Liu Chang, Zhang Xing, Xie Qian, Fang Binbin, Liu Fen, Luo Junyi, Aihemaiti Gulandanmu, Ji Wei, Yang Yining, Li Xiaomei
Department of Cardiology, The first Affiliated Hospital of Xinjiang Medical University, Urumqi, 830054, China.
Xinjiang Key Laboratory of Cardiovascular Disease Research, Clinical Medical Research Institute of First Affiliated Hospital of Xinjiang Medical University, Urumqi, 830054, China.
Sci Rep. 2025 Jun 4;15(1):19530. doi: 10.1038/s41598-025-04401-w.
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.
急性心肌梗死（AMI）是全球发病和死亡的主要原因，需要更深入了解其分子机制以改善诊断和治疗。本研究结合蛋白质组学、转录组学和机器学习（ML）来识别与AMI相关的关键蛋白质和信号通路。使用48例AMI患者和50例健康对照（HC）的血浆样本进行蛋白质组测序。鉴定差异表达蛋白（DEP）并分析其通路富集情况。构建蛋白质-蛋白质相互作用（PPI）网络，并使用逆方差模型进行荟萃分析（GSE60993、GSE61144、GSE48060），以合并通过LIMMA和FDR调整在三项研究中鉴定的差异表达基因（DEG）。使用K-中心点算法和加权基因共表达网络分析（WGCNA）进行聚类和共表达分析。ML特征选择识别出枢纽蛋白，并在动脉粥样硬化（ATH）和心肌梗死的批量、单细胞和空间数据集中进行验证。在本研究中，我们鉴定出437个DEP，其中291个蛋白上调，146个蛋白下调。功能富集分析揭示了参与炎症、免疫、代谢和细胞应激反应等的关键信号通路。使用非负矩阵分解（NNMF）和K-中心点算法聚类，将AMI患者分为两个簇（C1和C2），具有不同的蛋白质表达模式和炎症反应。簇间差异分析揭示了200个簇特异性DEP，C1与血管生成和血管重塑相关，C2与细胞应激和凋亡相关。荟萃分析鉴定出1383个DEG，它们与DEP的交集产生63个蛋白质，随后通过逻辑回归将其精炼为36个与AMI相关的蛋白质。此外，蛋白质共表达网络分析鉴定出49个模块，其中蓝绿色模块与AMI密切相关，突出了脂质代谢、免疫反应和组织修复中的信号通路。从该模块中选择了17个关键蛋白，ML进一步将其提炼为9个核心特征（CAMP、CLTC、CTNNB1、FUBP3、IQGAP1、MANBA、ORM1、PSME1和SPP1），这些特征与免疫调节、凋亡和代谢密切相关。这些蛋白质在多个数据集中得到验证。单细胞分析揭示了这些蛋白质在ATH和MI的不同细胞类型和空间区域中的独特表达模式，强调了它们在炎症、血管重塑和斑块不稳定性中的作用。本研究确定了AMI中的关键蛋白质和信号通路，提供了潜在的生物标志物和治疗靶点。ML的使用为识别AMI的关键分子提供了一个强大的框架。
