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Retrotransposons and nucleic acid-editing enzymes: activation, somatic events and association with cancer

Abstract

Retrotransposable or Mobile Elements (MEs) comprise a constant threat to host genome due to their spontaneous mutational capabilities in DNA. To ensure such a level of propagation, a family of nucleic acid-modifying enzymes acts to mobilize the deleterious effects of the MEs. However, what could be regarded as a mechanism of protection to the integrity of the genome may be a source of chronic injury when this activity becomes promiscuous by inducing mutations in DNA. The relationship between the MEs's activity and collateral damage caused by the enzymes is poorly understood or unknown. This project aims to detect and to provide hints to elicit the DNA mutators, molecular mechanism, and therefore, additional therapeutic targets. Here, we propose to apply an integrative analysis to interrogate the transcriptome of 75 cell lines, whole genomes and exomes of 207 samples (normal vs tumor) of the patients diagnosed with lymphoma. Finally, we intend to improve our knowledge of the molecular damage, shedding new light on the nature of spontaneous somatic mutagenesis during the formation and progression in B cell malignancy. (AU)

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VEICULO: TITULO (DATA)

Scientific publications (5)
(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)
VENEZIAN POVOA, LUCAS; RIBEIRO, CARLOS HENRIQUE COSTA; DA SILVA, ISRAEL TOJAL. Machine learning predicts treatment sensitivity in multiple myeloma based on molecular and clinical information coupled with drug response. PLoS One, v. 16, n. 7 JUL 28 2021. Web of Science Citations: 0.
BUTTURA, JAQUELINE RAMALHO; PROVISOR SANTOS, MONIZE NAKAMOTO; VALIERIS, RENAN; DRUMMOND, RODRIGO DUARTE; DEFELICIBUS, ALEXANDRE; LIMA, JOAO PAULO; CALSAVARA, VINICIUS FERNANDO; FREITAS, HELANO CARIOCA; CORDEIRO DE LIMA, VLADMIR C.; BARTELLI, THAIS FERNANDA; WIEDNER, MARC; ROSALES, RAFAEL; GOLLOB, KENNETH JOHN; LOIZOU, JOANNA; DIAS-NETO, EMMANUEL; NUNES, DIANA NORONHA; DA SILVA, ISRAEL TOJAL. Mutational Signatures Driven by Epigenetic Determinants Enable the Stratification of Patients with Gastric Cancer for Therapeutic Intervention. CANCERS, v. 13, n. 3 FEB 2021. Web of Science Citations: 0.
POVOA, LUCAS VENEZIAN; BALAN CALVI, URIEL CAIRE; LORENA, ANA CAROLINA; COSTA RIBEIRO, CARLOS HENRIQUE; DA SILVA, ISRAEL TOJAL. A Multi-Learning Training Approach for Distinguishing Low and High Risk Cancer Patients. IEEE ACCESS, v. 9, p. 115453-115465, 2021. Web of Science Citations: 0.
VALIERIS, RENAN; AMARO, LUCAS; BUENO DE TOLEDO OSORIO, CYNTHIA APARECIDA; BUENO, ADRIANA PASSOS; ROSALES MITROWSKY, RAFAEL ANDRES; CARRARO, DIRCE MARIA; NUNES, DIANA NORONHA; DIAS-NETO, EMMANUEL; DA SILVA, ISRAEL TOJAL. Deep Learning Predicts Underlying Features on Pathology Images with Therapeutic Relevance for Breast and Gastric Cancer. CANCERS, v. 12, n. 12 DEC 2020. Web of Science Citations: 0.
ROSALES, RAFAEL A.; DRUMMOND, RODRIGO D.; VALIERIS, RENAN; DIAS-NETO, EMMANUEL; DA SILVA, ISRAEL T. signeR: an empirical Bayesian approach to mutational signature discovery. Bioinformatics, v. 33, n. 1, p. 8-16, JAN 1 2017. Web of Science Citations: 19.

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