通过凝血相关基因表达与免疫通路分析鉴定败血症的关键生物标志物及治疗靶点

败血症是一种由感染引起的广泛且失调的免疫反应，导致器官功能障碍，在诊断和治疗方面面临巨大挑战。本研究分析了203个与凝血相关的基因在败血症患者中的表达，探讨其在疾病中的作用。差异表达分析发现20个基因表现出显著变化。通过环状图和热图进行相关性分析，揭示这些基因间存在显著关系。基因本体（GO）和KEGG通路富集分析表明，这些基因参与免疫反应激活、凝血及免疫受体活性。疾病本体（DO）分析进一步将这些基因与自身免疫性溶血性贫血和肿瘤相关信号通路相关联。此外，CIBERSORT分析显示败血症患者免疫细胞组成存在差异，表现为中性粒细胞和单核细胞增加，静止的NK细胞、CD8 T细胞和B细胞减少。我们采用随机森林和支持向量机（SVM）等机器学习方法构建诊断模型，筛选出FCER1G和FYN作为关键生物标志物。这些标志物的表达水平和ROC曲线分析在独立验证队列中得到验证，显示出良好的诊断潜力。单细胞分析（GSE167363数据集）进一步确认了这些基因在不同细胞类型中的表达特征，FCER1G主要在单核细胞、NK细胞和血小板中表达，FYN主要在CD4+T细胞和NK细胞中表达。GSEA和ssGSEA富集分析显示，这些基因涉及肠道免疫网络、脂肪酸合成和抗原处理等关键通路。综上所述，我们的全面分析鉴定出FCER1G和FYN作为败血症的潜在生物标志物，为阐明该复杂疾病的分子机制提供了新见解，也为败血症的靶向诊断和治疗策略提供了潜在方向。

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.