Wang Hao, Hu Yingchun, Hu Li
School of Clinical Medicine, Shandong Second Medical University, Weifang, People's Republic of China.
Department of Emergency Medicine, The Affiliated Hospital of Southwest Medical University, Luzhou, People's Republic of China.
Medicine (Baltimore). 2025 Apr 18;104(16):e42184. doi: 10.1097/MD.0000000000042184.
Sepsis, a complex inflammatory condition with high mortality rates, lacks effective treatments. This study explores the therapeutic mechanisms of Calculus Bovis in sepsis using network pharmacology and RNA sequencing.
Sepsis data from the China National GeneBank Database were analyzed for differentially expressed genes (FC ≥ 2, FDR < 0.05). Active components of Calculus Bovis were identified via the HERB and BATMAN-TCM databases, with target interactions assessed through protein-protein interaction (PPI) networks. GO and KEGG analyses identified pathway enrichments (P ≤ .01). Survival analysis using the GSE65682 database evaluated prognosis-related genes (P < .05). Four machine learning models (XGBoost, SVM, Decision Tree, KNN) were constructed to assess diagnostic potential, with AUC values evaluating accuracy. Immunofluorescence and single-cell RNA sequencing localized key genes, while molecular docking and molecular dynamics simulations (MD) assessed binding affinities and stability of Calculus Bovis compounds with target proteins.
We identified 593 targets for Calculus Bovis and 4329 sepsis-related genes, with 149 overlapping. Key genes ADAM17, CASP1, CD81, and MGMT were linked to improved prognosis (P < .05) and involved in inflammatory responses and pyroptosis (P ≤ .01). The XGBoost model achieved high diagnostic accuracy (AUC: training = 1.000, test = 0.964). Molecular docking showed strong binding (energy < -6.0 kcal/mol), and MD indicated stable interactions, particularly with ADAM17 and CD81.
This study highlights the potential of Calculus Bovis in sepsis treatment, identifying key genes as therapeutic targets.
脓毒症是一种死亡率高的复杂炎症性疾病，缺乏有效的治疗方法。本研究运用网络药理学和RNA测序技术探索牛黄在脓毒症中的治疗机制。
分析中国国家基因库数据库中的脓毒症数据，以确定差异表达基因（FC≥2，FDR<0.05）。通过HERB和BATMAN-TCM数据库鉴定牛黄的活性成分，并通过蛋白质-蛋白质相互作用（PPI）网络评估靶点相互作用。基因本体（GO）和京都基因与基因组百科全书（KEGG）分析确定通路富集（P≤0.01）。使用GSE65682数据库进行生存分析，评估与预后相关的基因（P<0.05）。构建四个机器学习模型（XGBoost、支持向量机、决策树、K近邻算法）以评估诊断潜力，通过曲线下面积（AUC）值评估准确性。免疫荧光和单细胞RNA测序对关键基因进行定位，而分子对接和分子动力学模拟（MD）评估牛黄化合物与靶蛋白的结合亲和力和稳定性。
我们确定了593个牛黄靶点和4329个脓毒症相关基因，其中149个重叠。关键基因ADAM17、半胱天冬酶1（CASP1）、CD81和O6-甲基鸟嘌呤-DNA甲基转移酶（MGMT）与预后改善相关（P<0.05），并参与炎症反应和细胞焦亡（P≤0.01）。XGBoost模型具有较高的诊断准确性（AUC：训练集=1.000，测试集=0.964）。分子对接显示出较强的结合力（能量<-6.0千卡/摩尔），MD表明相互作用稳定，特别是与ADAM17和CD81的相互作用。
本研究突出了牛黄在脓毒症治疗中的潜力，确定了关键基因作为治疗靶点。
