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

作者：Chang Liu, Xing Zhang, Qian Xie, Binbin Fang, Fen Liu, Junyi Luo, Gulandanmu Aihemaiti, Wei Ji, Yi‐Ning Yang, Xiao‐Mei Li · 发表于：Scientific Reports · 年份：2025 · DOI：10.1038/s41598-025-04401-w · 被引用次数：4 · 研究领域：Bioinformatics and Genomic Networks、Atherosclerosis and Cardiovascular Diseases、Machine Learning in Bioinformatics

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. Differen...