Tao Linfeng, Zhu Yue, Wu Lifang, Liu Jun
Gusu School of Nanjing Medical University, Department of Critical Care Medicine, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, Suzhou Clinical Medical Center of Critical Care Medicine, Suzhou, China.
Department of Breast and Thyroid Surgery, The Affiliated Suzhou Hospital of Nanjing Medical University, Gusu School, Nanjing Medical University, Suzhou, China.
Front Mol Biosci. 2024 Jan 8;10:1322221. doi: 10.3389/fmolb.2023.1322221. eCollection 2023.
Sepsis is a pathological state resulting from dysregulated immune response in host during severe infection, leading to persistent organ dysfunction and ultimately death. Senescence-associated genes (SAGs) have manifested their potential in controlling the proliferation and dissemination of a variety of diseases. Nevertheless, the correlation between sepsis and SAGs remains obscure and requires further investigation. Two RNA expression datasets (GSE28750 and GSE57065) specifically related to sepsis were employed to filter hub SAGs, based on which a diagnostic model predictive of the incidence of sepsis was developed. The association between the expression of the SAGs identified and immune-related modules was analyzed employing Cell-type Identification By Estimating Relative Subsets Of RNA Transcripts (CIBERSORT) and Microenvironment Cell Populations-counter (MCP-counter) analysis. The identified genes in each cohort were clustered by unsupervised agreement clustering analysis and weighted gene correlation network analysis (WGCNA). A diagnostic model for sepsis established based on hub genes (IGFBP7, GMFG, IL10, IL18, ETS2, HGF, CD55, and MMP9) exhibited a strong clinical reliability (AUC = 0.989). Sepsis patients were randomly assigned and classified by WGCNA into two clusters with distinct immune statuses. Analysis on the single-cell RNA sequencing (scRNA-seq) data revealed high scores of SAGs in the natural killer (NK) cells of the sepsis cohort than the healthy cohort. These findings suggested a close association between SAGs and sepsis alterations. The identified hub genes had potential to serve as a viable diagnostic marker for sepsis.
脓毒症是宿主在严重感染期间免疫反应失调导致的一种病理状态，可导致持续性器官功能障碍并最终死亡。衰老相关基因（SAGs）已在控制多种疾病的增殖和传播方面显示出其潜力。然而，脓毒症与SAGs之间的相关性仍不明确，需要进一步研究。利用两个与脓毒症特异性相关的RNA表达数据集（GSE28750和GSE57065）筛选核心SAGs，并在此基础上建立了预测脓毒症发病率的诊断模型。采用通过估计RNA转录本相对子集进行细胞类型鉴定（CIBERSORT）和微环境细胞群体计数器（MCP-counter）分析，分析已鉴定的SAGs表达与免疫相关模块之间的关联。通过无监督一致性聚类分析和加权基因共表达网络分析（WGCNA）对每个队列中鉴定出的基因进行聚类。基于核心基因（胰岛素样生长因子结合蛋白7、胶质纤维酸性蛋白、白细胞介素10、白细胞介素18、ETS2、肝细胞生长因子、CD55和基质金属蛋白酶9）建立的脓毒症诊断模型具有很强的临床可靠性（曲线下面积=0.989）。脓毒症患者通过WGCNA随机分配并分为具有不同免疫状态的两个集群。对单细胞RNA测序（scRNA-seq）数据的分析显示，脓毒症队列的自然杀伤（NK）细胞中SAGs的得分高于健康队列。这些发现表明SAGs与脓毒症改变之间存在密切关联。已鉴定的核心基因有潜力作为脓毒症的可行诊断标志物。
