综合临床分析和转录组分析揭示脓毒症中凝血过程的潜在关键基因及其调控机制
Combined Clinical Analysis and Transcriptomic Analysis Reveals the Potential Hub Genes and Regulatory Mechanisms of Coagulation in Sepsis
DOI: 10.12677/acm.2026.1652155, PDF,    科研立项经费支持
作者: 吴静澜, 赵晶晶*：安徽医科大学，安徽 合肥；合肥市第二??医院重症医学科，安徽 合肥；詹 圆：合肥市第二??医院重症医学科，安徽 合肥
关键词: 脓毒症；MIMIC-IV数据库；Bulk RNA-Seq Data；Single-Cell RNA-Seq Data；凝血；Sepsis； MIMIC-IV Database； Bulk RNA-Seq Data； Single-Cell RNA-Seq Data； Coagulation
摘要: 背景：通过临床分析，我们发现血液凝固与败血症密切相关。然而，凝血与败血症之间的具体机制尚不明确。因此，本研究的目的是探究与败血症相关的凝血的潜在靶点及调控机制。方法：脓毒症的临床数据来自MIMIC-IV数据库和我们医院的数据库。脓毒症的测序数据来自GEO数据库(GSE57065、GSE134347和GSE167363)。通过limma算法的差异分析获得差异基因。凝血相关基因来自MSigDB数据库。使用维恩分析获得与脓毒症和凝血相关的交集基因。使用机器学习算法筛选关键靶点。使用逻辑回归分析构建诊断模型。使用免疫浸润分析获取与脓毒症相关的免疫细胞。使用单细胞测序分析进一步研究脓毒症中免疫细胞之间的联系，并评估关键基因的表达模式。结果：我们筛选出了58个与脓毒症凝血相关的交集基因。通过一系列生物信息学分析，构建了与脓毒症凝血相关的诊断模型。该模型包含两个核心基因，其中DGKA在脓毒症中表达，而GP9在脓毒症中的表达水平较高。该模型对脓毒症的诊断效果极佳(AUC > 0.8)。单基因富集分析表明，DGKA和GP9都与T细胞受体信号通路密切相关。免疫浸润分析表明，脓毒症与免疫细胞密切相关。单细胞结果表明，中性粒细胞与其他细胞的连接最为活跃。结论：DGKA和GP9与脓毒症凝血密切相关，并且已经构建出了出色的诊断模型，这些模型可用于未来的诊断和治疗研究。
Abstract: Background: Through clinical analysis, we have found that blood coagulation is closely related to sepsis. However, the specific mechanism between coagulation and sepsis remains unclear. Therefore, the aim of this study was to investigate the potential targets and regulatory mechanisms of coagulation related to sepsis. Methods: Clinical data of sepsis were obtained from the MIMIC-IV database and the database of our hospital. The sequencing data of sepsis were obtained from the GEO (GSE57065, GSE134347, and GSE167363) dataset. Differential genes were obtained using limma differential analysis. Coagulation-related genes were obtained from the MSigDB database. Venn analysis was used to obtain the intersection genes related to sepsis and coagulation. Machine learning algorithms were used to screen hub targets. Logistic regression analysis was used to construct the diagnostic model. Immune infiltration analysis was used to obtain immune cells associated with sepsis. Single-cell sequencing analysis was used to further study the connections between immune cells in sepsis and evaluate the expression patterns of hub genes. Results: We screened out 58 intersection genes related to sepsis coagulation. Through a series of bioinformatics analyses, a diagnostic model related to sepsis coagulation was constructed. The model contains two hub genes, among which DGKA was expressed in sepsis and GP9 was highly expressed in sepsis. The model had an excellent diagnostic effect on sepsis (AUC > 0.8). Single-gene enrichment analysis indicated that both DGKA and GP9 were closely related to the T cell receptor signaling pathway (TCR). Immune infiltration analysis indicates that sepsis was closely related to immune cells. Single-cell results indicated that Neutrophils had the most active connections with other cells. Conclusions: DGKA and GP9 were closely related to sepsis coagulation, and excellent diagnostic model had been constructed, which can be used for future diagnostic and therapeutic research.
文章引用：吴静澜, 赵晶晶, 詹圆. 综合临床分析和转录组分析揭示脓毒症中凝血过程的潜在关键基因及其调控机制[J]. 临床医学进展, 2026, 16(5): 3333-3350. https://doi.org/10.12677/acm.2026.1652155

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