Fu Xue, Dong Jiawei, Yang Jian, Zhang Xiaotian, Wang Sen, Cai Shangkun, Zhang Yiwei, Zhang Meng
Department of Emergency, Hebei Medical University Third Hospital, Shijiazhuang, China.
Department of Thoracic Surgery, Hebei Medical University Third Hospital, Shijiazhuang, China.
PLoS One. 2025 Jul 22;20(7):e0327945. doi: 10.1371/journal.pone.0327945. eCollection 2025.
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.
脓毒症诱导的急性肺损伤（ALI）是一种具有复杂病理生理机制的炎症性肺部疾病。脓毒症诱导的ALI的发生和发展伴随着显著的氧化损伤。本研究旨在鉴定与脓毒症诱导的ALI相关的关键氧化应激相关基因。从基因表达综合数据库中获取样本，包括脓毒症组、脓毒症诱导的ALI组和对照组。使用加权基因共表达网络分析（WGCNA）、蛋白质-蛋白质相互作用（PPI）网络分析、逻辑回归和套索回归分析来鉴定脓毒症诱导的ALI中关键的氧化应激相关基因。通过基因集变异分析（GSVA）和基因集富集分析（GSEA）探索这些基因的功能信息。基于鉴定出的核心氧化应激相关基因构建逻辑回归模型。使用受试者工作特征（ROC）曲线评估该模型对脓毒症诱导的ALI的诊断价值。使用CIBERSORT软件计算22种人类免疫细胞类型的相对丰度。通过RT-PCR和ELISA分析脓毒症诱导的ALI患者血样中核心基因的表达水平。通过WGCNA共鉴定出1055个与脓毒症诱导的ALI相关的基因，其中145个基因与氧化应激有关。GSVA显示这145个基因在79条生物学途径中显著富集，而GSEA表明与免疫相关信号通路有很强的关联。此外，通过PPI网络分析选出前20个基因。使用电压依赖性阴离子通道1（VDAC1）、热休克蛋白家族A成员8（HSPA8）、超氧化物歧化酶1（SOD1）、热休克蛋白家族A成员9（HSPA9）、硫氧还蛋白（TXN）和突触核蛋白（SNCA）构建逻辑回归模型。在训练集和验证集中，逻辑回归模型的AUC值分别为0.9091和0.8279，表明在区分正常与脓毒症诱导的ALI时具有良好的辨别能力。值得注意的是，这六个基因与脓毒症诱导的ALI中的免疫细胞浸润相关，其中HSPA8、SOD1和HSPA9在脓毒症诱导的ALI中表达下调。总之，VDAC1、HSPA8、SOD1、HSPA9、TXN和SNCA已被鉴定为与脓毒症诱导的ALI相关的氧化应激相关基因。使用这六个基因开发的逻辑回归模型可以识别脓毒症诱导的ALI患者。我们的研究结果可能为脓毒症诱导的ALI的分子治疗靶点提供新的研究策略。
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