Wu Xize, Huang Yuxi, Ren Jiaqi, Pan Xue, Wu Qiuying, Cai Qicheng, Wang Ruiying, Feng Teng, Gao Shan, Wang Bo, Cheng Meijia, Li Yue, Gong Lihong
The First Clinical College, Liaoning University of Traditional Chinese Medicine, Shenyang, China.
College of Traditional Chinese Medicine, Dazhou Vocational College of Chinese Medicine, Dazhou, China.
Front Immunol. 2025 Aug 14;16:1642984. doi: 10.3389/fimmu.2025.1642984. eCollection 2025.
Atherosclerosis (AS), characterized by lipid accumulation, contributes significantly to global cardiovascular morbidity. Ferroptosis, an iron-dependent form of cell death triggered by lipid peroxidation, is emerging as a critical player in AS progression. Therefore, our study seeks to elucidate the intricate mechanisms of ferroptosis within the lipid metabolism pathway in AS.
Differentially expressed genes were identified from the GSE100927 dataset, subsequently isolating AS lipid metabolism-related ferroptosis genes (ASLMRFeGs). Unsupervised cluster analysis was performed on AS samples to identify molecular clusters. WGCNA was performed to uncover module Hub genes. Multiple machine learning models (LASSO, SVM-RFE, RF) were applied to screen Hub genes. Experimental validation was performed by ox-LDL-induced HUVECs and RAW 264.7 cells. Single-cell data analyzes the gene structure and gene expression status of individual cells.
Six ASLMRFeGs (CTSB, CYBB, DPP4, HILPDA, HMOX1, IL1B) alter the immune microenvironment in AS. AS samples were stratified into two molecular clusters, exhibiting significant variations in inflammation and immune responses. Enrichment analysis of the 225 module Hub genes showed close association with inflammation, immune responses, cytoskeleton organization, and various organelles. Machine learning identified four candidate Hub genes (TYROBP, CSF1R, LCP2, C1QA). experiments showed that dysregulated lipid metabolism promotes ferroptosis, and inhibition of ferroptosis improves mitochondrial and lysosomal dysfunction and suppresses endoplasmic reticulum stress. Ferrostatin-1, an ferroptosis inhibitor, attenuated the ox-LDL-induced upregulation of CYBB, HMOX1, IL1B, TYROBP, and CSF1R genes. A nomogram for predicting AS risk was constructed incorporating the expression levels of these five validated Hub genes. Single-cell data analysis results suggested that these genes were highly expressed in foam cells, inflammatory macrophages, smooth muscle cells, and helper T cells.
In AS, abnormal lipid metabolism may drive ferroptosis via key regulatory genes (CYBB, HMOX1, IL1B, TYROBP, CSF1R), while also reshaping the immune microenvironment, potentially through the modulation of organelle function.
动脉粥样硬化(AS)以脂质积聚为特征,是全球心血管疾病发病的重要原因。铁死亡是一种由脂质过氧化引发的铁依赖性细胞死亡形式,正逐渐成为AS进展中的关键因素。因此,我们的研究旨在阐明AS脂质代谢途径中铁死亡的复杂机制。
从GSE100927数据集中鉴定差异表达基因,随后分离出AS脂质代谢相关铁死亡基因(ASLMRFeGs)。对AS样本进行无监督聚类分析以识别分子簇。进行加权基因共表达网络分析(WGCNA)以揭示模块中心基因。应用多种机器学习模型(LASSO、支持向量机递归特征消除法(SVM-RFE)、随机森林(RF))筛选中心基因。通过氧化型低密度脂蛋白(ox-LDL)诱导的人脐静脉内皮细胞(HUVECs)和RAW 264.7细胞进行实验验证。单细胞数据分析单个细胞的基因结构和基因表达状态。
六个ASLMRFeGs(组织蛋白酶B(CTSB)、细胞色素b-245β链(CYBB)、二肽基肽酶4(DPP4)、高迁移率族蛋白结构域包含蛋白A(HILPDA)、血红素加氧酶1(HMOX1)、白细胞介素1β(IL1B))改变了AS中的免疫微环境。AS样本被分层为两个分子簇,在炎症和免疫反应方面表现出显著差异。对225个模块中心基因的富集分析表明与炎症、免疫反应、细胞骨架组织和各种细胞器密切相关。机器学习确定了四个候选中心基因(酪氨酸蛋白激酶结合蛋白(TYROBP)、集落刺激因子1受体(CSF1R)、淋巴细胞胞质蛋白2(LCP2)、补体C1q亚基A(C1QA))。实验表明脂质代谢失调促进铁死亡,抑制铁死亡可改善线粒体和溶酶体功能障碍并抑制内质网应激。铁死亡抑制剂铁抑素-1减弱了ox-LDL诱导的CYBB、HMOX1、IL1B、TYROBP和CSF1R基因的上调。结合这五个经过验证的中心基因的表达水平构建了一个预测AS风险的列线图。单细胞数据分析结果表明这些基因在泡沫细胞、炎性巨噬细胞、平滑肌细胞和辅助性T细胞中高表达。
在AS中,异常的脂质代谢可能通过关键调控基因(CYBB、HMOX1、IL1B、TYROBP、CSF1R)驱动铁死亡,同时也可能通过调节细胞器功能重塑免疫微环境。