Non-Coding RNAs and the Development of Chemoresistance to Docetaxel in Prostate Cancer: Regulatory Interactions and Approaches Based on Machine Learning Methods
Elena Pudova,
Anastasiya Kobelyatskaya,
Marina Emelyanova,
Anastasiya Snezhkina,
Maria Fedorova,
Vladislav Pavlov,
Zulfiya Guvatova,
Alexandra Dalina,
Anna Kudryavtseva
Affiliations
Elena Pudova
Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, 119991 Moscow, Russia
Anastasiya Kobelyatskaya
Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, 119991 Moscow, Russia
Marina Emelyanova
Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, 119991 Moscow, Russia
Anastasiya Snezhkina
Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, 119991 Moscow, Russia
Maria Fedorova
Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, 119991 Moscow, Russia
Vladislav Pavlov
Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, 119991 Moscow, Russia
Zulfiya Guvatova
Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, 119991 Moscow, Russia
Alexandra Dalina
Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, 119991 Moscow, Russia
Anna Kudryavtseva
Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, 119991 Moscow, Russia
Chemotherapy based on taxane-class drugs is the gold standard for treating advanced stages of various oncological diseases. However, despite the favorable response trends, most patients eventually develop resistance to this therapy. Drug resistance is the result of a combination of different events in the tumor cells under the influence of the drug, a comprehensive understanding of which has yet to be determined. In this review, we examine the role of the major classes of non-coding RNAs in the development of chemoresistance in the case of prostate cancer, one of the most common and socially significant types of cancer in men worldwide. We will focus on recent findings from experimental studies regarding the prognostic potential of the identified non-coding RNAs. Additionally, we will explore novel approaches based on machine learning to study these regulatory molecules, including their role in the development of drug resistance.