Li Zhile, Ling Hong, Wei Qiuyu, Tang Xiukai, Zhang Danyi, Huang Zhaohe
Department of Cardiology, Affiliated Hospital of Youjiang Medical University for Nationalities, Baise, Guangxi, China.
Laboratory of the Atherosclerosis and Ischemic Cardiovascular Diseases, Affiliated Hospital of Youjiang Medical University for Nationalities, Baise, Guangxi, China.
Front Cardiovasc Med. 2025 Aug 14;12:1600321. doi: 10.3389/fcvm.2025.1600321. eCollection 2025.
Atherosclerosis (AS), the primary cause of cardiovascular disorders and stroke, is a complex, multifactorial disease. Numerous studies have shown that oxidative stress and circadian disruption are paramount contributors to the development of AS and its complications. Nevertheless, there is no applicable related diagnostic model to assess the AS clinical risk according to patients' oxidative stress status and circadian rhythm molecular expression. This study aimed to develop an oxidative stress-circadian rhythm-related model using AS cohorts (GSE100927 and GSE43292) to explore the potential relationship between AS and oxidative stress with circadian rhythm. We screened the significant oxidative stress-circadian rhythm-related genes in AS samples by integrating two datasets by various machine learning methods. Then, we developed an oxidative stress-circadian rhythm-related diagnostic model based on six risk genes (, , , , , ) identified through LASSO regression analysis and a nomogram diagram. Calibration and decision curve analysis (DCA) showed the relevant accuracy of the risk model. Receiver operating characteristic curve (ROC) delineated the higher reliability of our model than each single risk gene diagnostic model. Then, we verified the accuracy of our model in the validation dataset (GSE27034). Latent regulatory networks (including miRNA, transcription factor, and small-molecule compound) regarding risk genes were also constructed using the ENCORO, ChIPBase, and CTD databases. We observed significantly greater immune infiltration in the high-risk group of AS samples than that in the low-risk group based on the linear predictor derived from our logistic model. Finally, we classified the AS samples into two subtypes according to the expression patterns of risk genes and, interestingly, found an obvious discrepancy in immune cell infiltration between these subtypes.
动脉粥样硬化（AS）是心血管疾病和中风的主要原因，是一种复杂的多因素疾病。大量研究表明，氧化应激和昼夜节律紊乱是AS及其并发症发生发展的重要因素。然而，目前尚无适用的相关诊断模型可根据患者的氧化应激状态和昼夜节律分子表达来评估AS临床风险。本研究旨在利用AS队列（GSE100927和GSE43292）建立一个与氧化应激-昼夜节律相关的模型，以探讨AS与氧化应激及昼夜节律之间的潜在关系。我们通过各种机器学习方法整合两个数据集，筛选出AS样本中与氧化应激-昼夜节律相关的显著基因。然后，我们基于通过LASSO回归分析确定的六个风险基因（、、、、、）和列线图，开发了一个与氧化应激-昼夜节律相关的诊断模型。校准和决策曲线分析（DCA）显示了风险模型的相关准确性。受试者工作特征曲线（ROC）表明我们的模型比每个单一风险基因诊断模型具有更高的可靠性。然后，我们在验证数据集（GSE27034）中验证了模型的准确性。还使用ENCORO、ChIPBase和CTD数据库构建了关于风险基因的潜在调控网络（包括miRNA、转录因子和小分子化合物）。基于我们的逻辑模型得出的线性预测指标，我们观察到AS样本高危组的免疫浸润明显高于低危组。最后，我们根据风险基因的表达模式将AS样本分为两个亚型，有趣的是，发现这些亚型之间免疫细胞浸润存在明显差异。
