Table_3_Identification of Four Potential Biomarkers Associated With Coronary Artery Disease in Non-diabetic Patients by Gene Co-expression Network Ana.docx (18.63 kB)
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Table_3_Identification of Four Potential Biomarkers Associated With Coronary Artery Disease in Non-diabetic Patients by Gene Co-expression Network Analysis.docx

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posted on 24.06.2020, 04:38 by Min Jiao, Jingtian Li, Quan Zhang, Xiufeng Xu, Ruidong Li, Peikang Dong, Chun Meng, Yi Li, Lijuan Wang, Wanpeng Qi, Kai Kang, Hongjie Wang, Tao Wang
Background

Coronary artery disease (CAD) is a type of cardiovascular disease that greatly hurts the health of human beings. Diabetic status is one of the largest clinical factors affecting CAD-associated gene expression changes. Most of the studies focus on diabetic patients, whereas few have been done for non-diabetic patients. Since the pathophysiological processes may vary among these patients, we cannot simply follow the standard based on the data from diabetic patients. Therefore, the prognostic and predictive diagnostic biomarkers for CAD in non-diabetic patient need to be fully recognized.

Materials and Methods

To screen out candidate genes associated with CAD in non-diabetic patients, weighted gene co-expression network analysis (WGCNA) was constructed to conduct an analysis of microarray expression profiling in patients with CAD. First, the microarray data GSE20680 and GSE20681 were downloaded from NCBI. We constructed co-expression modules via WGCNA after excluding the diabetic patients. As a result, 18 co-expression modules were screened out, including 1,225 differentially expressed genes (DEGs) that were obtained from 152 patients (luminal stenosis ≥50% in at least one major vessel) and 170 patients (stenosis of <50%). Subsequently, a Pearson’s correlation analysis was conducted between the modules and clinical traits. Then, a functional enrichment analysis was conducted, and we used gene network analysis to reveal hub genes. Last, we validated the hub genes with peripheral blood samples in an independent patient cohort using RT-qPCR.

Results

The results showed that the midnight blue module and the yellow module played vital roles in the pathogenesis of CAD in non-diabetic patients. Additionally, CD40, F11R, TNRC18, and calcium/calmodulin-dependent protein kinase type II gamma (CAMK2G) were screened out and validated using enzyme-linked immunosorbent assay (ELISA) in an independent patient cohort and immunohistochemical (IHC) staining in an atherosclerosis mouse model.

Conclusion

Our findings demonstrate that hub genes, CD40, F11R, TNRC18, and CAMK2G, are surrogate diagnostic biomarkers and/or therapeutic targets for CAD in non-diabetic patients and require deeper validation.

History

References