Sepsis is a major cause of mortality in patients, and ARDS is one of the most common outcomes. The pathophysiology of acute respiratory distress syndrome (ARDS) caused by sepsis is significantly impacted by genes related to ferroptosis. In this study, Weighted gene co-expression network analysis (WGCNA), protein-protein interaction (PPI) networks, functional enrichment analysis, and machine learning were employed to identify characterized genes and to construct receiver operating characteristic (ROC) curves. Additionally, DNA methylation levels were quantified and single-cell analysis was conducted. To validate the alterations in the expression of Lipocalin-2 (LCN2) and ferroptosis-related proteins in the in vitro model, Western blotting was carried out, and the changes in intracellular ROS and Fe levels were detected. A combination of eight machine learning algorithms, including RFE, LASSO, RandomForest, SVM-RFE, GBDT, Bagging, XGBoost, and Boruta, were used with a machine learning model to highlight the significance of LCN2 as a key gene in sepsis-induced ARDS. Analysis of immune cell infiltration showed a positive correlation between neutrophils and LCN2. In a cell model induced by LPS, it was found that Fer-1, a ferroptosis inhibitor, was able to reverse the expression of LCN2. Knocking down LCN2 in BEAS-2B cells reversed the LPS-induced lipid peroxidation, Fe2+ levels, ACSL4, and GPX4 levels, indicating that LCN2, a FRG, plays a crucial role in mediating ferroptosis. Upon establishing an FRG model for individuals with sepsis-induced ARDS, we determined that LCN2 could be a dependable marker for predicting survival in these patients. This finding provides a basis for more accurate ARDS diagnosis and the exploration of innovative treatment options.

中文翻译：

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基于机器学习探索铁死亡相关基因脂质运载蛋白2作为败血症诱发的急性呼吸窘迫综合征的潜在生物标志物

脓毒症是患者死亡的主要原因，ARDS 是最常见的结果之一。脓毒症引起的急性呼吸窘迫综合征（ARDS）的病理生理学受到铁死亡相关基因的显着影响。在本研究中，采用加权基因共表达网络分析（WGCNA）、蛋白质-蛋白质相互作用（PPI）网络、功能富集分析和机器学习来识别特征基因并构建受试者工作特征（ROC）曲线。此外，还对 DNA 甲基化水平进行了定量并进行了单细胞分析。为了验证体外模型中Lipocalin-2（LCN2）和铁死亡相关蛋白表达的变化，进行了Western blotting，并检测了细胞内ROS和Fe水平的变化。将 RFE、LASSO、RandomForest、SVM-RFE、GBDT、Bagging、XGBoost 和 Boruta 等八种机器学习算法与机器学习模型结合使用，以强调 LCN2 作为脓毒症诱发 ARDS 关键基因的重要性。免疫细胞浸润分析显示中性粒细胞与 LCN2 呈正相关。在LPS诱导的细胞模型中，发现铁死亡抑制剂Fer-1能够逆转LCN2的表达。敲低 BEAS-2B 细胞中的 LCN2 可逆转 LPS 诱导的脂质过氧化、Fe2+ 水平、ACSL4 和 GPX4 水平，表明 LCN2（一种 FRG）在介导铁死亡中发挥着至关重要的作用。在为脓毒症引起的 ARDS 患者建立 FRG 模型后，我们确定 LCN2 可能是预测这些患者生存的可靠标志物。这一发现为更准确的ARDS诊断和探索创新治疗方案提供了基础。