Detecting early-warning biomarkers associated with heart-exosome genetic-signature for acute myocardial infarction: A source-tracking study of exosome.

Abstract

The genetic information of plasma total-exosomes originating from tissues have already proven useful to assess the severity of coronary artery diseases (CAD). However, plasma total-exosomes include multiple sub-populations secreted by various tissues. Only analysing the genetic information of plasma total-exosomes is perturbed by exosomes derived from other organs except the heart. We aim to detect early-warning biomarkers associated with heart-exosome genetic-signatures for acute myocardial infarction (AMI) by a source-tracking analysis of plasma exosome. The source-tracking of AMI plasma total-exosomes was implemented by deconvolution algorithm. The final early-warning biomarkers associated with heart-exosome genetic-signatures for AMI was identified by integration with single-cell sequencing, weighted gene correction network and machine learning analyses. The correlation between biomarkers and clinical indicators was validated in impatient cohort. A nomogram was generated using early-warning biomarkers for predicting the CAD progression. The molecular subtypes landscape of AMI was detected by consensus clustering. A higher fraction of exosomes derived from spleen and blood cells was revealed in plasma exosomes, while a lower fraction of heart-exosomes was detected. The gene ontology revealed that heart-exosomes genetic-signatures was associated with the heart development, cardiac function and cardiac response to stress. We ultimately identified three genes associated with heart-exosomes defining early-warning biomarkers for AMI. The early-warning biomarkers mediated molecular clusters presented heterogeneous metabolism preference in AMI. Our study introduced three early-warning biomarkers associated with heart-exosome genetic-signatures, which reflected the genetic information of heart-exosomes carrying AMI signals and provided new insights for exosomes research in CAD progression and prevention.