Evaluation of altered miRNA expression pattern to predict COVID-19 severity

Area of Science:

• Molecular Biology
• Genomics
• Infectious Diseases

Background:

• The COVID-19 pandemic highlighted the need for reliable prognostic biomarkers.
• Existing biomarkers for COVID-19 lack specificity for predicting patient outcomes.
• MicroRNAs (miRNAs) are emerging as potential diagnostic and prognostic indicators in various diseases.

Purpose of the Study:

• To identify specific microRNA (miRNA) biomarkers for predicting the severity of COVID-19 infection.
• To develop a tool for formulating targeted treatment strategies based on miRNA profiles.
• To analyze differential miRNA expression in patients with varying COVID-19 severity compared to healthy controls.

Main Methods:

• Small RNA sequencing was performed on peripheral blood samples from COVID-19 patients (Dead, Severe, Moderate) and healthy controls.
• Differential expression analysis identified significantly up-regulated and down-regulated miRNAs.
• Functional enrichment, network analysis, and ROC curve analysis were employed to identify key miRNAs and their diagnostic potential (AUC > 0.8).

Main Results:

• 31 differentially expressed miRNAs were significantly associated with COVID-19.
• Specific miRNAs (hsa-miR-147b-5p, hsa-miR-107, hsa-miR-1299) were uniquely altered in deceased patients.
• Distinct miRNA panels (hsa-miR-224-5p, hsa-miR-4659b-3p, hsa-miR-495-3p, hsa-miR-335-3p) were identified in severe cases, and miRNA families (hsa-miR-20, hsa-miR-32, hsa-miR-548) were down-regulated across patient groups.

Conclusions:

• A distinct miRNA expression profile differentiates COVID-19 severity, including mortality.
• The identified panel of miRNAs shows potential as diagnostic biomarkers for predicting COVID-19 severity.
• These miRNA signatures could aid in stratifying patients and guiding therapeutic interventions.