Jiang Huimin, Ren Yun, Yu Jiale, Hu Sheng, Zhang Jihui
Emergency Intensive Care Unit, Ningxiang People's Hospital Affiliated to Hunan University of Chinese Medicine, Changsha, China.
Emergency Department, Ningxiang People's Hospital Affiliated to Hunan University of Chinese Medicine, Changsha, China.
Front Genet. 2023 Jul 26;14:1223243. doi: 10.3389/fgene.2023.1223243. eCollection 2023.
Lactate, as an essential clinical evaluation index of septic shock, is crucial in the incidence and progression of septic shock. This study aims to investigate the differential expression, regulatory relationship, clinical diagnostic efficacy, and immune infiltration of lactate metabolism-related genes (LMGs) in septic shock. Two sepsis shock datasets (GSE26440 and GSE131761) were screened from the GEO database, and the common differentially expressed genes (DEGs) of the two datasets were screened out. LMGs were selected from the GeneCards database, and lactate metabolism-related DEGs (LMDEGs) were determined by integrating DEGs and LMGs. Protein-protein interaction networks, mRNA-miRNA, mRNA-RBP, and mRNA-TF interaction networks were constructed using STRING, miRDB, ENCORI, and CHIPBase databases, respectively. Receiver operating characteristic (ROC) curves were constructed for each of the LMDEGs to evaluate the diagnostic efficacy of the expression changes in relation to septic shock. Finally, immune infiltration analysis was performed using ssGSEA and CIBERSORT. This study identified 10 LMDEGs, including , and . Enrichment analysis revealed that DEGs were significantly enriched in pathways such as pyruvate metabolism, hypoxia pathway, and immune-inflammatory pathways. PPI networks based on LMDEGs, as well as 148 pairs of mRNA-miRNA interactions, 243 pairs of mRNA-RBP interactions, and 119 pairs of mRNA-TF interactions were established. ROC curves of eight LMDEGs (, and ) with consistent expression patterns in two datasets had an area under the curve (AUC) ranging from 0.662 to 0.889. The results of ssGSEA and CIBERSORT both showed significant differences in the infiltration of various immune cells, including CD8 T cells, T regulatory cells, and natural killer cells, and LMDEGs such as , and were significantly associated with various immune cells. The LMDEGs are significantly associated with the immune-inflammatory response in septic shock and have a certain diagnostic accuracy for septic shock.
乳酸作为脓毒症休克重要的临床评估指标，在脓毒症休克的发生和发展过程中至关重要。本研究旨在探究脓毒症休克中乳酸代谢相关基因（LMGs）的差异表达、调控关系、临床诊断效能及免疫浸润情况。从基因表达综合数据库（GEO数据库）中筛选出两个脓毒症休克数据集（GSE26440和GSE131761），并筛选出两个数据集的共同差异表达基因（DEGs）。从基因卡片数据库中选取LMGs，并通过整合DEGs和LMGs确定乳酸代谢相关差异表达基因（LMDEGs）。分别使用STRING、miRDB、ENCORI和CHIPBase数据库构建蛋白质-蛋白质相互作用网络、mRNA- miRNA、mRNA-RBP和mRNA-TF相互作用网络。为每个LMDEG构建受试者工作特征（ROC）曲线，以评估表达变化对脓毒症休克的诊断效能。最后，使用单样本基因集富集分析（ssGSEA）和CIBERSORT进行免疫浸润分析。本研究鉴定出10个LMDEGs，包括……和……。富集分析显示，DEGs在丙酮酸代谢、缺氧途径和免疫炎症途径等通路中显著富集。基于LMDEGs建立了蛋白质-蛋白质相互作用网络，以及148对mRNA- miRNA相互作用、243对mRNA-RBP相互作用和119对mRNA-TF相互作用。在两个数据集中表达模式一致的8个LMDEGs（……和……）的ROC曲线下面积（AUC）范围为0.662至0.889。ssGSEA和CIBERSORT的结果均显示，包括CD8 T细胞、调节性T细胞和自然杀伤细胞在内的各种免疫细胞浸润存在显著差异，且……和……等LMDEGs与各种免疫细胞显著相关。LMDEGs与脓毒症休克中的免疫炎症反应显著相关，对脓毒症休克具有一定的诊断准确性。
