Ding Ding, Min Zhang, Zhen Li, Zhengxiang Liu, Nian Liu
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Immune cell infiltration and immune-related pathway activities were analyzed using the ssGSEA algorithm. GSVA and GSEA indicated significant enrichment of oxidative stress-related pathways in sepsis patients compared to controls.</p><p><strong>Results: </strong>Differential expression analysis identified 371 upregulated and 304 downregulated genes in sepsis, with 34 genes linked to oxidative stress. LASSO and Random Forest analyses highlighted key diagnostic genes (GBA and MGST1), validated in independent datasets (GSE13904) with high diagnostic accuracy (AUC: GBA = 0.924, MGST1 = 0.857). Unsupervised clustering revealed two distinct sepsis subtypes with differential immune cell infiltration and pathway activities: Subtype 1 showed higher T cell and TFH infiltration, while Subtype 2 exhibited increased macrophage infiltration. Functional enrichment and GSEA identified key metabolic, oxidative stress, and immune pathways that were enriched in Subtype 2.</p><p><strong>Conclusion: </strong>Our comprehensive bioinformatics analysis unveils significant oxidative stress-related molecular heterogeneity in sepsis, identifying potential diagnostic biomarkers and therapeutic targets. Personalized medicine approaches targeting specific oxidative stress pathways and immune responses could enhance sepsis management and patient outcomes.</p>","PeriodicalId":23177,"journal":{"name":"Toxicology Mechanisms and Methods","volume":" ","pages":"1-13"},"PeriodicalIF":3.2000,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Molecular heterogeneity in pediatric sepsis: identification of oxidative stress-related subtypes and diagnostic biomarkers through integrated bioinformatics analysis.\",\"authors\":\"Ding Ding, Min Zhang, Zhen Li, Zhengxiang Liu, Nian Liu\",\"doi\":\"10.1080/15376516.2025.2466577\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>Pediatric sepsis is a life-threatening condition characterized by a dysregulated immune response to infection, often involving heightened oxidative stress. 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引用次数: 0
摘要
背景:儿童败血症是一种危及生命的疾病,其特征是对感染的免疫反应失调,通常涉及氧化应激升高。了解脓毒症的分子异质性可以为潜在的治疗靶点和诊断生物标志物提供见解。方法:采用机器学习方法识别诊断性生物标志物。进行无监督聚类以确定不同的脓毒症亚型。我们进行了一项综合分析,结合基因集变异分析(GSVA)、基因集富集分析(GSEA)、差异基因表达和功能富集来研究脓毒症患者的氧化应激相关亚群。采用ssGSEA算法分析免疫细胞浸润和免疫相关通路活性。与对照组相比,GSVA和GSEA显示脓毒症患者中氧化应激相关通路显著富集。结果:差异表达分析鉴定出脓毒症中371个基因表达上调,304个基因表达下调,其中34个基因与氧化应激有关。LASSO和Random Forest分析突出了关键的诊断基因(GBA和MGST1),在独立数据集(GSE13904)中得到验证,具有很高的诊断准确性(AUC: GBA = 0.924, MGST1 = 0.857)。无监督聚类揭示了两种不同的脓毒症亚型,它们具有不同的免疫细胞浸润和途径活性:亚型1表现为更高的T细胞和TFH浸润,而亚型2表现为更高的巨噬细胞浸润。功能富集和GSEA鉴定了亚型2中富集的关键代谢、氧化应激和免疫途径。结论:我们的综合生物信息学分析揭示了脓毒症中氧化应激相关的分子异质性,确定了潜在的诊断生物标志物和治疗靶点。针对特定氧化应激途径和免疫反应的个性化医学方法可以增强败血症的管理和患者的预后。
Molecular heterogeneity in pediatric sepsis: identification of oxidative stress-related subtypes and diagnostic biomarkers through integrated bioinformatics analysis.
Background: Pediatric sepsis is a life-threatening condition characterized by a dysregulated immune response to infection, often involving heightened oxidative stress. Understanding the molecular heterogeneity of sepsis can provide insights into potential therapeutic targets and diagnostic biomarkers.
Methods: Machine learning approaches were employed to identify diagnostic biomarkers. Unsupervised clustering was performed to identify distinct sepsis subtypes. We conducted an integrative analysis combining Gene Set Variation Analysis (GSVA), Gene Set Enrichment Analysis (GSEA), differential gene expression, and functional enrichment to study oxidative stress-related subgroups in sepsis patients. Immune cell infiltration and immune-related pathway activities were analyzed using the ssGSEA algorithm. GSVA and GSEA indicated significant enrichment of oxidative stress-related pathways in sepsis patients compared to controls.
Results: Differential expression analysis identified 371 upregulated and 304 downregulated genes in sepsis, with 34 genes linked to oxidative stress. LASSO and Random Forest analyses highlighted key diagnostic genes (GBA and MGST1), validated in independent datasets (GSE13904) with high diagnostic accuracy (AUC: GBA = 0.924, MGST1 = 0.857). Unsupervised clustering revealed two distinct sepsis subtypes with differential immune cell infiltration and pathway activities: Subtype 1 showed higher T cell and TFH infiltration, while Subtype 2 exhibited increased macrophage infiltration. Functional enrichment and GSEA identified key metabolic, oxidative stress, and immune pathways that were enriched in Subtype 2.
Conclusion: Our comprehensive bioinformatics analysis unveils significant oxidative stress-related molecular heterogeneity in sepsis, identifying potential diagnostic biomarkers and therapeutic targets. Personalized medicine approaches targeting specific oxidative stress pathways and immune responses could enhance sepsis management and patient outcomes.
期刊介绍:
Toxicology Mechanisms and Methods is a peer-reviewed journal whose aim is twofold. Firstly, the journal contains original research on subjects dealing with the mechanisms by which foreign chemicals cause toxic tissue injury. Chemical substances of interest include industrial compounds, environmental pollutants, hazardous wastes, drugs, pesticides, and chemical warfare agents. The scope of the journal spans from molecular and cellular mechanisms of action to the consideration of mechanistic evidence in establishing regulatory policy.
Secondly, the journal addresses aspects of the development, validation, and application of new and existing laboratory methods, techniques, and equipment.