The detection of left main coronary artery disease (LMCAD) is crucial before ST-segment elevated myocardial infarction (STEMI) or sudden cardiac death. The aim of this study was to identify characteristic metabolite modifications in the LMCAD phenotype, using the metabolomics technique. Metabolic profiles were generated based on ultra-performance liquid chromatography and mass spectrometry, combined with multivariate statistical analysis. Plasma samples were collected prospectively from a propensity-score matched cohort including 44 STEMI patients (22 consecutive LMCAD and 22 non-LMCAD), and 22 healthy controls. A comprehensive metabolomics data analysis was performed with Metaboanalyst 3.0 version. The retinol metabolism pathway was shown to have the strongest discriminative power for the LMCAD phenotype. According to biomarker analysis through receiver-operating characteristic curves, 9-cis-retinoic acid (9cRA) dominated the first page of biomarkers, with area under the curve (AUC) value 0.888. Next highest were a biomarker panel consisting of 9cRA, dehydrophytosphingosine, 1H-Indole-3-carboxaldehyde, and another seven variants of lysophosphatidylcholines, exhibiting the highest AUC (0.933). These novel data propose that the retinol metabolism pathway was the strongest differential pathway for the LMCAD phenotype. 9cRA was the most critical biomarker of LMCAD, and a ten-metabolite plasma biomarker panel, in which 9cRA remained the weightiest, may help develop a potent predictive model for LMCAD in clinic.
The ability of blood transcriptome analysis to identify dysregulated pathways and outcome-related genes following myocardial infarction remains unknown. Two gene expression datasets (GSE60993 and GSE61144) were downloaded from Gene Expression Omnibus (GEO) Datasets to identify altered plasma transcriptomes in patients with ST-segment elevated myocardial infarction (STEMI) undergoing primary percutaneous coronary intervention. GEO2R, Gene Ontology/Kyoto Encyclopedia of Genes and Genomes annotations, protein–protein interaction analysis, etc., were adopted to determine functional roles and regulatory networks of differentially expressed genes (DEGs). Dysregulated expressomes were verified at transcriptional and translational levels by analyzing the GSE49925 dataset and our own samples, respectively. A total of 91 DEGs were identified in the discovery phase, consisting of 15 downregulated genes and 76 upregulated genes. Two hub modules consisting of 12 hub genes were identified. In the verification phase, six of the 12 hub genes exhibited the same variation patterns at the transcriptional level in the GSE49925 dataset. Among them, S100A12 was shown to have the best discriminative performance for predicting in-hospital mortality and to be the only independent predictor of death during follow-up. Validation of 223 samples from our center showed that S100A12 protein level in plasma was significantly lower among patients who survived to discharge, but it was not an independent predictor of survival to discharge or recurrent major adverse cardiovascular events after discharge. In conclusion, the dysregulated expression of plasma S100A12 at the transcriptional level is a robust early prognostic factor in patients with STEMI, while the discrimination power of the protein level in plasma needs to be further verified by large-scale, prospective, international, multicenter studies.
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