Abstract
BACKGROUND: COVID-19 is a disease that affects people globally. Beyond affecting the respiratory system, COVID-19 patients are at an elevated risk for both venous and arterial thrombosis. This heightened risk contributes to an increased probability of acute complications, including acute myocardial infarction (AMI) and acute ischemic stroke (AIS). Given the unclear relationship between COVID-19, AMI, and AIS, it is crucial to gain a deeper understanding of their associations and potential molecular mechanisms. This study aims to utilize bioinformatics to analyze gene expression data, identify potential therapeutic targets and biomarkers, and explore the role of immune cells in the disease. METHODS: This study employed three Gene Expression Omnibus (GEO) datasets for analysis, which included data on COVID-19, AMI and AIS. We performed enrichment analysis on the co-DEGs for these three diseases to clarify gene pathways and functions, and also examined the relationship between co-DEGs and immune infiltration. Machine learning techniques and protein-protein interaction networks (PPI) were used to identify hub genes within the co-DEGs. Finally, we employed a dual validation strategy integrating independent GEO datasets and in vitro experiments with human blood samples to comprehensively assess the reliability of our experimental findings. RESULTS: We identified 88 co-DEGs associated with COVID-19, AMI and AIS. Enrichment analysis results indicated that co-DEGs were significantly enriched in immune inflammatory responses related to leukocytes and neutrophils. Immune infiltration analysis revealed significant differences in immune cell populations between the disease group and the normal group. Finally, genes selected through machine learning methods included: CLEC4E, S100A12, and IL1R2. Based on the PPI network, the top ten most influential DEGs were identified as MMP9, TLR2, TLR4, ITGAM, S100A12, FCGR1A, CD163, FCER1G, FPR2, and CLEC4D. The integration of the protein-protein interaction (PPI) network with machine learning techniques facilitated the identification of S100A12 as a potential common biomarker for early diagnosis and a therapeutic target for all three diseases. Ultimately, validation of S100A12 showed that it was consistent with our experimental results, confirming its reliability as a biomarker. Moreover, it demonstrated good diagnostic performance for the three diseases. CONCLUSION: We employed bioinformatics methods and machine learning to investigate common diagnostic biomarkers and immune infiltration characteristics of COVID-19, AMI and AIS. Functional and pathway analyses indicated that the co-DEGs were primarily enriched in immune inflammatory responses related to leukocytes and neutrophils. Through two machine learning approaches and the PPI network, and subsequent validation and evaluation, we identified S100A12 as a potential common therapeutic target and biomarker related to immune response that may influence these three diseases.