Non-Coding RNAs and the Development of Chemoresistance to Docetaxel in Prostate Cancer: Regulatory Interactions and Approaches Based on Machine Learning Methods.
Elena A PudovaAnastasiya KobelyatskayaMarina EmelyanovaAnastasiya V SnezhkinaMaria S FedorovaVladislav S PavlovZulfiya G GuvatovaAlexandra DalinaAnna V KudryavtsevaPublished in: Life (Basel, Switzerland) (2023)
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.
Keyphrases
- prostate cancer
- machine learning
- radical prostatectomy
- end stage renal disease
- newly diagnosed
- ejection fraction
- transcription factor
- prognostic factors
- chronic kidney disease
- artificial intelligence
- emergency department
- big data
- stem cells
- squamous cell carcinoma
- patient reported outcomes
- rectal cancer
- mesenchymal stem cells
- cancer stem cells
- adverse drug
- chemotherapy induced
- electronic health record