ADAR-Mediated A>I(G) RNA Editing in the Genotoxic Drug Response of Breast Cancer.
Yanara A BernalEduardo DuránIsidora SolarEduardo A SagredoRicardo ArmisénPublished in: International journal of molecular sciences (2024)
Epitranscriptomics is a field that delves into post-transcriptional changes. Among these modifications, the conversion of adenosine to inosine, traduced as guanosine (A>I(G)), is one of the known RNA-editing mechanisms, catalyzed by ADARs. This type of RNA editing is the most common type of editing in mammals and contributes to biological diversity. Disruption in the A>I(G) RNA-editing balance has been linked to diseases, including several types of cancer. Drug resistance in patients with cancer represents a significant public health concern, contributing to increased mortality rates resulting from therapy non-responsiveness and disease progression, representing the greatest challenge for researchers in this field. The A>I(G) RNA editing is involved in several mechanisms over the immunotherapy and genotoxic drug response and drug resistance. This review investigates the relationship between ADAR1 and specific A>I(G) RNA-edited sites, focusing particularly on breast cancer, and the impact of these sites on DNA damage repair and the immune response over anti-cancer therapy. We address the underlying mechanisms, bioinformatics, and in vitro strategies for the identification and validation of A>I(G) RNA-edited sites. We gathered databases related to A>I(G) RNA editing and cancer and discussed the potential clinical and research implications of understanding A>I(G) RNA-editing patterns. Understanding the intricate role of ADAR1-mediated A>I(G) RNA editing in breast cancer holds significant promise for the development of personalized treatment approaches tailored to individual patients' A>I(G) RNA-editing profiles.
Keyphrases
- crispr cas
- public health
- immune response
- dna damage
- nucleic acid
- cancer therapy
- type diabetes
- oxidative stress
- newly diagnosed
- stem cells
- squamous cell carcinoma
- drug delivery
- machine learning
- cardiovascular disease
- young adults
- transcription factor
- mesenchymal stem cells
- artificial intelligence
- prognostic factors
- ejection fraction
- smoking cessation
- dna repair
- big data
- squamous cell
- deep learning
- heat shock protein