Cheng Yanwei, Peng Haoran, Chen Qiao, Xu Lijun, Qin Lijie
Department of Emergency, Henan Provincial People's Hospital, People's Hospital of Zhengzhou University, People's Hospital of Henan University, Zhengzhou, China.
Department of Neurology, People's Hospital of Henan University, Henan Provincial People's Hospital, Zhengzhou, Henan, China.
Front Pharmacol. 2025 Mar 31;16:1576467. doi: 10.3389/fphar.2025.1576467. eCollection 2025.
Sepsis is a life-threatening condition characterized by a dysregulated host response to infection, resulting in high mortality rates and complex clinical management. This study leverages transcriptomics and machine learning (ML) to identify critical biomarkers and therapeutic targets in sepsis. Analyzing microarray data from the Gene Expression Omnibus (GEO) datasets GSE28750, GSE26440, GSE13205, and GSE9960, we discovered three pivotal biomarkers that (bone marrow tyrosine kinase gene on chromosome X), growth factor receptor bound protein 10), and (growth arrest and DNA damage inducible alpha), exhibiting exceptional diagnostic accuracy (AUC >0.9). Functional enrichment analyses revealed that these genes play key roles in reactive oxygen species metabolism and immune response regulation. Specifically, was positively correlated with eosinophils and inversely associated with activated NK cells, CD8 T cells, and activated memory CD4 T cells. showed positive correlations with eosinophils, mast cells, and neutrophils, while was linked to eosinophils and M2 macrophages. Additionally, we constructed a comprehensive mRNA-miRNA-lncRNA regulatory network, identifying key interactions that may drive sepsis pathogenesis. Molecular docking and dynamics simulations validated Bendroflumethiazide, Cianidanol, and Hexamidine as promising therapeutic agents targeting these biomarkers. In conclusion, this integrated approach provides profound insights into the molecular mechanisms underlying sepsis, pinpointing , , and as pivotal biomarkers and therapeutic targets. These findings significantly enhance our understanding of sepsis pathophysiology and lay the groundwork for developing personalized diagnostic and therapeutic strategies aimed at improving patient outcomes.
脓毒症是一种危及生命的病症，其特征是宿主对感染的反应失调，导致高死亡率和复杂的临床管理。本研究利用转录组学和机器学习（ML）来识别脓毒症中的关键生物标志物和治疗靶点。通过分析来自基因表达综合数据库（GEO）数据集GSE28750、GSE26440、GSE13205和GSE9960的微阵列数据，我们发现了三个关键生物标志物，即（X染色体上的骨髓酪氨酸激酶基因）、生长因子受体结合蛋白10和（生长停滞和DNA损伤诱导α），它们具有卓越的诊断准确性（曲线下面积>0.9）。功能富集分析表明，这些基因在活性氧代谢和免疫反应调节中起关键作用。具体而言，与嗜酸性粒细胞呈正相关，与活化的自然杀伤细胞、CD8 T细胞和活化的记忆CD4 T细胞呈负相关。与嗜酸性粒细胞、肥大细胞和中性粒细胞呈正相关，而与嗜酸性粒细胞和M2巨噬细胞有关。此外，我们构建了一个全面的mRNA-miRNA-lncRNA调控网络，确定了可能驱动脓毒症发病机制的关键相互作用。分子对接和动力学模拟验证了苄氟噻嗪、西尼达醇和己脒定是针对这些生物标志物的有前景的治疗药物。总之，这种综合方法为脓毒症的分子机制提供了深刻见解，确定了、和为关键生物标志物和治疗靶点。这些发现显著增强了我们对脓毒症病理生理学的理解，并为制定旨在改善患者预后的个性化诊断和治疗策略奠定了基础。
