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.
【저자키워드】 COVID-19, SARS-CoV-2, Biomarker, mendelian randomization, microRNA, 【초록키워드】 pandemic, Biomarkers, severe COVID-19, Prognosis, eQTL, miRNA, COVID-19 severity, risk, Risk assessment, outcomes, miRNAs, early treatment, death, severe cases, phenotype, assessment, group, Causality, diagnose, COVID-19 cases, causal relationship, risk assessments, approaches, COVID-19 case, help, measure, expression data, circulating, circulating miRNAs, Multiple studies, miRNA expression, Multiple, GWAS summary statistics, Effect, independent, IMPROVE, shown, identify, caused, investigated, evaluated, provide, unique, imperative, canonical, circulating miRNA, miRNA biomarker, retrieved, with COVID-19, 【제목키워드】 identifying, Causal,