Zhejiang Cancer Hospital, Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, China.
Department of Anesthesiology, Daping Hospital, Army Medical University, Chongqing, China.
Front Immunol. 2023 May 9;14:1184126. doi: 10.3389/fimmu.2023.1184126. eCollection 2023.
Sepsis remains a complex condition with incomplete understanding of its pathogenesis. Further research is needed to identify prognostic factors, risk stratification tools, and effective diagnostic and therapeutic targets.
Three GEO datasets (GSE54514, GSE65682, and GSE95233) were used to explore the potential role of mitochondria-related genes (MiRGs) in sepsis. WGCNA and two machine learning algorithms (RF and LASSO) were used to identify the feature of MiRGs. Consensus clustering was subsequently carried out to determine the molecular subtypes for sepsis. CIBERSORT algorithm was conducted to assess the immune cell infiltration of samples. A nomogram was also established to evaluate the diagnostic ability of feature biomarkers via "rms" package.
Three different expressed MiRGs (DE-MiRGs) were identified as sepsis biomarkers. A significant difference in the immune microenvironment landscape was observed between healthy controls and sepsis patients. Among the DE-MiRGs, was selected to be a potential therapeutic target and its significant elevated expression level was confirmed in sepsis using experiments and confocal microscopy, indicating its significant contribution to the mitochondrial quality imbalance in the LPS-simulated sepsis model.
By digging the role of these pivotal genes in immune cell infiltration, we gained a better understanding of the molecular immune mechanism in sepsis and identified potential intervention and treatment strategies.
脓毒症仍然是一种复杂的疾病，其发病机制尚未完全了解。需要进一步的研究来确定预后因素、风险分层工具以及有效的诊断和治疗靶点。
使用三个 GEO 数据集（GSE54514、GSE65682 和 GSE95233）来探讨线粒体相关基因（MiRGs）在脓毒症中的潜在作用。采用 WGCNA 和两种机器学习算法（RF 和 LASSO）来识别 MiRGs 的特征。随后进行共识聚类以确定脓毒症的分子亚型。采用 CIBERSORT 算法评估样本中的免疫细胞浸润。通过“rms”包建立列线图来评估特征生物标志物的诊断能力。
鉴定出三个不同表达的 MiRGs（DE-MiRGs）作为脓毒症的生物标志物。健康对照组和脓毒症患者之间的免疫微环境景观存在显著差异。在 DE-MiRGs 中，选择作为潜在的治疗靶点，并通过实验和共聚焦显微镜确认其在脓毒症中的显著高表达水平，表明其在 LPS 模拟脓毒症模型中线粒体质量失衡中具有重要作用。
通过挖掘这些关键基因在免疫细胞浸润中的作用，我们更好地了解了脓毒症中的分子免疫机制，并确定了潜在的干预和治疗策略。
