Identification of a Circulating miRNA Signature to Stratify Acute Respiratory Distress Syndrome Patients.
Gennaro MartucciAntonio ArcadipaneFabio TuzzolinoGiovanna OcchipintiGiovanna PanarelloClaudia CarcioneEleonora BonicoliniChiara VitielloRoberto LorussoPier Giulio ConaldiVitale MiceliPublished in: Journal of personalized medicine (2020)
There is a need to improve acute respiratory distress syndrome (ARDS) diagnosis and management, particularly with extracorporeal membrane oxygenation (ECMO), and different biomarkers have been tested to implement a precision-focused approach. We included ARDS patients on veno-venous (V-V) ECMO in a prospective observational pilot study. Blood samples were obtained before cannulation, and screened for the expression of 754 circulating microRNA (miRNAs) using high-throughput qPCR and hierarchical cluster analysis. The miRNet database was used to predict target genes of deregulated miRNAs, and the DIANA tool was used to identify significant enrichment pathways. A hierarchical cluster of 229 miRNAs (identified after quality control screening) produced a clear separation of 11 patients into two groups: considering the baseline SAPS II, SOFA, and RESP score cluster A (n = 6) showed higher severity compared to cluster B (n = 5); p values < 0.05. After analysis of differentially expressed miRNAs between the two clusters, 95 deregulated miRNAs were identified, and reduced to 13 by in silico analysis. These miRNAs target genes implicated in tissue remodeling, immune system, and blood coagulation pathways. The blood levels of 13 miRNAs are altered in severe ARDS. Further investigations will have to match miRNA results with inflammatory biomarkers and clinical data.
Keyphrases
- extracorporeal membrane oxygenation
- acute respiratory distress syndrome
- mechanical ventilation
- end stage renal disease
- respiratory failure
- newly diagnosed
- ejection fraction
- high throughput
- chronic kidney disease
- genome wide
- prognostic factors
- quality control
- gene expression
- poor prognosis
- artificial intelligence
- mass spectrometry
- cross sectional
- long non coding rna
- deep learning