Integrated bioinformatics and machine learning algorithms reveal the unfolded protein response pathways and immune infiltration in acute myocardial infarction.

Abstract

Background: The unfolded protein response (UPR) is a critical biological process related to a variety of physiological functions and cardiac disease. However, the role of UPR-related genes in acute myocardial infarction (AMI) has not been well characterized. Therefore, this study aims to elucidate the mechanism and role of the UPR in the context of AMI.

Methods: Gene expression profiles related to AMI and UPR pathway were downloaded from the Gene Expression Omnibus database and PathCards database, respectively. Differentially expressed genes (DEGs) were identified and then functionally annotated. The random forest (RF) and least absolute shrinkage and selection operator (LASSO) regression analysis were conducted to identify potential diagnostic UPR-AMI biomarkers. Furthermore, the results were validated by using external data sets, and discriminability was measured by the area under the curve (AUC). A nomogram based on the feature genes was developed to predict the AMI-risk rate. Then we utilized two algorithms, CIBERSORT and MCPcounter, to investigate the relationship between the key genes and immune microenvironment. Additionally, we performed uniform clustering of AMI samples based on the expression of UPR pathway-related genes. The weighted gene co-expression network analysis was conducted to identify the key modules in various clusters, enrichment analysis was performed for the genes existing in different modules.

Results: A total of 14 DEGs related to the UPR pathway were identified. Among the 14 DEGs, CEBPB, ATF3, EIF2S3, and TSPYL2 were subsequently identified as biomarkers by the LASSO and RF algorithms. A diagnostic model was constructed with these four genes, and the AUC was 0.939. The calibration curves, receiver operating characteristic (ROC) curves, and the decision curve analysis of the nomogram exhibited good performance. Furthermore, immune cell infiltration analysis revealed that four feature genes were linked with the infiltration of immune cells such as neutrophils. The cluster analysis of the AMI samples identified two distinct clusters, each with differential expression of genes related to the UPR pathway, immune cell infiltration, and inflammatory cytokine secretion. Weighted gene coexpression network analysis and enrichment analysis showed that both clusters were associated with the UPR.

Conclusions: Our study highlights the importance of the UPR pathway in the pathogenesis of myocardial infarction, and identifies four genes CEBPB, ATF3, EIF2S3, and TSPYL2 as diagnostic biomarkers for AMI, providing new ideas for the clinical diagnosis and treatment of AMI.