Identifying Putative Causal Links between MicroRNAs and Severe COVID-19 Using Mendelian Randomization

The SARS-CoV-2 (COVID-19) pandemic has caused millions of deaths worldwide. Early risk assessment of COVID-19 cases can help direct early treatment measures that have been shown to improve the prognosis of severe cases. Currently, circulating miRNAs have not been evaluated as canonical COVID-19 biom...

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Autores principales: Li, Chang, Wu, Aurora, Song, Kevin, Gao, Jeslyn, Huang, Eric, Bai, Yongsheng, Liu, Xiaoming
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8700362/
https://www.ncbi.nlm.nih.gov/pubmed/34944012
http://dx.doi.org/10.3390/cells10123504
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author Li, Chang
Wu, Aurora
Song, Kevin
Gao, Jeslyn
Huang, Eric
Bai, Yongsheng
Liu, Xiaoming
author_facet Li, Chang
Wu, Aurora
Song, Kevin
Gao, Jeslyn
Huang, Eric
Bai, Yongsheng
Liu, Xiaoming
author_sort Li, Chang
collection PubMed
description The SARS-CoV-2 (COVID-19) pandemic has caused millions of deaths worldwide. Early risk assessment of COVID-19 cases can help direct early treatment measures that have been shown to improve the prognosis of severe cases. Currently, circulating miRNAs have not been evaluated as canonical COVID-19 biomarkers, and identifying biomarkers that have a causal relationship with COVID-19 is imperative. To bridge these gaps, we aim to examine the causal effects of miRNAs on COVID-19 severity in this study using two-sample Mendelian randomization approaches. Multiple studies with available GWAS summary statistics data were retrieved. Using circulating miRNA expression data as exposure, and severe COVID-19 cases as outcomes, we identified ten unique miRNAs that showed causality across three phenotype groups of COVID-19. Using expression data from an independent study, we validated and identified two high-confidence miRNAs, namely, hsa-miR-30a-3p and hsa-miR-139-5p, which have putative causal effects on developing cases of severe COVID-19. Using existing literature and publicly available databases, the potential causative roles of these miRNAs were investigated. This study provides a novel way of utilizing miRNA eQTL data to help us identify potential miRNA biomarkers to make better and early diagnoses and risk assessments of severe COVID-19 cases.
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spelling pubmed-87003622021-12-24 Identifying Putative Causal Links between MicroRNAs and Severe COVID-19 Using Mendelian Randomization Li, Chang Wu, Aurora Song, Kevin Gao, Jeslyn Huang, Eric Bai, Yongsheng Liu, Xiaoming Cells Article The SARS-CoV-2 (COVID-19) pandemic has caused millions of deaths worldwide. Early risk assessment of COVID-19 cases can help direct early treatment measures that have been shown to improve the prognosis of severe cases. Currently, circulating miRNAs have not been evaluated as canonical COVID-19 biomarkers, and identifying biomarkers that have a causal relationship with COVID-19 is imperative. To bridge these gaps, we aim to examine the causal effects of miRNAs on COVID-19 severity in this study using two-sample Mendelian randomization approaches. Multiple studies with available GWAS summary statistics data were retrieved. Using circulating miRNA expression data as exposure, and severe COVID-19 cases as outcomes, we identified ten unique miRNAs that showed causality across three phenotype groups of COVID-19. Using expression data from an independent study, we validated and identified two high-confidence miRNAs, namely, hsa-miR-30a-3p and hsa-miR-139-5p, which have putative causal effects on developing cases of severe COVID-19. Using existing literature and publicly available databases, the potential causative roles of these miRNAs were investigated. This study provides a novel way of utilizing miRNA eQTL data to help us identify potential miRNA biomarkers to make better and early diagnoses and risk assessments of severe COVID-19 cases. MDPI 2021-12-11 /pmc/articles/PMC8700362/ /pubmed/34944012 http://dx.doi.org/10.3390/cells10123504 Text en © 2021 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 Article
Li, Chang
Wu, Aurora
Song, Kevin
Gao, Jeslyn
Huang, Eric
Bai, Yongsheng
Liu, Xiaoming
Identifying Putative Causal Links between MicroRNAs and Severe COVID-19 Using Mendelian Randomization
title Identifying Putative Causal Links between MicroRNAs and Severe COVID-19 Using Mendelian Randomization
title_full Identifying Putative Causal Links between MicroRNAs and Severe COVID-19 Using Mendelian Randomization
title_fullStr Identifying Putative Causal Links between MicroRNAs and Severe COVID-19 Using Mendelian Randomization
title_full_unstemmed Identifying Putative Causal Links between MicroRNAs and Severe COVID-19 Using Mendelian Randomization
title_short Identifying Putative Causal Links between MicroRNAs and Severe COVID-19 Using Mendelian Randomization
title_sort identifying putative causal links between micrornas and severe covid-19 using mendelian randomization
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8700362/
https://www.ncbi.nlm.nih.gov/pubmed/34944012
http://dx.doi.org/10.3390/cells10123504
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