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Predicting suicidal behavior by an accurate monitoring of RNA editing biomarkers in blood samples

Published online by Cambridge University Press:  23 March 2020

B. Vire
Affiliation:
Alcediag/Sys2Diag, R&D, Montpellier Cedex 4, France
S. Van der Laan
Affiliation:
Alcediag/Sys2Diag, R&D, Montpellier Cedex 4, France
N. Salvetat
Affiliation:
Alcediag/Sys2Diag, R&D, Montpellier Cedex 4, France
S. Pointet
Affiliation:
Alcediag/Sys2Diag, R&D, Montpellier Cedex 4, France
Y. Lannay
Affiliation:
Alcediag/Sys2Diag, R&D, Montpellier Cedex 4, France
G. Marcellin
Affiliation:
Alcediag/Sys2Diag, R&D, Montpellier Cedex 4, France
F. Molina
Affiliation:
Alcediag/Sys2Diag, R&D, Montpellier Cedex 4, France

Abstract

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Introduction

Predicting suicidal behaviors is one of the most complex challenges of daily psychiatric practices. Alterations of RNA editing of neurotransmitter receptors and other proteins have been shown to be involved in etiology of different psychiatric disorders and linked to suicidal behavior. Additionally, an increase in expression levels of ADARs, the RNA editing enzymes, has also been observed.

Objective

The objective of the present study was to test whether modifications in RNA editing profile of prime targets allow identifying disease-relevant blood biomarkers and evaluating suicide risk in patients.

Methods

A clinical study was performed to identify an RNA editing signature in blood of depressed patients with and without history of suicide attempts. Patient's samples were drawn in PAXgene tubes and analyzed on Alcediag's proprietary RNA editing platform using NGS. In addition, gene expression analysis by quantitative PCR was performed.

Results

We generated a predictive algorithm comprising various selected biomarkers to detect patients with a high risk to attempt suicide. We evaluated the diagnostic performance using the relative proportion of the phosphodiesterase 8A (PDE8A) mRNA editing at different sites as well as the expression of PDE8A and the ADARs. The significance of these biomarkers for suicidality was evaluated using the receiver–operating characteristic (ROC) curve. The generated algorithm comprising the biomarkers was found to have strong diagnostic performances with high specificity and sensitivity.

Conclusions

We developed tools to measure disease–specific biomarkers in blood samples of patients for identifying individuals at the greatest risk for future suicide attempts.

Disclosure of interest

The authors have not supplied their declaration of competing interest.

Type
e-Poster viewing: Suicidology and suicide prevention
Copyright
Copyright © European Psychiatric Association 2017
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