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Noncoding RNome as Enabling Biomarkers for Precision Health

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...

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Autores principales: Cheong, Jit Kong, Rajgor, Dimple, Lv, Yang, Chung, Ka Yan, Tang, Yew Chung, Cheng, He
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9499633/
https://www.ncbi.nlm.nih.gov/pubmed/36142304
http://dx.doi.org/10.3390/ijms231810390
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author Cheong, Jit Kong
Rajgor, Dimple
Lv, Yang
Chung, Ka Yan
Tang, Yew Chung
Cheng, He
author_facet Cheong, Jit Kong
Rajgor, Dimple
Lv, Yang
Chung, Ka Yan
Tang, Yew Chung
Cheng, He
author_sort Cheong, Jit Kong
collection PubMed
description 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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spelling pubmed-94996332022-09-23 Noncoding RNome as Enabling Biomarkers for Precision Health Cheong, Jit Kong Rajgor, Dimple Lv, Yang Chung, Ka Yan Tang, Yew Chung Cheng, He Int J Mol Sci Review 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. MDPI 2022-09-08 /pmc/articles/PMC9499633/ /pubmed/36142304 http://dx.doi.org/10.3390/ijms231810390 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Review
Cheong, Jit Kong
Rajgor, Dimple
Lv, Yang
Chung, Ka Yan
Tang, Yew Chung
Cheng, He
Noncoding RNome as Enabling Biomarkers for Precision Health
title Noncoding RNome as Enabling Biomarkers for Precision Health
title_full Noncoding RNome as Enabling Biomarkers for Precision Health
title_fullStr Noncoding RNome as Enabling Biomarkers for Precision Health
title_full_unstemmed Noncoding RNome as Enabling Biomarkers for Precision Health
title_short Noncoding RNome as Enabling Biomarkers for Precision Health
title_sort noncoding rnome as enabling biomarkers for precision health
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9499633/
https://www.ncbi.nlm.nih.gov/pubmed/36142304
http://dx.doi.org/10.3390/ijms231810390
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