Table_1_Integrated analysis of multi-omics data reveals T cell exhaustion in sepsis.xlsx

BackgroundSepsis is a heterogeneous disease, therefore the single-gene-based biomarker is not sufficient to fully understand the disease. Higher-level biomarkers need to be explored to identify important pathways related to sepsis and evaluate their clinical significance.
MethodsGene Set Enrichment Analysis (GSEA) was used to analyze the sepsis transcriptome to obtain the pathway-level expression. Limma was used to identify differentially expressed pathways. Tumor IMmune Estimation Resource (TIMER) was applied to estimate immune cell abundance. The Spearman correlation coefficient was used to find the relationships between pathways and immune cell abundance. Methylation and single-cell transcriptome data were also employed to identify important pathway genes. Log-rank test was performed to test the prognostic significance of pathways for patient survival probability. DSigDB was used to mine candidate drugs based on pathways. PyMol was used for 3-D structure visualization. LigPlot was used to plot the 2-D pose view for receptor-ligand interaction.
ResultsEighty-four KEGG pathways were differentially expressed in sepsis patients compared to healthy controls. Of those, 10 pathways were associated with 28-day survival. Some pathways were significantly correlated with immune cell abundance and five pathways could be used to distinguish between systemic inflammatory response syndrome (SIRS), bacterial sepsis, and viral sepsis with Area Under the Curve (AUC) above 0.80. Seven related drugs were screened using survival-related pathways.
ConclusionSepsis-related pathways can be utilized for disease subtyping, diagnosis, prognosis, and drug screening.

背景 脓毒症（Sepsis）是一种异质性疾病，仅基于单基因的生物标志物不足以全面阐明该疾病。亟需探索更高层级的生物标志物，以明确与脓毒症相关的关键通路并评估其临床意义。
方法 采用基因集富集分析（Gene Set Enrichment Analysis, GSEA）分析脓毒症转录组数据，以获取通路层面的基因表达谱；使用Limma筛选差异表达通路；借助肿瘤免疫估算资源（Tumor Immune Estimation Resource, TIMER）评估免疫细胞丰度；采用斯皮尔曼相关系数分析通路与免疫细胞丰度之间的关联；整合甲基化数据与单细胞转录组数据以筛选关键通路基因；通过对数秩检验（Log-rank test）检验通路对患者生存概率的预后价值；利用DSigDB数据库基于通路挖掘候选药物；使用PyMol进行三维结构可视化；采用LigPlot绘制受体-配体相互作用的二维构象视图。
结果 与健康对照组相比，脓毒症患者中共存在84条差异表达的KEGG通路。其中10条通路与28天生存率相关。部分通路与免疫细胞丰度呈显著相关，另有5条通路可用于区分全身炎症反应综合征（Systemic Inflammatory Response Syndrome, SIRS）、细菌性脓毒症与病毒性脓毒症，其曲线下面积（Area Under the Curve, AUC）均高于0.80。基于生存相关通路共筛选出7种候选药物。
结论 脓毒症相关通路可用于疾病分型、诊断、预后评估及药物筛选。