Zheng Biwei, Li Yujing, Xiong Guoliang
Department of Cardiology, Dongguan Hospital of Integrated Chinese and Western Medicine Affiliated to Guangzhou University of Traditional Chinese Medicine, Dongguan, China.
Shenzhen Traditional Chinese Medicine Hospital, The Fourth Clinical Medical College of Guangzhou University of Chinese Medicine, Shenzhen, China.
Front Genet. 2024 Feb 29;15:1351774. doi: 10.3389/fgene.2024.1351774. eCollection 2024.
Coronary artery disease (CAD) is the most common type of cardiovascular disease and cause significant morbidity and mortality. Abnormal coagulation cascade is one of the high-risk factors in CAD patients, but the molecular mechanism of coagulation in CAD is still limited. We clustered and categorized 352 CAD paitents based on the expression patterns of coagulation-related genes (CRGs), and then we explored the molecular and immunological variations across the subgroups to reveal the underlying biological characteristics of CAD patients. The feature genes between CRG-subgroups were further identified using a random forest model (RF) and least absolute shrinkage and selection operator (LASSO) regression, and an artificial neural network prediction model was constructed. CAD patients could be divided into the C1 and C2 CRG-subgroups, with the C1 subgroup highly enriched in immune-related signaling pathways. The differential expressed genes between the two CRG-subgroups (DE-CRGs) were primarily enriched in signaling pathways connected to signal transduction and energy metabolism. Subsequently, 10 feature DE-CRGs were identified by RF and LASSO. We constructed a novel artificial neural network model using these 10 genes and evaluated and validated its diagnostic performance on a public dataset. Diverse molecular subgroups of CAD patients may each have a unique gene expression pattern. We may identify subgroups using a few feature genes, providing a theoretical basis for the precise treatment of CAD patients with different molecular subgroups.
冠状动脉疾病（CAD）是最常见的心血管疾病类型，会导致严重的发病率和死亡率。异常凝血级联反应是CAD患者的高危因素之一，但CAD中凝血的分子机制仍不明确。我们根据凝血相关基因（CRG）的表达模式对352例CAD患者进行聚类和分类，然后探索各亚组间的分子和免疫差异，以揭示CAD患者潜在的生物学特征。使用随机森林模型（RF）和最小绝对收缩和选择算子（LASSO）回归进一步确定CRG亚组之间的特征基因，并构建人工神经网络预测模型。CAD患者可分为C1和C2 CRG亚组，C1亚组高度富集于免疫相关信号通路。两个CRG亚组之间的差异表达基因（DE-CRG）主要富集于与信号转导和能量代谢相关的信号通路。随后，通过RF和LASSO确定了10个特征DE-CRG。我们使用这10个基因构建了一个新的人工神经网络模型，并在一个公共数据集上评估和验证了其诊断性能。CAD患者的不同分子亚组可能各自具有独特的基因表达模式。我们可以使用一些特征基因来识别亚组，为不同分子亚组的CAD患者的精准治疗提供理论依据。
