Background: Numerous studies have shown that mitochondrial damage induces inflammation and activates inflammatory cells, leading to sepsis, while sepsis, a systemic inflammatory response syndrome, also exacerbates mitochondrial damage and hyperactivation. Mitochondrial autophagy eliminates aged, abnormal or damaged mitochondria to reduce intracellular mitochondrial stress and the release of mitochondria-associated molecules, thereby reducing the inflammatory response and cellular damage caused by sepsis. In addition, mitochondrial autophagy may also influence the onset and progression of sepsis, but the exact mechanisms are unclear. background: Sepsis is a critical systemic infection, a syndrome of severe inflammatory response of the organism to various pathogenic microorganisms. Methods: In this study, we mined the available publicly available microarray data in the GEO database (Home - GEO - NCBI (nih.gov)) with the aim of identifying key genes associated with mitochondrial autophagy in sepsis. objective: In this study, we used a bioinformatics approach to integrate multiple microarray data to screen for mitochondrial autophagy-related hub genes associated with sepsis onset and progression in a more scientific and systematic manner. Results: We identified four mitophagy-related genes in sepsis, TOMM20, TOMM22, TOMM40, and MFN1. method: Robust rank aggregation (RRA) Conclusion: This study provides preliminary evidence for the treatment of sepsis and may provide a solid foundation for subsequent biological studies. result: we constructed a PPI network combined with RRA analysis method to finally identify 4 key genes, namely TOMM20, TOMM22, TOMM40, and MFN1. conclusion: In this study, we used a bioinformatics analysis method, RRA, to integrate five gene microarray datasets to identify pivotal genes associated with mitochondrial autophagy in sepsis. Gene ontology (GO) functional annotation results show that these hub genes are mainly enriched in mitochondrial transport and establishment of protein localization to mitochondrion. Finally, we constructed the PPI network with the top 100 genes obtained from the rra method analysis. Based on the RRA results, the PPI results and the mitochondrial autophagy-related genes we found in the Reactome Pathway Database, we finally identified four key genes as TOMM20, TOMM22, TOMM40, and MFN1, respectively.

中文翻译：

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脓毒症线粒体自噬相关基因的鉴定

背景：大量研究表明，线粒体损伤会诱发炎症并激活炎症细胞，导致脓毒症，而脓毒症这种全身炎症反应综合征也会加剧线粒体损伤和过度激活。线粒体自噬消除老化、异常或受损的线粒体，以减少细胞内线粒体应激和线粒体相关分子的释放，从而减轻脓毒症引起的炎症反应和细胞损伤。此外，线粒体自噬也可能影响脓毒症的发生和进展，但具体机制尚不清楚。背景：脓毒症是一种危重的全身感染，是机体对多种病原微生物产生严重炎症反应的综合征。方法：在本研究中，我们挖掘了 GEO 数据库（主页 - GEO - NCBI (nih.gov)）中可用的公开微阵列数据，目的是识别与脓毒症线粒体自噬相关的关键基因。目的：本研究采用生物信息学方法，整合多个微阵列数据，以更科学、系统的方式筛选与脓毒症发病和进展相关的线粒体自噬相关中枢基因。结果：我们在脓毒症中鉴定了四种线粒体自噬相关基因：TOMM20、TOMM22、TOMM40 和 MFN1。方法：稳健排序聚合（RRA）结论：本研究为脓毒症的治疗提供了初步证据，并可能为后续生物学研究提供坚实的基础。结果：我们构建了PPI网络结合RRA分析方法，最终识别出4个关键基因，即TOMM20、TOMM22、TOMM40和MFN1。结论：在本研究中，我们使用生物信息学分析方法 RRA 整合五个基因微阵列数据集，以确定与脓毒症线粒体自噬相关的关键基因。基因本体（GO）功能注释结果表明，这些枢纽基因主要富集于线粒体运输和建立线粒体蛋白质定位。最后，我们用rra方法分析得到的前100个基因构建了PPI网络。根据RRA结果、PPI结果以及我们在Reactome Pathway Database中找到的线粒体自噬相关基因，我们最终确定了四个关键基因，分别为TOMM20、TOMM22、TOMM40和MFN1。