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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...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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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. |
format | Online Article Text |
id | pubmed-9499633 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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