Identification of Potential Crucial Biomarkers in STEMI Through Integrated Bioinformatic Analysis.

Arq Bras Cardiol

Department of Nephrology, The Key Laboratory for the Prevention and Treatment of Chronic Kidney Disease of Chongqing, Chongqing Clinical Research Center of Kidney and Urology Diseases, Xinqiao Hospital, Army Medical University (Third Military Medical University), Chongqing - China.

Published: April 2024

AI Article Synopsis

  • The study investigates potential biomarkers for ST-segment elevation myocardial infarction (STEMI), a major cause of cardiovascular mortality.
  • A bioinformatics analysis identified 133 differentially expressed genes (DEGs) that highlight the roles of inflammation, metabolism, and immunity in the progression from stable coronary artery disease (SCAD) to STEMI.
  • Specific genes MS4A3, KLRC4, KLRD1, AQP9, and CD14 showed promise as candidate biomarkers due to their high sensitivity and specificity in predicting STEMI.

Article Abstract

Background: ST-segment elevation myocardial infarction (STEMI) is one of the leading causes of fatal cardiovascular diseases, which have been the prime cause of mortality worldwide. Diagnosis in the early phase would benefit clinical intervention and prognosis, but the exploration of the biomarkers of STEMI is still lacking.

Objectives: In this study, we conducted a bioinformatics analysis to identify potential crucial biomarkers in the progress of STEMI.

Methods: We obtained GSE59867 for STEMI and stable coronary artery disease (SCAD) patients. Differentially expressed genes (DEGs) were screened with the threshold of |log2fold change| > 0.5 and p <0.05. Based on these genes, we conducted enrichment analysis to explore the potential relevance between genes and to screen hub genes. Subsequently, hub genes were analyzed to detect related miRNAs and DAVID to detect transcription factors for further analysis. Finally, GSE62646 was utilized to assess DEGs specificity, with genes demonstrating AUC results exceeding 75%, indicating their potential as candidate biomarkers.

Results: 133 DEGs between SCAD and STEMI were obtained. Then, the PPI network of DEGs was constructed using String and Cytoscape, and further analysis determined hub genes and 6 molecular complexes. Functional enrichment analysis of the DEGs suggests that pathways related to inflammation, metabolism, and immunity play a pivotal role in the progression from SCAD to STEMI. Besides, related-miRNAs were predicted, has-miR-124, has-miR-130a/b, and has-miR-301a/b regulated the expression of the largest number of genes. Meanwhile, Transcription factors analysis indicate that EVI1, AML1, GATA1, and PPARG are the most enriched gene. Finally, ROC curves demonstrate that MS4A3, KLRC4, KLRD1, AQP9, and CD14 exhibit both high sensitivity and specificity in predicting STEMI.

Conclusions: This study revealed that immunity, metabolism, and inflammation are involved in the development of STEMI derived from SCAD, and 6 genes, including MS4A3, KLRC4, KLRD1, AQP9, CD14, and CCR1, could be employed as candidate biomarkers to STEMI.

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Source
http://dx.doi.org/10.36660/abc.20230462DOI Listing

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