameters used to assess the prognosis and choose further tactics for treating a patient are insufficiently informative and need to be supplemented with new markers. In this study, we performed RNA-Seq of PCa tissue samples, aimed at identifying potential prognostic markers at the level of gene expression and miRNAs associated with one of the key signs of cancer aggressiveness-lymphatic dissemination. The relative expression of candidate markers was validated by quantitative PCR, including an independent sample of patients based on archival material. Statistically significant results, derived from an independent set of samples, were confirmed for miR-148a-3p and miR-615-3p, as well as for the CST2, OCLN, and PCAT4 genes. Considering the obtained validation data, we also analyzed the predictive value of models based on various combinations of identified markers using algorithms based on machine learning. The highest predictive potential was shown for the CST2 + OCLN + pT model (AUC = 0.863) based on the CatBoost Classifier algorithm.

Automated summary

Radical prostatectomy is the gold standard treatment for prostate cancer, but complete cure is not always achieved and recurrence is common. The current methods for assessing prognosis and choosing treatment strategies in PCa patients are not sufficient and new markers are needed. This study used RNA-Seq to identify potential prognostic markers at the gene expression and miRNA levels associated with cancer aggressiveness. The expression of candidate markers was validated and the predictive value of different marker combinations was analyzed using machine learning algorithms, with the CST2 + OCLN + pT model showing the highest potential (AUC = 0.863) based on CatBoost Classifier algorithm.