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中华卫生应急电子杂志 ›› 2025, Vol. 11 ›› Issue (02) : 86 -101. doi: 10.3877/cma.j.issn.2095-9133.2025.02.004

论著

基于机器学习和转录组学综合分析线粒体自噬和铁死亡关键基因在成人脓毒症诱导ARDS中的免疫调控作用机制
欧范妍1, 郭乾2, 曾莉雄1, 陈秋莉1, 甘厚玉1, 杨洁1,()   
  1. 1. 530007 广西南宁,广西医科大学第二附属医院临床病理学
    2. 530007 广西南宁,广西医科大学第二附属医院全科医学
  • 收稿日期:2025-01-24 出版日期:2025-04-18
  • 通信作者: 杨洁
  • 基金资助:
    广西科技基地和人才专项(桂科AD25069030)

Machine learning and omics-driven dissection of mitophagy and ferroptosis in sepsis-associated ARDS: unraveling key genetic roles and immune regulatory mechanisms

Fanyan Ou1, Qian Guo2, Lixiong Zeng1, Qiuli Chen1, Houyu Gan1, Jie Yang3,()   

  1. 1. Department of Clinical Pathology, the Second Affiliated Hospital of Guangxi Medical University, Nanning 530007, China
    2. Department of General Practice, the Second Affiliated Hospital of Guangxi Medical University, Nanning 530007, China
    3. Clinical Medical Research Center, the Second Affiliated Hospital of Guangxi Medical University, Nanning 530007, China
  • Received:2025-01-24 Published:2025-04-18
  • Corresponding author: Jie Yang
引用本文:

欧范妍, 郭乾, 曾莉雄, 陈秋莉, 甘厚玉, 杨洁. 基于机器学习和转录组学综合分析线粒体自噬和铁死亡关键基因在成人脓毒症诱导ARDS中的免疫调控作用机制[J/OL]. 中华卫生应急电子杂志, 2025, 11(02): 86-101.

Fanyan Ou, Qian Guo, Lixiong Zeng, Qiuli Chen, Houyu Gan, Jie Yang. Machine learning and omics-driven dissection of mitophagy and ferroptosis in sepsis-associated ARDS: unraveling key genetic roles and immune regulatory mechanisms[J/OL]. Chinese Journal of Hygiene Rescue(Electronic Edition), 2025, 11(02): 86-101.

目的

基于机器学习和转录组学综合分析,揭示线粒体自噬与铁死亡关键基因在脓毒症诱导急性呼吸窘迫综合征(ARDS)中的作用及免疫调控机制。

方法

(1)数据获取与差异基因筛选:从GEO数据库下载脓毒症致ARDS患者转录组数据集GSE32707,筛选差异表达基因(DEGs)。(2)核心基因挖掘与验证:联合LASSO回归和SVM-RFE算法对DEGs二次筛选,获得核心基因,并通过ROC曲线评估其诊断效能。(3)功能与免疫调控解析:利用GSEA富集分析和免疫浸润分析,明确DEGs相关生物学通路及免疫细胞互作关系。(4)关键基因交叉锁定:整合线粒体自噬相关基因与铁死亡标志物,与核心DEGs取交集,确定枢纽基因。(5)多维调控网络构建:通过miRNA靶向预测(miRWalk数据库等)和泛素化互作分析(UbiBrowser数据库),探究枢纽基因可能的调控机制。

结果

初步筛选出576个DEGs。经机器学习算法二次筛选,鉴定出12个核心基因。GSEA与免疫浸润分析显示,核心基因显著富集于免疫相关通路。整合线粒体自噬与铁死亡相关基因集并与核心DEGs取交集,确定FTH1为枢纽基因。FTH1表达水平与中性粒细胞及细胞因子受体(CCR)呈正相关。机制探索表明,FTH1的表达可能受miR-224-5p靶向调控,并与E3泛素连接酶SMURF1互作,提示其可能通过泛素化修饰参与疾病进程。

结论

本研究结合机器学习与多组学整合策略,首次鉴定出FTH1是脓毒症相关ARDS 中线粒体自噬与铁死亡的关键调控基因。揭示其可能受miR-224-5p靶向调控及与泛素化连接酶SMURF1互作的调控网络,为开发针对该疾病的早期免疫干预新靶点提供了重要方向和理论依据。

Objective

To explore the roles and immune regulatory mechanisms of key genes in mitophagy and ferroptosis in sepsis-induced acute respiratory distress syndrome (ARDS) using an integrative analysis of machine learning and transcriptomics.

Methods

The DEGs from the GSE32707 dataset in the GEO database were obtained and the differential genes screened. The core genes were identified and validated by secondary screening of DEGs with LASSO regression and the SVM-RFE algorithm, and their diagnostic performance with ROC curves was evaluated. DEGs-related biological pathways and immune cell interactions were investigated via GSEA and immune infiltration analysis. The hub genes by intersecting mitophagy-related genes, ferroptosis markers, and core DEGs were determined. A multi-dimensional regulatory network was established by predicting miRNA targets (using miRWalk, etc.) and ubiquitination interactions (using UbiBrowser), and then potential regulatory mechanisms of hub genes were explored.

Results

Initially, 576 DEGs were screened. Then, 12 core genes were identified via machine learning algorithms. GSEA and immune infiltration analysis showed these core genes were significantly enriched in immune-related pathways. By integrating mitophagy and ferroptosis-related genes with core DEGs, FTH1 was identified as a hub gene, whose expression positively correlated with neutrophil levels and CCR. Mechanistic exploration suggested FTH1 expression might be regulated by miR-224-5p and interact with the E3 ubiquitin ligase SMURF1, implying its potential involvement in disease progression via ubiquitination modification.

Conclusion

This study, combining machine learning and multi-omics integration, first identifies FTH1 as a key regulator of mitophagy and ferroptosis in sepsis-related ARDS. It reveals a regulatory network where FTH1 might be targeted by miR-224-5p and interact with SMURF1, offering new directions and a theoretical basis for developing early immunointervention targets for this disease.

图1 基于机器学习和转录组学综合分析线粒体自噬和铁死亡关键基因在成人脓毒症诱导ARDS中的免疫调控作用机制流程图注:ARDS为急性呼吸窘迫综合征,DEGs为差异表达基因,LASSO为最小绝对收缩和选择算法,SVM+RFE为回归和支持向量机结合递归特征消除算法,ROC为受试者工作特征曲线,FTH1为铁蛋白重链1
图2 脓毒症致ARDS的DEGs的鉴定和免疫细胞浸润 注:a为脓毒症诱导ARDS与对照组前100个差异表达基因热图,b为免疫浸润分析,c为免疫浸润差异,d为22种免疫细胞的相关性分析(蓝色表示负相关,红色表示正相关。相关性越强,颜色越深)
图3 脓毒症诱导ARDS相关的12个差异基因 注:a~b为LASSO获取脓毒症诱导ARDS相关的差异基因22个;c~d为SVM+RFE获取脓毒症诱导的ARDS相关的差异表达基因24个;e~p为12个差异基因在脓毒症诱导的ARDS与对照组中的表达量对比图
图4 12个关键差异基因的ROC曲线 注:a为FBXO45,b为FTH1,c为SLC15A1,d为SLC22A11,e为SLC35A2,f为SLC39A9,g为UBE2E2,h为USP11,i为ZNF366,j为ZNF409,k为ZNF561,l为ZNF594
图5 关键差异基因的GSEA富集分析 注:a为FBXO45高表达组的GSEA富集分析,b为ZNF366高表达组的GSEA富集分析,c为ZNF561高表达组的GSEA富集分析
图6 关键差异基因的GSEA富集分析 注:a为SLC15A1低表达组的GSEA富集分析,b为SLC39A9低表达组的GSEA富集分析,c为SLC22A11高表达组的GSEA富集分析,d为SLC22A11低表达组的GSEA富集分析,e为SLC35A2高表达组的GSEA富集分析,f为SLC35A2低表达组的GSEA富集分析,g为FTH1高表达组的GSEA富集分析,h为FTH1低表达组的GSEA富集分析,i为UBE2E2高表达组的GSEA富集分析,j为UBE2E2低表达组的GSEA富集分析,k为USP11高表达组的GSEA富集分析,l为USP11低表达组的GSEA富集分析,m为ZNF409高表达组的GSEA富集分析,n为ZNF409低表达组的GSEA富集分析,o为ZNF594高表达组的GSEA富集分析,p为ZNF594低表达组的GSEA富集分析
图7 12个基因的免疫功能分析 注:a为FBXO45,b为FTH1,c为SLC15A1,d为SLC22A11,e为SLC35A2,f为SLC39A9,g为UBE2E2,h为USP11,i为ZNF366,j为ZNF409,k为ZNF561,l为ZNF594
图8 12个基因与免疫细胞的相关性 注:a为FBXO45,b为FTH1,c为SLC15A1,d为SLC22A11,e为USP11,f-g为SLC35A2,h-j为SLC39A9,k-o为UBE2E2,p-q为ZNF366,r-t为ZNF409
图9 miR-224-5p可能通过靶向FTH1,并与SMURF1相互作用,参与调控脓毒症诱导ARDS的铁死亡及线粒体自噬过程 注:a为LASSO、SVM+RFE、线粒体自噬与铁死亡标记物基因列表,b为对4组基因取交集,c为预测miRNA,d为FTH1基因的泛素化修饰及其相互作用网络
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