Department of General Surgery, Jinling Hospital, Medical School of Nanjing University, Nanjing, China.
Nursing School, Chongqing Medical University, Chongqing, China.
Front Immunol. 2023 Jun 15;14:1196306. doi: 10.3389/fimmu.2023.1196306. eCollection 2023.
Owing to the complex pathophysiological features and heterogeneity of sepsis, current diagnostic methods are not sufficiently precise or timely, causing a delay in treatment. It has been suggested that mitochondrial dysfunction plays a critical role in sepsis. However, the role and mechanism of mitochondria-related genes in the diagnostic and immune microenvironment of sepsis have not been sufficiently investigated.
Mitochondria-related differentially expressed genes (DEGs) were identified between human sepsis and normal samples from GSE65682 dataset. Least absolute shrinkage and selection operator (LASSO) regression and the Support Vector Machine (SVM) analyses were carried out to locate potential diagnostic biomarkers. Gene ontology and gene set enrichment analyses were conducted to identify the key signaling pathways associated with these biomarker genes. Furthermore, correlation of these genes with the proportion of infiltrating immune cells was estimated using CIBERSORT. The expression and diagnostic value of the diagnostic genes were evaluated using GSE9960 and GSE134347 datasets and septic patients. Furthermore, we established an sepsis model using lipopolysaccharide (1 µg/mL)-stimulated CP-M191 cells. Mitochondrial morphology and function were evaluated in PBMCs from septic patients and CP-M191 cells, respectively.
In this study, 647 mitochondrion-related DEGs were obtained. Machine learning confirmed six critical mitochondrion-related DEGs, including , , , , , and . We then developed a diagnostic model using the six genes, and receiver operating characteristic (ROC) curves indicated that the novel diagnostic model based on the above six critical genes screened sepsis samples from normal samples with area under the curve (AUC) = 1.000, which was further demonstrated in the GSE9960 and GSE134347 datasets and our cohort. Importantly, we also found that the expression of these genes was associated with different kinds of immune cells. In addition, mitochondrial dysfunction was mainly manifested by the promotion of mitochondrial fragmentation (p<0.05), impaired mitochondrial respiration (p<0.05), decreased mitochondrial membrane potential (p<0.05), and increased reactive oxygen species (ROS) generation (p<0.05) in human sepsis and LPS-simulated sepsis models.
We constructed a novel diagnostic model containing six MRGs, which has the potential to be an innovative tool for the early diagnosis of sepsis.
由于脓毒症复杂的病理生理特征和异质性，目前的诊断方法不够精确或及时，导致治疗延误。有研究表明，线粒体功能障碍在脓毒症中起着关键作用。然而，线粒体相关基因在脓毒症的诊断和免疫微环境中的作用和机制尚未得到充分研究。
从 GSE65682 数据集的人类脓毒症和正常样本中鉴定线粒体相关差异表达基因（DEGs）。采用最小绝对收缩和选择算子（LASSO）回归和支持向量机（SVM）分析来定位潜在的诊断生物标志物。进行基因本体论和基因集富集分析，以确定与这些生物标志物基因相关的关键信号通路。此外，使用 CIBERSORT 估计这些基因与浸润免疫细胞比例的相关性。使用 GSE9960 和 GSE134347 数据集和脓毒症患者评估诊断基因的表达和诊断价值。此外，我们使用脂多糖（1μg/mL）刺激的 CP-M191 细胞建立脓毒症模型。分别评估脓毒症患者和 CP-M191 细胞的外周血单核细胞的线粒体形态和功能。
本研究获得了 647 个线粒体相关 DEGs。机器学习证实了 6 个关键的线粒体相关 DEGs，包括 、 、 、 、 和 。然后，我们使用这 6 个基因开发了一个诊断模型，受试者工作特征（ROC）曲线表明，基于上述 6 个关键基因的新型诊断模型从正常样本中筛选脓毒症样本的曲线下面积（AUC）为 1.000，这在 GSE9960 和 GSE134347 数据集以及我们的队列中得到进一步验证。重要的是，我们还发现这些基因的表达与不同类型的免疫细胞有关。此外，线粒体功能障碍主要表现为促进线粒体碎片化（p<0.05）、线粒体呼吸受损（p<0.05）、线粒体膜电位降低（p<0.05）和活性氧（ROS）生成增加（p<0.05）在人类脓毒症和 LPS 模拟的脓毒症模型中。
我们构建了一个包含 6 个 MRGs 的新型诊断模型，它有可能成为脓毒症早期诊断的创新工具。
