Normal distribution results.

Sepsis-induced acute lung injury (ALI) is an inflammatory pulmonary condition characterized by a complex pathophysiological mechanism. The development and progression of sepsis-induced ALI are accompanied by significant oxidative damage. This study aimed to identify key oxidative stress-related genes associated with sepsis-induced ALI. Samples, including sepsis, sepsis-induced ALI, and control groups, were obtained from the Gene Expression Omnibus database. Key oxidative stress-related genes in sepsis-induced ALI were identified using Weighted Gene Co-expression Network Analysis (WGCNA), Protein-Protein Interaction (PPI) network analysis, logistic regression, and LASSO regression analysis. Functional information regarding these genes was explored through Gene Set Variation Analysis (GSVA) and Gene Set Enrichment Analysis (GSEA). A logistic regression model was constructed based on the identified hub oxidative stress-related genes. The diagnostic value of this model for sepsis-induced ALI was assessed using the receiver operating characteristic (ROC) curve. The relative abundance of 22 human immune cell types was calculated using CIBERSORT software. The expression levels of hub genes in the blood samples of sepsis-induced ALI patients were analyzed through RT-PCR and ELISA. A total of 1,055 genes associated with sepsis-induced ALI were identified via WGCNA, of which 145 genes were linked to oxidative stress. GSVA revealed that these 145 genes were significantly enriched in 79 biological pathways, while GSEA indicated a strong association with immune-related signaling pathways. Additionally, the top 20 genes were selected through PPI network analysis. The logistic regression model was constructed using VDAC1, HSPA8, SOD1, HSPA9, TXN, and SNCA. In the training set and the validation set, the AUC values of logistic regression model were 0.9091 and 0.8279, respectively, suggesting good discriminability when distinguishing normal from sepsis-induced ALI. Notably, these six genes were correlated with immune cell infiltration in sepsis-induced ALI, with HSPA8, SOD1, and HSPA9 showing downregulation in sepsis-induced ALI. In conclusion, VDAC1, HSPA8, SOD1, HSPA9, TXN, and SNCA have been identified as oxidative stress-related genes associated with sepsis-induced ALI. The logistic regression model developed using these six genes could identify patients with sepsis-induced ALI. Our findings might provide novel research strategies for the molecular therapeutic target of sepsis-induced ALI.脓毒症诱导性急性肺损伤（acute lung injury, ALI）是一类以复杂病理生理机制为特征的炎症性肺部疾病。脓毒症诱导性ALI的发生与进展均伴随显著的氧化损伤。本研究旨在筛选与脓毒症诱导性ALI相关的关键氧化应激相关基因。研究从基因表达综合数据库（Gene Expression Omnibus, GEO）中获取了脓毒症组、脓毒症诱导性ALI组及对照组的样本。采用加权基因共表达网络分析（Weighted Gene Co-expression Network Analysis, WGCNA）、蛋白质相互作用（Protein-Protein Interaction, PPI）网络分析、logistic回归及最小绝对收缩和选择算子（Least Absolute Shrinkage and Selection Operator, LASSO）回归分析，筛选脓毒症诱导性ALI的关键氧化应激相关基因。通过基因集变异分析（Gene Set Variation Analysis, GSVA）与基因集富集分析（Gene Set Enrichment Analysis, GSEA），探究上述基因的功能特征。基于筛选得到的核心氧化应激相关基因，构建logistic回归模型。采用受试者工作特征（receiver operating characteristic, ROC）曲线评估该模型对脓毒症诱导性ALI的诊断价值。采用CIBERSORT软件计算22种人类免疫细胞的相对丰度。通过逆转录聚合酶链反应（Reverse Transcription Polymerase Chain Reaction, RT-PCR）与酶联免疫吸附测定（Enzyme-Linked Immunosorbent Assay, ELISA），分析脓毒症诱导性ALI患者血液样本中核心基因的表达水平。经WGCNA分析，共筛选得到1055个与脓毒症诱导性ALI相关的基因，其中145个基因与氧化应激相关。GSVA结果显示，这145个基因显著富集于79条生物学通路；GSEA结果则表明其与免疫相关信号通路存在紧密关联。此外，通过PPI网络分析筛选得到排名前20的基因。本研究以VDAC1、HSPA8、SOD1、HSPA9、TXN及SNCA构建logistic回归模型。在训练集与验证集中，该logistic回归模型的曲线下面积（Area Under Curve, AUC）分别为0.9091与0.8279，表明其在区分正常样本与脓毒症诱导性ALI样本时具有良好的区分能力。值得注意的是，这6个基因与脓毒症诱导性ALI中的免疫细胞浸润显著相关，其中HSPA8、SOD1及HSPA9在脓毒症诱导性ALI中呈低表达。综上，本研究筛选得到VDAC1、HSPA8、SOD1、HSPA9、TXN及SNCA这6个与脓毒症诱导性ALI相关的氧化应激相关基因。基于这6个基因构建的logistic回归模型可有效识别脓毒症诱导性ALI患者。本研究结果可为脓毒症诱导性ALI的分子治疗靶点研究提供新的思路。