Identification of key proteins and pathways in myocardial infarction using machine learning approaches.

Acute myocardial infarction (AMI) is a leading cause of global morbidity and mortality, requiring deeper insights into its molecular mechanisms for improved diagnosis and treatment. This study combines proteomics, transcriptomics and machine learning (ML) to identify key proteins and pathways associated with AMI. Plasma samples from 48 AMI patients and 50 healthy controls (HC) were used for proteomic sequencing. Differentially expressed proteins (DEPs) were identified and analyzed for pathway enrichment. Protein-protein interaction (PPI) networks were constructed, and we conducted a meta-analysis (GSE60993, GSE61144, GSE48060) using an inverse variance model to combine differentially expressed genes (DEGs) identified via LIMMA and FDR adjustment across three studies. Clustering and co-expression analysis were performed using K-Medoids and weighted gene co-expression network analysis (WGCNA). ML feature selection identified hub proteins, which were validated across bulk, single-cell, and spatial datasets for atherosclerosis (ATH) and MI. In this study, we identified 437 DEPs with 291 up-regulated and 146 down-regulated proteins. Functional enrichment analysis revealed key pathways involved in inflammation, immunity, metabolism, and cellular stress responses, among others. Using non-negative matrix factorization (NNMF) and K-Medoids clustering, AMI patients were divided into two clusters (C1 and C2), with distinct protein expression patterns and inflammatory responses. Differential analysis between clusters revealed 200 cluster-specific DEPs, with C1 associated with angiogenesis and vascular remodeling, and C2 linked to cellular stress and apoptosis. A meta-analysis identified 1383 DEGs, and their intersection with DEPs yielded 63 proteins, which were subsequently refined by logistic regression to 36 AMI-associated proteins. Furthermore, a protein co-expression network analysis identified 49 modules, with the turquoise module being strongly associated with AMI highlighting pathways in lipid metabolism, immune response, and tissue repair. From this module, 17 key proteins were selected, and ML further distilled these to nine core features (CAMP, CLTC, CTNNB1, FUBP3, IQGAP1, MANBA, ORM1, PSME1, and SPP1) that are closely linked to immune regulation, apoptosis, and metabolism. These proteins were validated across multiple datasets. Single-cell analysis revealed distinct expression patterns of these proteins across cell types and spatial regions in ATH and MI, emphasizing their roles in inflammation, vascular remodeling, and plaque instability. This study identifies critical proteins and pathways in AMI, offering potential biomarkers and therapeutic targets. The use of ML provides a robust framework for identifying AMI's key molecular.