Sepsis, characterized by systemic inflammatory response syndrome and life-threatening organ dysfunction, remains a significant global cause of disability and death. Despite its impact, reliable biomarkers for sepsis diagnosis are yet to be identified. This study aims to investigate and identify key genes and pathways in sepsis through the analysis of multiple microarray datasets, providing potential treatment targets for future clinical trials. Two independent gene expression profiles (GSE54514 and GSE69528) were downloaded from the Gene Expression Omnibus (GEO) database. After merging and batch normalization, differentially expressed genes (DEGs) were obtained using the "limma" package. Gene Ontology (GO) and Gene Set Enrichment Analysis (GSEA) were performed using "R" software. A Protein-Protein Interaction (PPI) network was constructed using the Search Tool for the Retrieval of Interacting Genes (STRING). The top 10 hub genes were identified using Cytoscape. A Nomogram model for predicting sepsis occurrence was constructed and evaluated. Bioinformatic analysis of 210 sepsis and 91 control blood samples identified 72 DEGs. GO analyses revealed associations with immune response processes. GSEA indicated involvement in key signaling pathways. S100A12, MMP9, and PRTN3 were identified as independent risk factors for sepsis. This study unveils critical genes and pathways in sepsis through bioinformatic methods. S100A12, MMP9, and PRTN3 may play essential roles in the immune response to infection, influencing sepsis prognosis.

中文翻译：

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S100A12、MMP9 和 PRTN3 在脓毒症诊断中的关键作用：来自多个微阵列数据分析的见解

脓毒症以全身炎症反应综合征和危及生命的器官功能障碍为特征，仍然是全球残疾和死亡的重要原因。尽管有其影响，但用于脓毒症诊断的可靠生物标志物尚未确定。本研究旨在通过分析多个微阵列数据集来调查和识别脓毒症的关键基因和通路，为未来的临床试验提供潜在的治疗靶点。从基因表达综合 (GEO) 数据库下载两个独立的基因表达谱（GSE54514 和 GSE69528）。合并和批量归一化后，使用“limma”包获得差异表达基因（DEG）。使用“R”软件进行基因本体论（GO）和基因集富集分析（GSEA）。使用相互作用基因检索搜索工具 (STRING) 构建了蛋白质-蛋白质相互作用 (PPI) 网络。使用 Cytoscape 鉴定了前 10 个中心基因。构建并评估了预测脓毒症发生的列线图模型。对 210 份脓毒症血液样本和 91 份对照血液样本进行生物信息分析，确定了 72 个 DEG。 GO 分析揭示了与免疫反应过程的关联。 GSEA 表明参与关键信号通路。 S100A12、MMP9 和 PRTN3 被确定为脓毒症的独立危险因素。这项研究通过生物信息学方法揭示了脓毒症的关键基因和途径。 S100A12、MMP9 和 PRTN3 可能在针对感染的免疫反应中发挥重要作用，影响脓毒症预后。