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Kobelyatskaya AA, Kudryavtsev AA, Kudryavtseva AV, Snezhkina AV, Fedorova MS, Kalinin DV, Pavlov VS, Guvatova ZG, Naberezhnev PA, Nyushko KM, Alekseev BY, Krasnov GS, Bulavkina EV, Pudova EA. ALDH3A2, ODF2, QSOX2, and MicroRNA-503-5p Expression to Forecast Recurrence in TMPRSS2-ERG-Positive Prostate Cancer. Int J Mol Sci 2022; 23:ijms231911695. [PMID: 36232996 PMCID: PMC9569942 DOI: 10.3390/ijms231911695] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Revised: 09/23/2022] [Accepted: 09/29/2022] [Indexed: 11/05/2022] Open
Abstract
Following radical surgery, patients may suffer a relapse. It is important to identify such patients so that therapy tactics can be modified appropriately. Existing stratification schemes do not display the probability of recurrence with enough precision since locally advanced prostate cancer (PCa) is classified as high-risk but is not ranked in greater detail. Between 40 and 50% of PCa cases belong to the TMPRSS2-ERG subtype that is a sufficiently homogeneous group for high-precision prognostic marker search to be possible. This study includes two independent cohorts and is based on high throughput sequencing and qPCR data. As a result, we have been able to suggest a perspective-trained model involving a deep neural network based on both qPCR data for mRNA and miRNA and clinicopathological criteria that can be used for recurrence risk forecasts in patients with TMPRSS2-ERG-positive, locally advanced PCa (the model uses ALDH3A2 + ODF2 + QSOX2 + hsa-miR-503-5p + ISUP + pT, with an AUC = 0.944). In addition to the prognostic model’s use of identified differentially expressed genes and miRNAs, miRNA–target pairs were found that correlate with the prognosis and can be presented as an interactome network.
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Affiliation(s)
- Anastasiya A. Kobelyatskaya
- Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, 119991 Moscow, Russia
- Correspondence:
| | | | - Anna V. Kudryavtseva
- Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, 119991 Moscow, Russia
| | - Anastasiya V. Snezhkina
- Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, 119991 Moscow, Russia
| | - Maria S. Fedorova
- Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, 119991 Moscow, Russia
| | - Dmitry V. Kalinin
- Vishnevsky Institute of Surgery, Ministry of Health of the Russian Federation, 117997 Moscow, Russia
| | - Vladislav S. Pavlov
- Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, 119991 Moscow, Russia
| | - Zulfiya G. Guvatova
- Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, 119991 Moscow, Russia
| | - Pavel A. Naberezhnev
- Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, 119991 Moscow, Russia
| | - Kirill M. Nyushko
- National Medical Research Radiological Center, Ministry of Health of the Russian Federation, 125284 Moscow, Russia
| | - Boris Y. Alekseev
- National Medical Research Radiological Center, Ministry of Health of the Russian Federation, 125284 Moscow, Russia
| | - George S. Krasnov
- Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, 119991 Moscow, Russia
| | - Elizaveta V. Bulavkina
- Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, 119991 Moscow, Russia
| | - Elena A. Pudova
- Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, 119991 Moscow, Russia
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