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

Li, Chang; Wu, Aurora; Song, Kevin; Gao, Jeslyn; Huang, Eric J.; Bai, Yongsheng; Liu, Xiaoming

doi:10.3390/cells10123504

Cited by 11 publications

(5 citation statements)

References 42 publications

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“…The fourth feature analyzed was miR-139-5p. miR-139-5p was upregulated in SARS-CoV-2 infected cells [59] and was associated with COVID-19 severity [60]. MiR-139-5p could reduce the production of proinflammatory cytokines and chemokines by targeting genes, such as MyD88, c-FOS, and Rap1b, and mediating NF-κB and STAT3 signaling pathways [97][98][99][100].…”

Section: Analysis Of the Key Biomarkersmentioning

confidence: 99%

Identifying MicroRNA Markers That Predict COVID-19 Severity Using Machine Learning Methods

Ren

Guo

Kong

et al. 2022

Life

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Individuals with the SARS-CoV-2 infection may experience a wide range of symptoms, from being asymptomatic to having a mild fever and cough to a severe respiratory impairment that results in death. MicroRNA (miRNA), which plays a role in the antiviral effects of SARS-CoV-2 infection, has the potential to be used as a novel marker to distinguish between patients who have various COVID-19 clinical severities. In the current study, the existing blood expression profiles reported in two previous studies were combined for deep analyses. The final profiles contained 1444 miRNAs in 375 patients from six categories, which were as follows: 30 patients with mild COVID-19 symptoms, 81 patients with moderate COVID-19 symptoms, 30 non-COVID-19 patients with mild symptoms, 137 patients with severe COVID-19 symptoms, 31 non-COVID-19 patients with severe symptoms, and 66 healthy controls. An efficient computational framework containing four feature selection methods (LASSO, LightGBM, MCFS, and mRMR) and four classification algorithms (DT, KNN, RF, and SVM) was designed to screen clinical miRNA markers, and a high-precision RF model with a 0.780 weighted F1 was constructed. Some miRNAs, including miR-24-3p, whose differential expression was discovered in patients with acute lung injury complications brought on by severe COVID-19, and miR-148a-3p, differentially expressed against SARS-CoV-2 structural proteins, were identified, thereby suggesting the effectiveness and accuracy of our framework. Meanwhile, we extracted classification rules based on the DT model for the quantitative representation of the role of miRNA expression in differentiating COVID-19 patients with different severities. The search for novel biomarkers that could predict the severity of the disease could aid in the clinical diagnosis of COVID-19 and in exploring the specific mechanisms of the complications caused by SARS-CoV-2 infection. Moreover, new therapeutic targets for the disease may be found.

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Section: Analysis Of the Key Biomarkersmentioning

confidence: 99%

Identifying MicroRNA Markers That Predict COVID-19 Severity Using Machine Learning Methods

Ren

Guo

Kong

et al. 2022

Life

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“…By further verification, three TFs (EGR2, NFKBIA and ZFP36) are highly expressed in AMI and COVID-19, and they could be major drivers for the progression of COVID-19 complicated with AMI by promoting the expression of proinflammatory genes. Circulating profiles of plasma miRNAs are associated with SARS-CoV-2 infection [ 52 ]. As a potential biomarker, miRNA could provide a breakthrough therapeutic strategy for the diagnosis and management of COVID-19.…”

Section: Discussionmentioning

confidence: 99%

Exploring the underlying molecular mechanisms of acute myocardial infarction after SARS-CoV-2 infection

Xie,

Shen,

Yeo

et al. 2024

American Heart Journal Plus: Cardiology Research and Practice

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“…Similarly, reverse causation can be avoided because genetic variants are assigned before the development of the disease (Davey Smith and Hemani, 2014). MR has been widely used to explore the causality between miRNAs and diseases, such as COVID-19 (Li et al, 2021), schizophrenia (Mu et al, 2023), lung cancer , incident acute coronary syndrome (Shen et al, 2021), and type 2 diabetes (Mens et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Systematic genome-wide Mendelian randomization reveals the causal links between miRNAs and Parkinson’s disease

Shi,

Wu,

et al. 2024

Front. Neurosci.

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BackgroundMicroRNAs (miRNAs) have pivotal roles in gene regulation. Circulating miRNAs have been developed as novel candidate non-invasive biomarkers for diagnosis, prognosis, and treatment response for diseases. However, miRNAs that have causal effects on Parkinson’s Disease (PD) remain largely unknown. To investigate the causal relationships between miRNAs and PD, here we conduct a Mendelian randomization (MR) study.MethodsThis study utilized the summary-level data of respective genome-wide association studies (GWAS) for 2083 miRNAs and seven PD-related outcomes to comprehensively reveal the causal associations between the circulating miRNAs and PD. Two-sample MR design was deployed and the causal effects were estimated with inverse variance weighted, MR-Egger, and weighted median. Comprehensively sensitive analyses were followed, including Cochran’s Q test, MR-Egger intercept test, MR-PRESSO, and leave-one-out analysis, to validate the robustness of our results. Finally, we investigated the potential role of the MR significant miRNAs by predicting their target genes and functional enrichment analysis.ResultsInverse variance weighted estimates suggested that two miRNAs, miR-205-5p (β = −0.46, 95%CI: −0.690 to −0.229, p = 9.3 × 10−5) and miR-6800-5p (β = −0.389, 95%CI: −0.575 to −0.202, p = 4.32 × 10−5), significantly decreased the rate of cognitive decline among PD patients. In addition, eight miRNAs were nominally associated with more than three PD-related outcomes each. No significant heterogeneity of instrumental variables or horizontal pleiotropy was found. Gene Ontology (GO) analysis showed that the targets of these causal miRNAs were significantly enriched in cell cycle, apoptotic, and aging pathways.ConclusionThis MR study identified two miRNAs whose genetically regulated expression might have a causal role in the development of PD dementia. Our findings provided potential miRNA biomarkers to make better and early diagnoses and risk assessments of PD.

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

Cited by 11 publications

References 42 publications

Identifying MicroRNA Markers That Predict COVID-19 Severity Using Machine Learning Methods

Identifying MicroRNA Markers That Predict COVID-19 Severity Using Machine Learning Methods

Exploring the underlying molecular mechanisms of acute myocardial infarction after SARS-CoV-2 infection

Systematic genome-wide Mendelian randomization reveals the causal links between miRNAs and Parkinson’s disease

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