Long non-coding RNAs to predict outcome after cardiac arrest

SCHEME: CORE

CALL: 2017

DOMAIN: BM - Translational Biomedical Research

FIRST NAME: Yvan

LAST NAME: Devaux

INDUSTRY PARTNERSHIP / PPP: No

INDUSTRY / PPP PARTNER:

HOST INSTITUTION: LIH

KEYWORDS: Translational biomedical research; personalized medicine; biomarkers; prognostic; long non-coding RNAs; cardiovascular disease; cardiac arrest.

START: 2018-01-01

END:

WEBSITE: https://www.lih.lu

Submitted Abstract

Cardiac arrest is a main health issue in Luxembourg and abroad. Despite modern health care strategies including targeted temperature management, half of patients successfully resuscitated from out of hospital cardiac arrest suffer severe and irreversible damage of the brain and die within few days. Among survivors, half are unable to maintain an independent daily life due to major neurological sequelae. Even though the need for a personalized healthcare system becomes more and more recognized, personalized medicine is still far from being widely implemented. In the context of cardiac arrest for instance, it is known that personalized healthcare is possible, but its implementation is limited by a lack of specific prognostication strategies. In the present project, we aim to satisfy this unmet clinical need. Following our investigations of the prognostic value of microRNAs in cardiac arrest patients (CA-miRs FNR-CORE project 2015-2016), we here aim to extend our research activities to the longer arm of the non-coding RNA family, i.e. long non-coding RNAs. Recent experiments using RNA sequencing in a group of 50 cardiac arrest patients led to the identification of a set of 10 candidate long non-coding RNAs associated with survival. We will start the present project with a secondary analysis of RNA sequencing data (172 Gb available), focusing on circular RNAs, a novel type of non-coding RNAs. We recently identified the first circular RNA present in the blood and possessing a biomarker value for cardiac disease. Whether circular RNAs have some potential as biomarkers of cardiac arrest is still unknown. Then, we will proceed with a first validation of the 10 candidate long non-coding RNAs and novel circular RNAs in the large multicenter TTM cohort (>500 samples from cardiac arrest patients available for RNA analyses under an existing collaboration agreement with LIH). Validated long non-coding RNAs and circular RNAs will be characterized at the molecular level. Next, we will use two additional cohorts of patients with cardiac arrest for an extensive and independent validation (2000 patients). Ultimately, this project will led to the identification of a set of predictive non-coding RNAs that will constitute the basis for the development and commercialization of an in vitro diagnostic assay. This assay will help tailoring healthcare to each individual patient and will represent a step forward towards the implementation of personalized medicine.

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