Accuracy of triggering receptor expressed on myeloid cells 1 in diagnosis and prognosis of acute myocardial infarction: a prospective cohort study

Differential gene expression patterns in ST-elevation Myocardial Infarction and Non-ST-elevation Myocardial Infarction

The ST-elevation Myocardial Infarction (STEMI) and Non-ST-elevation Myocardial Infarction (NSTEMI) might occur because of coronary artery stenosis. The gene biomarkers apply to the clinical diagnosis and therapeutic decisions in Myocardial Infarction. The aim of this study was to introduce, enrich and estimate timely the blood gene profiles based on the high-throughput data for the molecular distinction of STEMI and NSTEMI. The text mining data (50 genes) annotated with DisGeNET data (144 genes) were merged with the GEO gene expression data (5 datasets) using R software. Then, the STEMI and NSTEMI networks were primarily created using the STRING server, and improved using the Cytoscape software. The high-score genes were enriched using the KEGG signaling pathways and Gene Ontology (GO). Furthermore, the genes were categorized to determine the NSTEMI and STEMI gene profiles. The time cut-off points were identified statistically by monitoring the gene profiles up to 30 days after Myocardial Infarction (MI). The gene heatmaps were clearly created for the STEMI (high-fold genes 69, low-fold genes 45) and NSTEMI (high-fold genes 68, low-fold genes 36). The STEMI and NSTEMI networks suggested the high-score gene profiles. Furthermore, the gene enrichment suggested the different biological conditions for STEMI and NSTEMI. The time cut-off points for the NSTEMI (4 genes) and STEMI (13 genes) gene profiles were established up to three days after Myocardial Infarction. The study showed the different pathophysiologic conditions for STEMI and NSTEMI. Furthermore, the high-score gene profiles are suggested to measure up to 3 days after MI to distinguish the STEMI and NSTEMI.

ACSL1, CH25H, GPCPD1, and PLA2G12A as the potential lipid-related diagnostic biomarkers of acute myocardial infarction

Lipid metabolism plays an essential role in the genesis and progress of acute myocardial infarction (AMI). Herein, we identified and verified latent lipid-related genes involved in AMI by bioinformatic analysis. Lipid-related differentially expressed genes (DEGs) involved in AMI were identified using the GSE66360 dataset from the Gene Expression Omnibus (GEO) database and R software packages. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were conducted to analyze lipid-related DEGs. Lipid-related genes were identified by two machine learning techniques: least absolute shrinkage and selection operator (LASSO) regression and support vector machine recursive feature elimination (SVM-RFE). The receiver operating characteristic (ROC) curves were used to descript diagnostic accuracy. Furthermore, blood samples were collected from AMI patients and healthy individuals, and real-time quantitative polymerase chain reaction (RT-qPCR) was used to determine the RNA levels of four lipid-related DEGs. Fifty lipid-related DEGs were identified, 28 upregulated and 22 downregulated. Several enrichment terms related to lipid metabolism were found by GO and KEGG enrichment analyses. After LASSO and SVM-RFE screening, four genes (ACSL1, CH25H, GPCPD1, and PLA2G12A) were identified as potential diagnostic biomarkers for AMI. Moreover, the RT-qPCR analysis indicated that the expression levels of four DEGs in AMI patients and healthy individuals were consistent with bioinformatics analysis results. The validation of clinical samples suggested that 4 lipid-related DEGs are expected to be diagnostic markers for AMI and provide new targets for lipid therapy of AMI.

Soluble triggering receptor expressed on myeloid cells-1 as a serum biomarker of early neurologic deterioration and prognosis in acute supratentorial intracerebral hemorrhage

BACKGROUND

Triggering receptor expressed on myeloid cells-1 (TREM-1) participates in neuroinflammation. We intended to ascertain whether serum soluble TREM-1 (sTREM-1) could be utilized as a biomarker of inflammation, severity, early neurologic deterioration (END) and outcome after primary intracerebral hemorrhage (ICH).

METHODS

Serum sTREM-1 levels were gauged in 104 ICH patients and 104 healthy controls. END was diagnosed when the National Institutes of Health Stroke Scale (NIHSS) score increased ≥ 4 points or death between admission and 24 h after admission. Patients with a modified Rankin scale score of > 2 at 3 months were considered to have poor outcome.

RESULTS

As compared to controls, patients exhibited significantly elevated serum sTREM-1 levels (median: 309.0 vs 67.9 pg/ml). Serum sTREM-1 concentrations were intimately correlated with NIHSS score (r = 0.574), hematoma volume (r = 0.554), blood leukocyte count (r = 0.529) and serum C-reactive protein concentrations (r = 0.509). Serum sTREM-1 concentrations > 309.0 pg/ml independently predicted END and poor outcome with odds ratio values of 4.054 and 4.721 respectively. Serum sTREM-1 concentrations distinguished END and poor outcome with areas under receiver operating characteristic curve of 0.789 and 0.813 respectively.

CONCLUSION

Serum sTREM-1 may represent a promising inflammatory biomarker for assessment of severity and prediction of END and poor outcome after ICH.