Collapsing the list of myocardial infarction-related differentially expressed genes into a diagnostic signature.
Machine learning
Myocardial infarction
Transcriptional signatures
Transcriptomics
miRNA
Journal
Journal of translational medicine
ISSN: 1479-5876
Titre abrégé: J Transl Med
Pays: England
ID NLM: 101190741
Informations de publication
Date de publication:
09 06 2020
09 06 2020
Historique:
received:
26
02
2020
accepted:
03
06
2020
entrez:
11
6
2020
pubmed:
11
6
2020
medline:
15
5
2021
Statut:
epublish
Résumé
Myocardial infarction (MI) is one of the most severe manifestations of coronary artery disease (CAD) and the leading cause of death from non-infectious diseases worldwide. It is known that the central component of CAD pathogenesis is a chronic vascular inflammation. However, the mechanisms underlying the changes that occur in T, B and NK lymphocytes, monocytes and other immune cells during CAD and MI are still poorly understood. One of those pathogenic mechanisms might be the dysregulation of intracellular signaling pathways in the immune cells. In the present study we performed a transcriptome profiling in peripheral blood mononuclear cells of MI patients and controls. The machine learning algorithm was then used to search for MI-associated signatures, that could reflect the dysregulation of intracellular signaling pathways. The genes ADAP2, KLRC1, MIR21, PDGFD and CD14 were identified as the most important signatures for the classification model with L1-norm penalty function. The classifier output quality was equal to 0.911 by Receiver Operating Characteristic metric on test data. These results were validated on two independent open GEO datasets. Identified MI-associated signatures can be further assisted in MI diagnosis and/or prognosis. Thus, our study presents a pipeline for collapsing the list of differential expressed genes, identified by high-throughput techniques, in order to define disease-associated diagnostic signatures.
Sections du résumé
BACKGROUND
Myocardial infarction (MI) is one of the most severe manifestations of coronary artery disease (CAD) and the leading cause of death from non-infectious diseases worldwide. It is known that the central component of CAD pathogenesis is a chronic vascular inflammation. However, the mechanisms underlying the changes that occur in T, B and NK lymphocytes, monocytes and other immune cells during CAD and MI are still poorly understood. One of those pathogenic mechanisms might be the dysregulation of intracellular signaling pathways in the immune cells.
METHODS
In the present study we performed a transcriptome profiling in peripheral blood mononuclear cells of MI patients and controls. The machine learning algorithm was then used to search for MI-associated signatures, that could reflect the dysregulation of intracellular signaling pathways.
RESULTS
The genes ADAP2, KLRC1, MIR21, PDGFD and CD14 were identified as the most important signatures for the classification model with L1-norm penalty function. The classifier output quality was equal to 0.911 by Receiver Operating Characteristic metric on test data. These results were validated on two independent open GEO datasets. Identified MI-associated signatures can be further assisted in MI diagnosis and/or prognosis.
CONCLUSIONS
Thus, our study presents a pipeline for collapsing the list of differential expressed genes, identified by high-throughput techniques, in order to define disease-associated diagnostic signatures.
Identifiants
pubmed: 32517814
doi: 10.1186/s12967-020-02400-1
pii: 10.1186/s12967-020-02400-1
pmc: PMC7285786
doi:
Substances chimiques
MIRN21 microRNA, human
0
MicroRNAs
0
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
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