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...
Autores principales: | , , , , , , |
---|---|
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 |
_version_ | 1784620738831974400 |
---|---|
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. |
format | Online Article Text |
id | pubmed-8700362 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT lichang identifyingputativecausallinksbetweenmicrornasandseverecovid19usingmendelianrandomization AT wuaurora identifyingputativecausallinksbetweenmicrornasandseverecovid19usingmendelianrandomization AT songkevin identifyingputativecausallinksbetweenmicrornasandseverecovid19usingmendelianrandomization AT gaojeslyn identifyingputativecausallinksbetweenmicrornasandseverecovid19usingmendelianrandomization AT huangeric identifyingputativecausallinksbetweenmicrornasandseverecovid19usingmendelianrandomization AT baiyongsheng identifyingputativecausallinksbetweenmicrornasandseverecovid19usingmendelianrandomization AT liuxiaoming identifyingputativecausallinksbetweenmicrornasandseverecovid19usingmendelianrandomization |