Heterogeneity of Neutrophils and Immunological Function in Neonatal Sepsis: Analysis of Molecular Subtypes Based on Hypoxia-Glycolysis-Lactylation.

IF 4.4 3区 医学 Q2 CELL BIOLOGY
Mediators of Inflammation Pub Date : 2025-03-26 eCollection Date: 2025-01-01 DOI:10.1155/mi/5790261
Huabin Wang, Ru Yang, Nan Chen, Xiang Li
{"title":"Heterogeneity of Neutrophils and Immunological Function in Neonatal Sepsis: Analysis of Molecular Subtypes Based on Hypoxia-Glycolysis-Lactylation.","authors":"Huabin Wang, Ru Yang, Nan Chen, Xiang Li","doi":"10.1155/mi/5790261","DOIUrl":null,"url":null,"abstract":"<p><p><b>Objective:</b> Hypoxia-glycolysis-lactylation (HGL) may play a crucial role in neonatal sepsis (NS). This study aims to identify HGL marker genes in NS and explore immune microenvironment among NS subtypes. <b>Materials and Methods:</b> The gene expression dataset GSE69686, comprising 64 NS cases and 85 controls, was selected for analysis. Based on the screened HGL-related marker genes, diagnostic prediction models were constructed using nine machine learning algorithms, and molecular subtypes of NS were identified through consensus clustering. Subsequently, the heterogeneity of biological functions and immune cell infiltration among the different subtypes was analyzed. Finally, the marker genes and lactylation were validated using the GSE25504 dataset, clinical samples, and mouse neutrophil, respectively. <b>Results:</b> MERTK, HK3, PGK1, and STAT3 were identified and validated as marker genes, and the diagnostic prediction model for NS constructed using the support vector machine (SVM) algorithm exhibited optimal predictive performance. Based on gene expression patterns, two distinct NS subtypes were identified. Functional enrichment analysis highlighted significant immune-related pathways, while immune infiltration analysis revealed differences in neutrophil proportions between the subtypes. Furthermore, the expression levels of marker genes were positively correlated with neutrophil infiltration. Importantly, the experimental validation results were consistent with the findings from the bioinformatics analysis. <b>Conclusion:</b> This study identified the distinct NS subtypes and their associated marker genes. These findings will contribute to elucidating the disease's heterogeneity and establishing appropriate personalized therapeutic approaches.</p>","PeriodicalId":18371,"journal":{"name":"Mediators of Inflammation","volume":"2025 ","pages":"5790261"},"PeriodicalIF":4.4000,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11964727/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Mediators of Inflammation","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1155/mi/5790261","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/1/1 0:00:00","PubModel":"eCollection","JCR":"Q2","JCRName":"CELL BIOLOGY","Score":null,"Total":0}
引用次数: 0

Abstract

Objective: Hypoxia-glycolysis-lactylation (HGL) may play a crucial role in neonatal sepsis (NS). This study aims to identify HGL marker genes in NS and explore immune microenvironment among NS subtypes. Materials and Methods: The gene expression dataset GSE69686, comprising 64 NS cases and 85 controls, was selected for analysis. Based on the screened HGL-related marker genes, diagnostic prediction models were constructed using nine machine learning algorithms, and molecular subtypes of NS were identified through consensus clustering. Subsequently, the heterogeneity of biological functions and immune cell infiltration among the different subtypes was analyzed. Finally, the marker genes and lactylation were validated using the GSE25504 dataset, clinical samples, and mouse neutrophil, respectively. Results: MERTK, HK3, PGK1, and STAT3 were identified and validated as marker genes, and the diagnostic prediction model for NS constructed using the support vector machine (SVM) algorithm exhibited optimal predictive performance. Based on gene expression patterns, two distinct NS subtypes were identified. Functional enrichment analysis highlighted significant immune-related pathways, while immune infiltration analysis revealed differences in neutrophil proportions between the subtypes. Furthermore, the expression levels of marker genes were positively correlated with neutrophil infiltration. Importantly, the experimental validation results were consistent with the findings from the bioinformatics analysis. Conclusion: This study identified the distinct NS subtypes and their associated marker genes. These findings will contribute to elucidating the disease's heterogeneity and establishing appropriate personalized therapeutic approaches.

新生儿脓毒症中性粒细胞和免疫功能的异质性:基于缺氧-糖酵解-乳酸化的分子亚型分析。
目的:缺氧-糖酵解-乳酰化(HGL)可能在新生儿败血症(NS)中发挥关键作用。本研究旨在鉴定 NS 中的 HGL 标记基因,并探讨 NS 亚型的免疫微环境。材料与方法:研究选择了由 64 例 NS 病例和 85 例对照组成的基因表达数据集 GSE69686 进行分析。根据筛选出的 HGL 相关标记基因,使用九种机器学习算法构建了诊断预测模型,并通过共识聚类确定了 NS 的分子亚型。随后,分析了不同亚型之间生物学功能和免疫细胞浸润的异质性。最后,分别利用 GSE25504 数据集、临床样本和小鼠中性粒细胞验证了标记基因和乳酸化。结果MERTK、HK3、PGK1和STAT3被鉴定并验证为标记基因,利用支持向量机(SVM)算法构建的NS诊断预测模型显示出最佳预测性能。根据基因表达模式,确定了两种不同的 NS 亚型。功能富集分析强调了重要的免疫相关通路,而免疫浸润分析则揭示了亚型之间中性粒细胞比例的差异。此外,标记基因的表达水平与中性粒细胞浸润呈正相关。重要的是,实验验证结果与生物信息学分析结果一致。结论本研究确定了不同的 NS 亚型及其相关标记基因。这些发现将有助于阐明该疾病的异质性,并建立适当的个性化治疗方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Mediators of Inflammation
Mediators of Inflammation 医学-免疫学
CiteScore
8.70
自引率
0.00%
发文量
202
审稿时长
4 months
期刊介绍: Mediators of Inflammation is a peer-reviewed, Open Access journal that publishes original research and review articles on all types of inflammatory mediators, including cytokines, histamine, bradykinin, prostaglandins, leukotrienes, PAF, biological response modifiers and the family of cell adhesion-promoting molecules.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信