Identification of hub biomarkers in coronary artery disease patients using machine learning and bioinformatic analyses

Understanding the molecular underpinnings of CAD is essential for developing effective therapeutic strategies. This study aims to identify and analyze differentially expressed hub biomarkers in the peripheral blood of CAD patients. Based on RNA-seq datasets from the Gene Expression Omnibus database, machine learning algorithms including LASSO, RF, and SVM-RFE were applied. Furthermore, the hub biomarkers were enriched to ascertain their roles in immune cell expression and signaling pathways through GO, KEGG, GSVE, and GSVA. An in vivo experiment was conducted to verify the hub biomarkers. Eleven hub biomarkers (ITM2B, GNA15, PLAU, GNG11, HIST1H2BH, SLC11A1, RPS7, DDIT4, CD83, GNLY, and S100A12) were identified and associated with CD8 + T cells and NK cells. They were mainly involved in immune responses, cardiac muscle contraction, oxidative phosphorylation, and apoptotic signaling pathways. Moreover, ITM2B had the most importance and significance to be the biomarker of CAD patients. In conclusion, these findings point to the possibility of ITM2B as a biomarker on the inflammatory pathogenesis of CAD and suggest new options for therapeutic intervention.