Circulating Levels of Brain-Enriched MicroRNAs Correlate with Neuron Specific Enolase after Cardiac Arrest-A Substudy of the Target Temperature Management Trial

Could MicroRNA be Neurological Prognosis Biomarkers after Cardiac Arrest?

For patients monitored in intensive care units in the aftermath of a cardiac arrest, one of the well-established difficulties of care after resuscitation is the ability to perform the necessary prognostic assessments as accurately and early as possible. Although current guidelines include algorithms to determine prognosis, there are still missing links and uncertainties. Biomarkers obtained from peripheral blood are generally non-invasive and easy to obtain. Although the potential to use microRNA as a prognostic biomarker after cardiac arrest has received less interest recently, its popularity has increased in the last few years. By identifying prognostic biomarkers within 24 h of cardiac arrest, clinicians in intensive care could gain valuable insights to guide patient outcomes and predict both mortality and survival rates.

Identification of predictors for neurological outcome after cardiac arrest in peripheral blood mononuclear cells through integrated bioinformatics analysis and machine learning

Neurological prognostication after cardiac arrest (CA) is important to avoid pursuing futile treatments for poor outcome and inappropriate withdrawal of life-sustaining treatment for good outcome. To predict neurological outcome after CA through biomarkers in peripheral blood mononuclear cells, four datasets were downloaded from the Gene Expression Omnibus database. GSE29546 and GSE74198 were used as training datasets, while GSE92696 and GSE34643 were used as verification datasets. The intersection of differentially expressed genes and hub genes from multiscale embedded gene co-expression network analysis (MEGENA) was utilized in the machine learning screening. Key genes were identified using support vector machine recursive feature elimination (SVM-RFE), least absolute shrinkage and selection operator (LASSO) logistic regression, and random forests (RF). The results were validated using receiver operating characteristic curve analysis. An mRNA-miRNA network was constructed. The distribution of immune cells was evaluated using cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT). Five biomarkers were identified as predictors for neurological outcome after CA, with an area under the curve (AUC) greater than 0.7: CASP8 and FADD-like apoptosis regulator (CFLAR), human protein kinase X (PRKX), miR-483-5p, let-7a-5p, and let-7c-5p. Interestingly, the combination of CFLAR minus PRKX showed an even higher AUC of 0.814. The mRNA-miRNA network consisted of 30 nodes and 76 edges. Statistical differences were found in immune cell distribution, including neutrophils, NK cells active, NK cells resting, T cells CD4 memory activated, T cells CD4 memory resting, T cells CD8, B cells memory, and mast cells resting between individuals with good and poor neurological outcome after CA. In conclusion, our study identified novel predictors for neurological outcome after CA. Further clinical and laboratory studies are needed to validate our findings.

MicroRNA-9-3p: a novel predictor of neurological outcome after cardiac arrest

AIMS

Resuscitated out-of-hospital cardiac arrest (OHCA) patients who remain comatose after hospital arrival are at high risk of mortality due to anoxic brain injury. MicroRNA are small-non-coding RNA molecules ultimately involved in gene-silencing. They show promise as biomarkers, as they are stable in body fluids. The microRNA 9-3p (miR-9-3p) is associated with neurological injury in trauma and subarachnoid haemorrhage.

METHODS AND RESULTS

This post hoc analysis considered all 171 comatose OHCA patients from a single centre in the target temperature management (TTM) trial. Patients were randomized to TTM at either 33°C or 36°C for 24 h. MicroRNA-9-3p (miR-9-3p) was measured in plasma sampled at admission and at 28, 48, and 72 h. There were no significant differences in age, gender, and pre-hospital data, including lactate level at admission, between miR-9-3p level quartiles. miR-9-3p levels changed markedly following OHCA with a peak at 48 h. Median miR-9-3p levels between TTM 33°C vs. 36°C were not different at any of the four time points. Elevated miR-9-3p levels at 48 h were strongly associated with an unfavourable neurological outcome [OR: 2.21, 95% confidence interval (CI): 1.64-3.15, P < 0.0001). MiR-9-3p was inferior to neuron-specific enolase in predicting functional neurological outcome [area under the curve: 0.79 (95% CI: 0.71-0.87) vs. 0.91 (95% CI: 0.85-0.97)].

CONCLUSION

MiR-9-3p is strongly associated with neurological outcome following OHCA, and the levels of miR-9-3p are peaking 48 hours following cardiac arrest.

The Levels of Circulating MicroRNAs at 6-Hour Cardiac Arrest Can Predict 6-Month Poor Neurological Outcome

Early prognostication in cardiac arrest survivors is challenging for physicians. Unlike other prognostic modalities, biomarkers are easily accessible and provide an objective assessment method. We hypothesized that in cardiac arrest patients with targeted temperature management (TTM), early circulating microRNA (miRNA) levels are associated with the 6-month neurological outcome. In the discovery phase, we identified candidate miRNAs associated with cardiac arrest patients who underwent TTM by comparing circulating expression levels in patients and healthy controls. Next, using a larger cohort, we validated the prognostic values of the identified early miRNAs by measuring the serum levels of miRNAs, neuron-specific enolase (NSE), and S100 calcium-binding protein B (S100B) 6 h after cardiac arrest. The validation cohort consisted of 54 patients with TTM. The areas under the curve (AUCs) for poor outcome were 0.85 (95% CI (confidence interval), 0.72–0.93), 0.82 (95% CI, 0.70–0.91), 0.78 (95% CI, 0.64–0.88), and 0.77 (95% CI, 0.63–0.87) for miR-6511b-5p, -125b-1-3p, -122-5p, and -124-3p, respectively. When the cut-off was based on miRNA levels predicting poor outcome with 100% specificity, sensitivities were 67.7% (95% CI, 49.5–82.6), 50.0% (95% CI, 32.4–67.7), 35.3% (95% CI, 19.7–53.5), and 26.5% (95% CI, 12.9–44.4) for the above miRNAs, respectively. The models combining early miRNAs with protein biomarkers demonstrated superior prognostic performance to those of protein biomarkers.