通过与凝血相关的基因表达和免疫途径分析鉴定败血症中关键生物标志物和治疗靶标

败血症的特征是对感染导致器官功能障碍的广泛和失调的免疫反应，在诊断和治疗中面临重大挑战。在这项研究中，我们研究了败血症患者的203个与凝血相关的基因，以探索其在该疾病中的作用。通过差异基因表达分析，我们鉴定出具有改变表达模式的20个基因。随后通过Circos图和热图可视化的随后相关分析显示了这些基因之间的显着关系。基因和基因组（KEGG）途径富集分析的基因本体论（GO）和京都百科全书表明，这些基因参与免疫反应激活，凝结和免疫受体活性。疾病本体论（DO）富集分析进一步将这些基因与自身免疫性溶血性贫血和与肿瘤相关的信号通路联系起来。此外，Cibersort分析强调了败血症患者免疫细胞组成的差异，揭示了中性粒细胞和单核细胞的增加，并且无活性NK细胞，CD8 T细胞和B细胞的降低。我们采用了包括随机森林和SVM在内的机器学习技术来构建诊断模型，将FCER1G和FYN识别为关键生物标志物。这些生物标志物通过独立验证队列中的表达水平和ROC曲线分析得到了验证，表现出强大的诊断潜力。来自GSE167363数据集的单细胞分析进一步证实了这些基因在各种细胞类型中的不同表达谱，FCER1G主要在单核细胞，NK细胞和血小板中表达，而CD4+ T细胞和NK细胞中的FYN。通过GSEA和SSGSEA进行的富集分析表明，这些基因参与关键途径，包括肠道免疫网络，脂肪酸合成和抗原加工。总之，我们的全面分析将FCER1G和FYN确定为败血症的有前途的生物标志物，为这种复杂条件的分子机制提供了宝贵的见解。这些发现为败血症管理中有针对性的诊断和治疗策略的发展提供了新的途径。

Sepsis, characterized by a widespread and dysregulated immune response to infection leading to organ dysfunction, presents significant challenges in diagnosis and treatment. In this study, we investigated 203 coagulation-related genes in sepsis patients to explore their roles in the disease. Through differential gene expression analysis, we identified 20 genes with altered expression patterns. Subsequent correlation analysis, visualized through circos plots and heatmaps, revealed significant relationships among these genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses indicated that these genes are involved in immune response activation, coagulation, and immune receptor activity. Disease Ontology (DO) enrichment analysis further linked these genes to autoimmune hemolytic anemia and tumor-related signaling pathways. Additionally, the CIBERSORT analysis highlighted differences in immune cell composition in sepsis patients, revealing an increase in neutrophils and monocytes and a decrease in inactive NK cells, CD8 T cells, and B cells. We employed machine learning techniques, including random forest and SVM, to construct a diagnostic model, identifying FCER1G and FYN as key biomarkers. These biomarkers were validated through their expression levels and ROC curve analysis in an independent validation cohort, demonstrating strong diagnostic potential. Single-cell analysis from the GSE167363 dataset further confirmed the distinct expression profiles of these genes across various cell types, with FCER1G predominantly expressed in monocytes, NK cells, and platelets, and FYN in CD4+ T cells and NK cells. Enrichment analysis via GSEA and ssGSEA revealed that these genes are involved in critical pathways, including intestinal immune networks, fatty acid synthesis, and antigen processing. In conclusion, our comprehensive analysis identifies FCER1G and FYN as promising biomarkers for sepsis, providing valuable insights into the molecular mechanisms of this complex condition. These findings offer new avenues for the development of targeted diagnostic and therapeutic strategies in sepsis management.