Project Title
Adenosine Deaminase RNA Specific (ADAR) regulates the breast cancer drug response
Partner Organisations
Internal ID
1221436
Principal Investigator
Start Date
2022

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    Item type:Publication,
    ADAR-Mediated A>I(G) RNA Editing in the Genotoxic Drug Response of Breast Cancer
    (2024)
    Yanara A. Bernal
    ;
    Eduardo Durán
    ;
    Isidora Solar
    ;
    Eduardo A. Sagredo
    ;
    ARMISEN YAÑEZ, RICARDO AMADO  
    <jats:p>Epitranscriptomics is a field that delves into post-transcriptional changes. Among these modifications, the conversion of adenosine to inosine, traduced as guanosine (A&gt;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&gt;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&gt;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&gt;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&gt;I(G) RNA-edited sites. We gathered databases related to A&gt;I(G) RNA editing and cancer and discussed the potential clinical and research implications of understanding A&gt;I(G) RNA-editing patterns. Understanding the intricate role of ADAR1-mediated A&gt;I(G) RNA editing in breast cancer holds significant promise for the development of personalized treatment approaches tailored to individual patients’ A&gt;I(G) RNA-editing profiles.</jats:p>
    Scopus© Citations 2  2
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    Advances in machine learning for tumour classification in cancer of unknown primary: A mini-review
    (2025)
    Karen Oróstica  
    ;
    Felipe Mardones
    ;
    Yanara A. Bernal
    ;
    Samuel Molina
    ;
    Marcos Orchard
    Scopus© Citations 3  8