Table 1_Comprehensive integration of diagnostic biomarker analysis and immune cell infiltration features in sepsis via machine learning and bioinformatics techniques.docx

IntroductionSepsis, a critical medical condition resulting from an irregular immune response to infection, leads to life-threatening organ dysfunction. Despite medical advancements, the critical need for research into dependable diagnostic markers and precise therapeutic targets.
MethodsWe screened out five gene expression datasets (GSE69063, GSE236713, GSE28750, GSE65682 and GSE137340) from the Gene Expression Omnibus. First, we merged the first two datasets. We then identified differentially expressed genes (DEGs), which were subjected to KEGG and GO enrichment analyses. Following this, we integrated the DEGs with the genes from key modules as determined by Weighted Gene Co-expression Network Analysis (WGCNA), identifying 262 overlapping genes. 12 core genes were subsequently selected using three machine-learning algorithms: random forest (RF), Least Absolute Shrinkage and Selection Operator (LASSO), and Support Vector Machine-Recursive Feature Elimination (SVW-RFE). The utilization of the receiver operating characteristic curve in conjunction with the nomogram model served to authenticate the discriminatory strength and efficacy of the key genes. CIBERSORT was utilized to evaluate the inflammatory and immunological condition of sepsis. Astragalus, Salvia, and Safflower are the primary elements of Xuebijing, commonly used in the clinical treatment of sepsis. Using the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP), we identified the chemical constituents of these three herbs and their target genes.
ResultsWe found that CD40LG is not only one of the 12 core genes we identified, but also a common target of the active components quercetin, luteolin, and apigenin in these herbs. We extracted the common chemical structure of these active ingredients -flavonoids. Through docking analysis, we further validated the interaction between flavonoids and CD40LG. Lastly, blood samples were collected from healthy individuals and sepsis patients, with and without the administration of Xuebijing, for the extraction of peripheral blood mononuclear cells (PBMCs). By qPCR and WB analysis. We observed significant differences in the expression of CD40LG across the three groups. In this study, we pinpointed candidate hub genes for sepsis and constructed a nomogram for its diagnosis.
DiscussionThis research not only provides potential diagnostic evidence for peripheral blood diagnosis of sepsis but also offers insights into the pathogenesis and disease progression of sepsis.

引言
脓毒症（Sepsis）是指机体对感染产生异常免疫应答后引发的危及生命的器官功能障碍的危重病症。尽管医学技术不断进步，目前仍亟需开展可靠诊断标志物与精准治疗靶点的相关研究。

方法
本研究从基因表达综合数据库（Gene Expression Omnibus，GEO）中筛选得到5个基因表达数据集，分别为GSE69063、GSE236713、GSE28750、GSE65682及GSE137340。首先将前两个数据集进行合并，随后鉴定差异表达基因（differentially expressed genes，DEGs），并对其开展京都基因与基因组百科全书（Kyoto Encyclopedia of Genes and Genomes，KEGG）及基因本体论（Gene Ontology，GO）富集分析。后续将DEGs与加权基因共表达网络分析（Weighted Gene Co-expression Network Analysis，WGCNA）得到的关键模块基因进行整合，最终获得262个重叠基因。随后通过随机森林（random forest，RF）、最小绝对收缩和选择算子（Least Absolute Shrinkage and Selection Operator，LASSO）以及支持向量机-递归特征消除（Support Vector Machine-Recursive Feature Elimination，SVW-RFE）三种机器学习算法筛选得到12个核心基因。利用受试者工作特征曲线（receiver operating characteristic curve，ROC）结合列线图（nomogram）模型，验证核心基因的区分能力与诊断效能。采用CIBERSORT算法评估脓毒症患者的炎症与免疫状态。血必净的主要成分为黄芪、丹参与红花，其临床常用于脓毒症的治疗。本研究通过中药系统药理学数据库与分析平台（Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform，TCMSP），检索得到这三味中药的化学成分及其对应靶基因。

结果
本研究发现CD40LG不仅是筛选得到的12个核心基因之一，同时也是上述三味中药活性成分槲皮素、木犀草素及芹菜素的共同靶标。我们提取得到这些活性成分的共同化学结构——黄酮类化合物。通过分子对接分析，进一步验证了黄酮类化合物与CD40LG的相互作用。最后，本研究采集健康个体与脓毒症患者（给药/未给药血必净）的血液样本，分离外周血单个核细胞（peripheral blood mononuclear cells，PBMCs），通过实时定量聚合酶链反应（qPCR）与蛋白质印迹（Western Blot，WB）分析，观察到三组样本中CD40LG的表达水平存在显著差异。本研究明确了脓毒症的候选核心基因，并构建了用于脓毒症诊断的列线图模型。

讨论
本研究不仅为脓毒症的外周血液诊断提供了潜在的生物学标志物依据，同时也为阐明脓毒症的发病机制与疾病进展提供了新的研究思路。