Identification of Key Genes as Early Warning Signals of Acute Myocardial Infarction Based on Weighted Gene Correlation Network Analysis and Dynamic Network Biomarker Algorithm.

Abstract

Purpose:

The specific mechanisms and biomarkersunderlying the progression of stable coronary artery disease (CAD) to acute myocardial infarction (AMI) remain unclear. The current study aims to explore novel gene biomarkers associated with CAD progression by analyzing the transcriptomic sequencing data of peripheral blood monocytes in different stages of CAD.

Material and Methods:

A total of 24 age- and sex- matched patients at different CAD stages who received coronary angiography were enrolled, which included 8 patients with normal coronary angiography, 8 patients with angiographic intermediate lesion, and 8 patients with AMI. The RNA from peripheral blood monocytes was extracted and transcriptome sequenced to analyze the gene expression and the differentially expressed genes (DEG). A Gene Oncology (GO) enrichment analysis was performed to analyze the biological function of genes. Weighted gene correlation network analysis (WGCNA) was performed to classify genes into several gene modules with similar expression profiles, and correlation analysis was carried out to explore the association of each gene module with a clinical trait. The dynamic network biomarker (DNB) algorithm was used to calculate the key genes that promote disease progression. Finally, the overlapping genes between different analytic methods were explored.

Results:

WGCNA analysis identified a total of nine gene modules, of which two modules have the highest positive association with CAD stages. GO enrichment analysis indicated that the biological function of genes in these two gene modules was closely related to inflammatory response, which included T-cell activation, cell response to inflammatory stimuli, lymphocyte activation, cytokine production, and the apoptotic signaling pathway. DNB analysis identified a total of 103 genes that may play key roles in the progression of atherosclerosis plaque. The overlapping genes between DEG/WGCAN and DNB analysis identified the following 13 genes that may play key roles in the progression of atherosclerosis disease: SGPP2, DAZAP2, INSIG1, CD82, OLR1, ARL6IP1, LIMS1, CCL5, CDK7, HBP1, PLAU, SELENOS, and DNAJB6.

Conclusions:

The current study identified a total of 13 genes that may play key roles in the progression of atherosclerotic plaque and provides new insights for early warning biomarkers and underlying mechanisms underlying the progression of CAD.

• Authors: Chenxi Song, Zheng Qiao, Luonan Chen, Jing Ge, Rui Zhang, Sheng Yuan, Xiaohui Bian, Chunyue Wang, Qianqian Liu, Lei Jia, Rui Fu, Kefei Dou
• Journal: Frontiers in immunology (Front Immunol) Vol. 13 Pg. 879657 ( 2022) ISSN: 1664-3224 [Electronic] Switzerland
• PMID: 35795669 (Publication Type: Journal Article, Research Support, Non-U.S. Gov't)
• Copyright: Copyright © 2022 Song, Qiao, Chen, Ge, Zhang, Yuan, Bian, Wang, Liu, Jia, Fu and Dou.

Chemical References

• DNAJB6 protein, human
• Genetic Markers
• HBP1 protein, human
• HSP40 Heat-Shock Proteins
• High Mobility Group Proteins
• Molecular Chaperones
• Nerve Tissue Proteins
• Repressor Proteins

Topics

• Algorithms
• Atherosclerosis
• Coronary Artery Disease (genetics)
• Gene Expression Profiling (methods)
• Genetic Markers
• HSP40 Heat-Shock Proteins (genetics)
• High Mobility Group Proteins (genetics)
• Humans
• Molecular Chaperones (genetics)
• Myocardial Infarction (diagnosis, genetics)
• Nerve Tissue Proteins (genetics)
• Repressor Proteins (genetics)