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

Author:

Pudova Elena A.1,Kobelyatskaya Anastasiya A.1,Katunina Irina V.1,Snezhkina Anastasiya V.1ORCID,Fedorova Maria S.1ORCID,Pavlov Vladislav S.1ORCID,Bakhtogarimov Ildar R.1,Lantsova Margarita S.1,Kokin Sergey P.2ORCID,Nyushko Kirill M.2,Alekseev Boris Ya.2,Kalinin Dmitry V.3ORCID,Melnikova Nataliya V.1ORCID,Dmitriev Alexey A.1ORCID,Krasnov George S.1ORCID,Kudryavtseva Anna V.1ORCID

Affiliation:

1. Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, 119991 Moscow, Russia

2. National Medical Research Radiological Center, Ministry of Health of the Russian Federation, 125284 Moscow, Russia

3. Vishnevsky Institute of Surgery, Ministry of Health of the Russian Federation, 117997 Moscow, Russia

Abstract

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.

Funder

Russian Science Foundation

Publisher

MDPI AG

Subject

Inorganic Chemistry,Organic Chemistry,Physical and Theoretical Chemistry,Computer Science Applications,Spectroscopy,Molecular Biology,General Medicine,Catalysis

Reference38 articles.

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