脓毒症的特点是对感染的广泛且失调的免疫反应，导致器官功能障碍，给诊断和治疗带来了巨大的挑战。在这项研究中，我们调查了脓毒症患者的 203 个凝血相关基因，以探讨它们在该疾病中的作用。通过差异基因表达分析，我们鉴定了 20 个表达模式发生改变的基因。随后的相关分析，通过 circos 图和热图可视化，揭示了这些基因之间的显着关系。基因本体论（GO）和京都基因与基因组百科全书（KEGG）通路富集分析表明这些基因参与免疫反应激活、凝血和免疫受体活性。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肿瘤相关信号通路联系起来。此外，CIBERSORT 分析强调了脓毒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以及非活性 NK 细胞、CD8 T 细胞和 B 细胞的减少。我们采用机器学习技术（包括随机森林和 SVM）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 曲线分析进行了验证，表现出强大的诊断潜力。 GSE167363 数据集的单细胞分析进一步证实了这些基因在不同细胞类型中的不同表达谱，其中 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。通过 GSEA 和 ssGSEA 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，我们的综合分析将 FCER1G 和 FYN 确定为脓毒症有前途的生物标志物，为了解这种复杂病症的分子机制提供了宝贵的见解。这些发现为脓毒症管理的靶向诊断和治疗策略的开发提供了新途径。版权所有 © 2024 Ge、Deng、Zhou、Hu、Zhang 和 Cheng。

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.Copyright © 2024 Ge, Deng, Zhou, Hu, Zhang and Zheng.