ArticleIET systems biology
Novel Biomarker Identification for Acute Coronary Syndrome via Integrating WGCNA and Machine Learning.
Article in IET systems biology. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
1 citing paper in PubMed.
- Novel Biomarker Identification for Acute Coronary Syndrome via Integrating WGCNA and Machine Learning.IET systems biologyArticle
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Authors and funding
4 authors.
Funding
Abstract
Immune cells in early atherosclerotic lesions promote inflammation and acute coronary syndrome (ACS), but the precise link between inflammation and ACS progression is still unclear. In this study, we analysed mRNA and miRNA expression profiles of ACS from GEO, identifying 98 mRNAs and 627 miRNAs by differentially expressed analysis. GSEA revealed abnormal activation of immune- and inflammation-related pathways, such as T cell receptor signalling pathway and cell adhesion molecules cams. The biomarkers ARG1, HECW2, and PFKFB3 were identified through WGCNA, LASSO, and SVM-RFE. Diagnostic performance and miRNA-mRNA interaction network were performed using ROC curves and Cytoscape. CIBERSORT analysis revealed that the levels of CD4 memory resting T cells were downregulated, whereas monocytes and neutrophils were upregulated. ARG1, HECW2 and PFKFB3 showed close relationships with specific immune cell types. These findings offer new avenues for ACS treatments and identify ARG1, HECW2 and PFKFB3 as potential biomarkers.
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