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Noncoding RNome as Enabling Biomarkers for Precision Health.
Cheong, Jit Kong; Rajgor, Dimple; Lv, Yang; Chung, Ka Yan; Tang, Yew Chung; Cheng, He.
Afiliação
  • Cheong JK; Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore (NUS), Singapore 117597, Singapore.
  • Rajgor D; Precision Medicine Translational Research Programme, Yong Loo Lin School of Medicine, National University of Singapore (NUS), Singapore 117597, Singapore.
  • Lv Y; NUS Centre for Cancer Research, Singapore 117599, Singapore.
  • Chung KY; MiRXES Lab, Singapore 138667, Singapore.
  • Tang YC; Precision Medicine Translational Research Programme, Yong Loo Lin School of Medicine, National University of Singapore (NUS), Singapore 117597, Singapore.
  • Cheng H; MiRXES Lab, Singapore 138667, Singapore.
Int J Mol Sci ; 23(18)2022 Sep 08.
Article em En | MEDLINE | ID: mdl-36142304
Noncoding RNAs (ncRNAs), in the form of structural, catalytic or regulatory RNAs, have emerged to be critical effectors of many biological processes. With the advent of new technologies, we have begun to appreciate how intracellular and circulatory ncRNAs elegantly choreograph the regulation of gene expression and protein function(s) in the cell. Armed with this knowledge, the clinical utility of ncRNAs as biomarkers has been recently tested in a wide range of human diseases. In this review, we examine how critical factors govern the success of interrogating ncRNA biomarker expression in liquid biopsies and tissues to enhance our current clinical management of human diseases, particularly in the context of cancer. We also discuss strategies to overcome key challenges that preclude ncRNAs from becoming standard-of-care clinical biomarkers, including sample pre-analytics standardization, data cross-validation with closer attention to discordant findings, as well as correlation with clinical outcomes. Although harnessing multi-modal information from disease-associated noncoding RNome (ncRNome) in biofluids or in tissues using artificial intelligence or machine learning is at the nascent stage, it will undoubtedly fuel the community adoption of precision population health.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: MicroRNAs / RNA Longo não Codificante Tipo de estudo: Prognostic_studies Limite: Humans Idioma: En Ano de publicação: 2022 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: MicroRNAs / RNA Longo não Codificante Tipo de estudo: Prognostic_studies Limite: Humans Idioma: En Ano de publicação: 2022 Tipo de documento: Article