Dataset Information

Circulating microRNAs in patients with intracranial aneurysms.

ABSTRACT:

Introduction

We compared circulating microRNA (miRNA) levels in plasma of patients with intracranial aneurysms (IA) to those of controls as a first step towards finding potential biomarkers for individuals at high risk of IA development and its subsequent rupture.

Patients and methods

Using a PCR array we measured 370 miRNAs in plasma of 15 patients with prior aneurysmal subarachnoid hemorrhage (aSAH), of whom 11 had an additional unruptured IA (UIA), and of 15 controls. MiRNAs with a difference in levels with an absolute fold change (FC) > 1.2 and p<0.01 were further tested using real-time (RT) PCR in an additional independent set of 15 aSAH patients, 15 untreated UIA patients and 15 controls for replication (absolute FC >1.2 and p<0.05). We used receiver operating characteristic (ROC) curves to illustrate the diagnostic potential of these miRNAs.

Results

Three of five miRNAs with a difference in levels in the PCR array study were replicated with miRNA-183-5p decreased in all patients (FC = -2.2, p = 1.7x10-3), miRNA-200a-3p increased in aSAH patients (FC = 1.8, p = 2.8x10-2) and miRNA-let7b-5p decreased in UIA patients (FC = -1.7, p = 1.27x10-3) as compared to controls. In distinguishing aSAH patients from controls, the area under the ROC curve (AUC) was 0.80 (95% confidence interval (95% CI) 0.63-0.97) for miRNA-183-5p, and 0.74 (95% CI 0.55-0.94) for miRNA-200a-3p. In distinguishing untreated UIA patients from controls, AUC was 0.83 (95% CI 0.69-0.98) for miRNA-183-5p and 0.92 (95% CI 0.81-1) for miRNA-let-7b.

Discussion/conclusions

We identified three specific circulating miRNAs that are able to discriminate between IA patients and controls. Follow-up studies should assess if these miRNAs may be used biomarkers for identifying individuals at high risk of IA development and its subsequent rupture.

SUBMITTER: Meeuwsen JAL

PROVIDER: S-EPMC5411042 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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Publications

Circulating microRNAs in patients with intracranial aneurysms.

Meeuwsen John A L JAL van T Hof Femke N G FNG van Rheenen Wouter W Rinkel Gabriel J E GJE Veldink Jan H JH Ruigrok Ynte M YM

PloS one 20170501 5

<h4>Introduction</h4>We compared circulating microRNA (miRNA) levels in plasma of patients with intracranial aneurysms (IA) to those of controls as a first step towards finding potential biomarkers for individuals at high risk of IA development and its subsequent rupture.<h4>Patients and methods</h4>Using a PCR array we measured 370 miRNAs in plasma of 15 patients with prior aneurysmal subarachnoid hemorrhage (aSAH), of whom 11 had an additional unruptured IA (UIA), and of 15 controls. MiRNAs wi ...[more]

PMID: 28459827

Similar Datasets

Project description:BACKGROUND:Intracranial aneurysms (IAs) are dangerous because of their potential to rupture and cause deadly subarachnoid hemorrhages. Previously, we found significant RNA expression differences in circulating neutrophils between patients with unruptured IAs and aneurysm-free controls. Searching for circulating biomarkers for unruptured IAs, we tested the feasibility of developing classification algorithms that use neutrophil RNA expression levels from blood samples to predict the presence of an IA. METHODS:Neutrophil RNA extracted from blood samples from 40 patients (20 with angiography-confirmed unruptured IA, 20 angiography-confirmed IA-free controls) was subjected to next-generation RNA sequencing to obtain neutrophil transcriptomes. In a randomly-selected training cohort of 30 of the 40 samples (15 with IA, 15 controls), we performed differential expression analysis. Significantly differentially expressed transcripts (false discovery rate?<?0.05, fold change???1.5) were used to construct prediction models for IA using four well-known supervised machine-learning approaches (diagonal linear discriminant analysis, cosine nearest neighbors, nearest shrunken centroids, and support vector machines). These models were tested in a testing cohort of the remaining 10 neutrophil samples from the 40 patients (5 with IA, 5 controls), and model performance was assessed by receiver-operating-characteristic (ROC) curves. Real-time quantitative polymerase chain reaction (PCR) was used to corroborate expression differences of a subset of model transcripts in neutrophil samples from a new, separate validation cohort of 10 patients (5 with IA, 5 controls). RESULTS:The training cohort yielded 26 highly significantly differentially expressed neutrophil transcripts. Models using these transcripts identified IA patients in the testing cohort with accuracy ranging from 0.60 to 0.90. The best performing model was the diagonal linear discriminant analysis classifier (area under the ROC curve?=?0.80 and accuracy?=?0.90). Six of seven differentially expressed genes we tested were confirmed by quantitative PCR using isolated neutrophils from the separate validation cohort. CONCLUSIONS:Our findings demonstrate the potential of machine-learning methods to classify IA cases and create predictive models for unruptured IAs using circulating neutrophil transcriptome data. Future studies are needed to replicate these findings in larger cohorts.

Dataset Information

Circulating microRNAs in patients with intracranial aneurysms.

Introduction

Patients and methods

Results

Discussion/conclusions

Publications

Circulating microRNAs in patients with intracranial aneurysms.

Similar Datasets

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