A Clinically Applicable Diagnostic Framework for Acute Myocardial Infarction: Leveraging Multiomics and Machine Learning to Decipher LPS‐Associated Immune Signatures

Background The early and accurate diagnosis of acute myocardial infarction (AMI) remains a critical challenge in clinical practice. While inflammation is a cornerstone of AMI pathogenesis, translating specific inflammatory pathways into clinically actionable tools requires an integrative analytical approach. The discovery of reliable molecular biomarkers reflecting these pathways is crucial for advancing diagnostic precision. This study aims to bridge this gap by employing a multiomics and machine learning strategy to identify and validate a robust diagnostic signature derived from lipopolysaccharide (LPS)‐related immune responses. Methods We performed an integrated transcriptomic analysis using five Gene Expression Omnibus (GEO) datasets. Differential expression, functional enrichment, and weighted gene coexpression network analysis (WGCNA) were conducted to pinpoint AMI‐associated modules. A core diagnostic gene panel was refined through a consensus machine learning–driven feature selection pipeline incorporating LASSO, support vector machine–recursive feature elimination (SVM–RFE), and random forest algorithms. The immune microenvironment was profiled using CIBERSORT, and patient stratification was achieved via consensus clustering. The clinical utility of the final signature was evaluated by constructing and validating a machine learning–based diagnostic model and a corresponding nomogram. Results Our multiomics workflow identified an eight‐gene LPS‐related signature (S100A9, SERPINA1, S100A12, MCEMP1, MAFB, C5AR1, RNASE2, and VSIG4) with high diagnostic accuracy across independent cohorts. A machine learning–optimized neural network model (NNET) and LASSO regression both achieved an area under the curve (AUC) of 0.859. Immune deconvolution revealed significant shifts in neutrophil and macrophage subsets, correlating strongly with signature genes. Unsupervised learning delineated two distinct AMI subtypes, with Cluster A exhibiting a hyperinflammatory phenotype characterized by the upregulation of this signature. Conclusion This study establishes a novel, machine learning–curated multiomics signature that captures TLR4 axis–related innate immune activation in AMI. The derived diagnostic model and nomogram offer a direct path for clinical translation, potentially aiding in rapid patient stratification. Furthermore, the identification of immune‐endotypic subtypes paves the way for biomarker‐guided personalized therapeutic strategies, moving beyond a one‐size‐fits‐all approach to AMI management. Prospective validation in real‐world clinical settings is warranted.