Identification of Diagnostically Relevant Biomarkers in Patients with Coronary Artery Disease by Comprehensive Analysis

Zimin Wu,1,* Sisi Mo,2,* Zuyuan Huang,1 Baoshi Zheng1 1Department of Cardiovascular Surgery Ward, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region, 530021, People’s Republic of China; 2Department of Medical Research, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region, 530021, People’s Republic of China*These authors contributed equally to this workCorrespondence: Baoshi Zheng, Department of Cardiovascular Surgery Ward, the First Affiliated Hospital of Guangxi Medical University, No. 6 Shuangyong Road, Qingxiu District, Nanning, Guangxi Zhuang Autonomous Region, 530021, People’s Republic of China, Tel +86 0771-5355280, Email [email protected]: Peripheral biomarkers are becoming an important method by which to monitor the progression of coronary artery disease (CAD). Not only are they minimally invasive and early detection, but they can also be used for classification and diagnosis of disease as well as prognostic assessment. Currently, this approach is still in the exploratory stage. The purpose of this research is to determine the diagnostic value and therapeutic potential of the endoplasmic reticulum stress (ERS) genes in CAD.Methods: The clinical information and RNA sequence data were obtained from the GEO database and subsequently subjected to a series of optimization and visualization processes using various analytical techniques, including WGCNA, LASSO, SVM-RFE feature selection, random forest (RF), and XGBoost, as well as R software and Cytoscape. Finally, immunofluorescence was used to validate the analysis.Results: We identify 6 key ERS differentially expressed genes (ERS-DEGs) (UFL1, HSPA1A, ERLIN1, LRRK2, ERN1, SERINC3) for constructing diagnostic models. They showed qualified diagnostic ability as biomarkers of CAD within training dataset (AUC = 0.803) and validation dataset (AUC = 0.776 and 0.797). Association analyses showed that peripheral immune cells, immune checkpoint genes and Human Leukocyte Antigen (HLA) genes had characteristic distributions in CAD and were closely related to specific ERS genes. Meanwhile, we found that HSPA1A may involve the MAPK signaling pathway in CAD.Conclusion: We constructed an efficient diagnostic model based on 6 key ERS-DEGs and explored their regulatory networks and effects on the CAD immune microenvironment. UFL1, HSPA1A, ERLIN1, LRRK2, ERN1, SERINC3 are expected to be biomarkers for CAD.Keywords: endoplasmic reticulum stress, coronary artery disease, HSPA1A, immune microenvironment