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

• Published in: International journal of molecular sciences Vol. 24; no. 3; p. 2418
• Main Authors: Pudova, Elena A, Kobelyatskaya, Anastasiya A, Katunina, Irina V, Snezhkina, Anastasiya V, Fedorova, Maria S, Pavlov, Vladislav S, Bakhtogarimov, Ildar R, Lantsova, Margarita S, Kokin, Sergey P, Nyushko, Kirill M, Alekseev, Boris Ya, Kalinin, Dmitry V, Melnikova, Nataliya V, Dmitriev, Alexey A, Krasnov, George S, Kudryavtseva, Anna V
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 26-01-2023; MDPI
• Subjects: Algorithms; Biomarkers, Tumor - genetics; Cancer surgery; Cell cycle; Gene expression; genes; Humans; Independent sample; lymphatic dissemination; Lymphatic system; Male; markers; Medical prognosis; Metastasis; MicroRNAs - genetics; MicroRNAs - metabolism; Patients; Prognosis; Prostate cancer; Prostatectomy; Prostatic Neoplasms - metabolism; RNA-Seq; Statistical analysis; Tactics; Transcriptome; Transcriptomics; Tumors; Urological surgery; Validation studies; microRNAs; models; prostate cancer; RNA-Seq; genes; lymphatic dissemination; machine learning; markers; prognosis
• ISSN: 1422-0067; 1661-6596; 1422-0067
• DOI: 10.3390/ijms24032418

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
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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.