Wu Xize, Kang Jian, Pan Xue, Xue Chentian, Pan Jiaxiang, Quan Chao, Ren Lihong, Gong Lihong, Li Yue
Department of Critical Care Medicine, Nantong Hospital of Traditional Chinese Medicine, Nantong Hospital Affiliated to Nanjing University of Chinese Medicine, Nantong, Jiangsu, China.
Graduate School, Liaoning University of Traditional Chinese Medicine, Shenyang, Liaoning, China.
Front Cardiovasc Med. 2024 Nov 1;11:1471153. doi: 10.3389/fcvm.2024.1471153. eCollection 2024.
Atherosclerosis is a leading cause of cardiovascular disease worldwide, while carotid atherosclerosis (CAS) is more likely to cause ischemic cerebrovascular events. Emerging evidence suggests that cuproptosis may be associated with an increased risk of atherosclerotic cardiovascular disease. This study aims to explore the potential mechanisms linking cuproptosis and CAS.
The GSE100927 and GSE43292 datasets were merged to screen for CAS differentially expressed genes (DEGs) and intersected with cuproptosis-related genes to obtain CAS cuproptosis-related genes (CASCRGs). Unsupervised cluster analysis was performed on CAS samples to identify cuproptosis molecular clusters. Weighted gene co-expression network analysis was performed on all samples and cuproptosis molecule clusters to identify common module genes. CAS-specific DEGs were identified in the GSE100927 dataset and intersected with common module genes to obtain candidate hub genes. Finally, 83 machine learning models were constructed to screen hub genes and construct a nomogram to predict the incidence of CAS.
Four ASCRGs (NLRP3, SLC31A2, CDKN2A, and GLS) were identified as regulators of the immune infiltration microenvironment in CAS. CAS samples were identified with two cuproptosis-related molecular clusters with significant biological function differences based on ASCRGs. 220 common module hub genes and 1,518 CAS-specific DEGs were intersected to obtain 58 candidate hub genes, and the machine learning model showed that the Lasso + XGBoost model exhibited the best discriminative performance. Further external validation of single gene differential analysis and nomogram identified SGCE, PCDH7, RAB23, and RIMKLB as hub genes; SGCE and PCDH7 were also used as biomarkers to characterize CAS plaque stability. Finally, a nomogram was developed to assess the incidence of CAS and exhibited satisfactory predictive performance.
Cuproptosis alters the CAS immune infiltration microenvironment and may regulate actin cytoskeleton formation.
动脉粥样硬化是全球心血管疾病的主要病因，而颈动脉粥样硬化（CAS）更易引发缺血性脑血管事件。新出现的证据表明，铜死亡可能与动脉粥样硬化性心血管疾病风险增加有关。本研究旨在探讨铜死亡与CAS之间潜在的关联机制。
合并GSE100927和GSE43292数据集以筛选CAS差异表达基因（DEG），并与铜死亡相关基因进行交集分析，以获得CAS铜死亡相关基因（CASCRG）。对CAS样本进行无监督聚类分析以识别铜死亡分子簇。对所有样本和铜死亡分子簇进行加权基因共表达网络分析，以识别共同模块基因。在GSE100927数据集中识别CAS特异性DEG，并与共同模块基因进行交集分析以获得候选枢纽基因。最后，构建83个机器学习模型以筛选枢纽基因并构建列线图来预测CAS的发生率。
四个ASCRG（NLRP3、SLC31A2、CDKN2A和GLS）被确定为CAS免疫浸润微环境的调节因子。基于ASCRG，CAS样本被识别为具有显著生物学功能差异的两个铜死亡相关分子簇。将220个共同模块枢纽基因与1518个CAS特异性DEG进行交集分析，获得58个候选枢纽基因，机器学习模型显示Lasso + XGBoost模型表现出最佳的判别性能。单基因差异分析和列线图的进一步外部验证确定SGCE、PCDH7、RAB23和RIMKLB为枢纽基因；SGCE和PCDH7也被用作表征CAS斑块稳定性的生物标志物。最后，开发了一个列线图来评估CAS的发生率，并表现出令人满意的预测性能。
铜死亡改变了CAS免疫浸润微环境，并可能调节肌动蛋白细胞骨架的形成。
