Atherosclerosis is a chronic inflammatory disease that affects arterial walls and is a leading cause of cardiovascular disease. Gene co-expression modules can provide insight into the molecular mechanisms underlying atherosclerosis progression. In this study, gene co-expression network analysis (WGCNA) was done to identify gene co-expression modules associated with atherosclerosis progression. Before conducting WGCNA, preprocessing and soft power selection were performed on the GSE28829, GSE100927, GSE43292, GSE10334, and GSE16134 datasets (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi). Co-expression modules were identified using dynamic tree cuts, and their correlations and trait associations were visualized. Enrichment analysis was performed on the blue and magenta modules to identify biological processes (BP) and pathways related to atherosclerosis. The CIBERSORT algorithm was used to predict immune cell infiltration in early and advanced atherosclerotic plaques. We identified 12 co-expression modules, in which blue and magenta were most highly correlated with atherosclerosis progression. The blue module was enriched for inflammation- and immune-related BP and pathways, including phagosome, lysosome, osteoclast differentiation, chemokine signaling pathway, platelet activation, NF-kappa B signaling pathway, Fc gamma R-mediated phagocytosis, lipid and atherosclerosis, autophagy, and apoptosis. The magenta module was significantly enriched for vascular permeability regulation, positive and negative regulation of epithelial to mesenchymal transition, and lamellipodium. Additionally, the CIBERSORT algorithm predicted less abundance of T regulatory cells and monocytes in advanced compared to early atherosclerotic plaques. The enrichment analysis of BP, cellular components, molecular functions, and atherosclerosis-related pathways in the blue and magenta modules showed that inflammation and immune response played a key role in the progression of atherosclerosis. Our study provides insights into the molecular mechanisms underlying atherosclerosis progression and identifies potential therapeutic targets for the treatment of atherosclerosis. The identification of immune cell subtypes associated with atherosclerosis could lead to the development of immunomodulatory therapies to prevent or treat atherosclerosis.

动脉粥样硬化是一种影响动脉壁的慢性炎症性疾病，是心血管疾病的主要原因。基因共表达模块可以深入了解动脉粥样硬化进展的分子机制。在这项研究中，进行了基因共表达网络分析（WGCNA）来识别与动脉粥样硬化进展相关的基因共表达模块。在进行 WGCNA 之前，对 GSE28829、GSE100927、GSE43292、GSE10334 和 GSE16134 数据集进行了预处理和软实力选择（https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi）。使用动态树切割来识别共表达模块，并将它们的相关性和性状关联可视化。对蓝色和洋红色模块进行富集分析，以确定与动脉粥样硬化相关的生物过程 (BP) 和途径。CIBERSORT 算法用于预测早期和晚期动脉粥样硬化斑块中的免疫细胞浸润。我们确定了 12 个共表达模块，其中蓝色和洋红色与动脉粥样硬化进展相关性最高。蓝色模块丰富了炎症和免疫相关的BP和通路，包括吞噬体、溶酶体、破骨细胞分化、趋化因子信号通路、血小板激活、NF-κB信号通路、FcγR介导的吞噬作用、脂质和动脉粥样硬化、自噬和细胞凋亡。洋红色模块显着丰富了血管通透性调节、上皮间质转化的正向和负向调节以及片状足。此外，CIBERSORT 算法预测，与早期动脉粥样硬化斑块相比，晚期 T 调节细胞和单核细胞的丰度较低。蓝色和洋红色模块中BP、细胞成分、分子功能和动脉粥样硬化相关通路的富集分析表明，炎症和免疫反应在动脉粥样硬化的进展中发挥着关键作用。我们的研究提供了对动脉粥样硬化进展的分子机制的见解，并确定了治疗动脉粥样硬化的潜在治疗靶点。与动脉粥样硬化相关的免疫细胞亚型的鉴定可能会导致开发预防或治疗动脉粥样硬化的免疫调节疗法。