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机器学习和蛋白质与蛋白质相互作用网络的结合建立了一个ATM-DPP4-TXN铁形诊断模型,并进行实验验证

Mengze Wu¹, Zhao Zou¹, Yuce Peng¹

¹Division of Cardiology, The First Affiliated Hospital of Chongqing Medical University, Yuzhong, Chongqing 400016, P.R. China.

Molecular medicine reports

|July 4, 2025

概括

细胞死亡过程 - - 铁亡与败血症引起的炎症有关. 研究人员确定了三个关键的铁亡相关基因 (FRDEGs) 作为毒症的潜在诊断生物标志物,提供了新的治疗点.

科学领域:

生物医学研究的研究.
计算生物学是一种计算生物学.
免疫学免疫学免疫学

背景情况:

败血症涉及炎症性微环境功能障碍,诊断的有效生物标志物有限.
铁亡是一种受调节的细胞死亡途径,因其在炎症中的作用越来越受认可.
向铁亡调节剂可能为败血症提供新的诊断和治疗策略.

研究的目的:

使用生物信息学研究铁和败血症之间的关联.
为了确定潜在的铁亡相关基因 (FRDEGs) 作为败血症的诊断生物标志物.
分析免疫微环境和与已识别的FRDEGs的炎症关联.

主要方法:

使用基因表达综合 (GEO) 数据集用于败血症.
进行了差异性基因表达分析,并与ferroptosis基因集交叉,以识别FRDEGs.
构建了蛋白质-蛋白质相互作用网络 (PPIN) 和应用机器学习算法来识别枢纽FRDEG.
验证了使用名图和分析免疫细胞透的诊断准确性.

主要成果:

从4410个差异表达基因 (DEG) 和506个铁灭基因中鉴定出94个FRDEG.
构建了一种PPIN,揭示了38个枢纽FRDEG,确定了三个关键基因:ataxia telangiectasia突变,dpeptidyl peptidase 4和thioredoxin.
开发并验证了用于败血症的诊断名录.
揭示了枢纽FRDEG和免疫细胞之间的显著差异,突出显示了免疫失调.

结论:

三个枢纽FRDEG (ATM,DPP4,TXN) 是潜在的败血症诊断生物标志物.
铁死在败血症中观察到的炎症功能障碍中起着重要作用.
这些发现表明药理干预和改善败血症诊断的新途径.

关键词:

DPP4 DPP4 DPP4 DPP4 DPP4 DPP4 DPP4 DPP4 DPP4 DPP4 DPP4 亚达克西亚 telangiectasia 突变的突变的铁性化 (ferroptosis) 是一种免疫微观环境免疫微环境这是一种血症.甲素 (thioredoxin) 是一种甲素.

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