Circulating MicroRNAs in Delayed Cerebral Infarction After Aneurysmal Subarachnoid Hemorrhage

Abstract

Background: Delayed cerebral infarction (DCI) is a major cause of morbidities after aneurysmal subarachnoid hemorrhage (SAH) and typically starts at day 4 to 7 after initial hemorrhage. MicroRNAs (miRNAs) play an important role in posttranscriptional gene expression control, and distinctive patterns of circulating miRNA changes have been identified for some diseases. We aimed to investigate miRNAs that characterize SAH patients with DCI compared with those without DCI.

Methods and results: Circulating miRNAs were collected on day 7 after SAH in healthy, SAH-free controls (n=20), SAH patients with DCI (n=20), and SAH patients without DCI (n=20). We used the LASSO (least absolute shrinkage and selection operator) method of regression analysis to characterize miRNAs associated with SAH patients with DCI compared with those without DCI. In the 28 dysregulated miRNAs associated with DCI and SAH, we found that a combination of 4 miRNAs (miR-4532, miR-4463, miR-1290, and miR-4793) could differentiate SAH patients with DCI from those without DCI with an area under the curve of 100% (95% CI 1.000-1.000, P<0.001). This 4-miRNA combination could also distinguish SAH patients with or without DCI from healthy controls with areas under the curve of 99.3% (95% CI 0.977-1.000, P<0.001) and 82.0% (95% CI 0.685-0.955, P<0.001), respectively.

Conclusions: We found a 4-miRNA combination that characterized SAH patients with DCI. The findings could guide future mechanistic study to develop therapeutic targets.

Keywords: biomarker; delayed cerebral infarction; miRNA; stroke; subarachnoid hemorrhage.

Figure 1

A, Quantitative polymerase chain reaction array of the 28 subarachnoid hemorrhage (SAH)‐associated microRNAs. The expression levels were normalized to the control and expressed as ΔΔCt. B, Principal component analysis of the 2 SAH subtypes with or without delayed cerebral infarction (DCI).

Figure 2

Four classification models, namely, linear support vector machine (L‐SVM), nonlinear support vector machine (non‐L‐SVM), linear discriminant analysis (LDA), and logistic regression (LR) models, were applied to construct microRNA (miRNA) classifiers. The performance of the miRNA‐based classifiers was determined by leave‐one‐out cross‐validation. The x‐axis denotes the number of miRNAs contained in the classifier, whereas the y‐axis indicates the performance of the classifiers including sensitivity (green circle), specificity (blue square), and accuracy (red triangle). In each group with the same miRNA number, only the classifier displaying the best performance is presented.

Figure 3

A, LASSO (least absolute shrinkage and selection operator) regression analysis, (B) the box‐and‐whisker plot (upper panel), receiver operating characteristic (ROC) curve (bottom left panel), and principal component analysis (bottom right panel) of the 4 microRNAs (miR‐4532, miR‐4463, miR‐1290, and miR‐4793‐3p) in the subarachnoid hemorrhage groups with and without delayed cerebral infarction (DCI) in the classification algorithm. AUC indicates area under the curve.

Figure 4

Box‐and‐whisker plots (left panel) and receiver operating characteristic curves (right panel) of the 4 microRNAs (miR‐4532, miR‐4463, miR‐1290, and miR‐4793‐3p) in (A) subarachnoid hemorrhage (SAH) patients with delayed cerebral infarction (DCI) vs control and (B) SAH patients without DCI (non‐DCI) vs control, using the R package. AUC indicates area under the curve.

Figure 5

A, Gene‐centric analysis of the putative targets of the 4 microRNAs (miRNAs; miR‐4532, miR‐4463, miR‐1290, and miR‐4793‐3p) using TargetScan, miRanda, and PITA. B, Topological network view of the downstream targets shared by the 4 miRNAs, constructed by Cytoscape.