Integrating bioinformatics analysis, machine learning, and experimental validation to identify pyroptosis-related genes in the diagnosis of sepsis combined with acute liver failure.
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引用次数: 0
Abstract
Background: Sepsis is frequently combined with acute liver failure (ALF), a critical determinant in the mortality of septic patients. Pyroptosis is a significant form of programmed cell death that plays an important role in the inflammatory response. Research has been conducted to elucidate the relationship between pyroptosis, sepsis, and ALF, but the mechanism of action remains unclear.
Methods: Datasets relating to sepsis and ALF were obtained from the Gene Expression Omnibus (GEO). The intersection of differentially expressed genes (DEGs) and pyroptosis-related genes for sepsis and ALF was identified. Simultaneously, a gene diagnosis model for sepsis and ALF was developed using machine learning, and the model's accuracy was assessed through the plotting of the ROC curves and confusion matrix. The Hub genes identified by the model with an area under the curve (AUC) value ≥ 0.7 were used for the investigation of immune cell infiltration to explain the molecular mechanism of sepsis combined with ALF. The precise mechanism of action of these model genes in sepsis combined with ALF was evaluated through animal experiments.
Results: Machine learning revealed that GABARAP and ITCH may serve as diagnostic biomarkers for pyroptosis in sepsis combined with ALF. The examination of immune cell infiltration indicated that immune dysregulation is present in both sepsis and ALF and preliminarily suggested that GABARAP and ITCH may be pivotal in cellular immunity responses, particularly those mediated by T cells. Animal experiments further validated that in the process of sepsis combined with ALF, the expression level of GABARAP is elevated, while the expression level of ITCH is diminished.
Conclusions: We found GABARAP and ITCH may serve as diagnostic biomarkers for pyroptosis in sepsis combined with ALF, suggesting their potential involvement in the initiation and advancement of sepsis combined with ALF through cellular immunomodulatory pathways.
背景:脓毒症经常合并急性肝衰竭(ALF),这是脓毒症患者死亡率的关键决定因素。焦亡是程序性细胞死亡的一种重要形式,在炎症反应中起重要作用。已有研究阐明了焦亡、脓毒症和ALF之间的关系,但其作用机制尚不清楚。方法:从Gene Expression Omnibus (GEO)获得脓毒症和ALF相关数据集。鉴定了脓毒症和ALF的差异表达基因(DEGs)与焦热相关基因的交集。同时,利用机器学习建立了脓毒症和ALF的基因诊断模型,并通过绘制ROC曲线和混淆矩阵来评估模型的准确性。采用模型鉴定的曲线下面积(AUC)值≥0.7的Hub基因进行免疫细胞浸润研究,解释脓毒症合并ALF的分子机制。通过动物实验评估这些模式基因在脓毒症合并ALF中的确切作用机制。结果:机器学习显示,GABARAP和ITCH可能作为脓毒症合并ALF的焦亡诊断生物标志物。免疫细胞浸润检查表明,败血症和ALF均存在免疫失调,初步提示GABARAP和ITCH可能在细胞免疫反应,特别是T细胞介导的细胞免疫反应中起关键作用。动物实验进一步证实,在脓毒症合并ALF的过程中,GABARAP表达水平升高,而ITCH表达水平降低。结论:我们发现GABARAP和ITCH可能是脓毒症合并ALF中焦亡的诊断性生物标志物,提示它们可能通过细胞免疫调节途径参与脓毒症合并ALF的发生和进展。临床试验号:不适用。
HereditasBiochemistry, Genetics and Molecular Biology-Genetics
CiteScore
3.80
自引率
3.70%
发文量
0
期刊介绍:
For almost a century, Hereditas has published original cutting-edge research and reviews. As the Official journal of the Mendelian Society of Lund, the journal welcomes research from across all areas of genetics and genomics. Topics of interest include human and medical genetics, animal and plant genetics, microbial genetics, agriculture and bioinformatics.