Liu Fangyu, Wang Qian, Ye Haoran, Du Yuan, Wang Mingjiao, Guo Yuhong, He Shasha
Beijing University of Chinese Medicine, Beijing, China.
Beijing Hospital of Traditional Chinese Medicine, Capital Medical University, Beijing, China.
J Mol Med (Berl). 2025 Jan;103(1):87-100. doi: 10.1007/s00109-024-02502-z. Epub 2024 Nov 19.
Ferroptosis is the well-known mechanism of septic cardiomyopathy (SCM). Bioinformatics analysis was employed to identify ferroptosis-related SCM differentially expressed genes (DEG). DEGs' functional enrichment was explored. Weighted gene co-expression network analysis (WGCNA) was employed to form gene clusters. The identified hub genes, signal transducer and activator of transcription 3 (STAT3) and myelocytomatosis (MYC) were further evaluated by generating receiver operator characteristic (ROC) curves and a nomogram prediction model. Additionally, survival rate, cardiac damage markers, and cardiac function and ferroptosis markers were evaluated in septic mouse model. STAT3 and MYC levels were measured in SCM heart tissue via immunohistochemical (IHC) staining, real-time polymerase chain reaction (qPCR) and western blot analysis. Analysis identified 225 DEGs and revealed 22 intersected genes. Of the 7 hub genes, STAT3 and MYC showed enrichment in septic heart tissue and a strong predicative ability based on AUC values. Cardiac damage, iron metabolism, and lipid peroxidation occurred in the SCM model. By experiments, STAT3 and MYC expression was increased in the SCM model. Impairment was reversed with a ferroptosis inhibitor, Fer-1. As conclusion, STAT3 and MYC are related with ferroptosis and may serve as potential SCM predictor indicators. KEY MESSAGES: Septic cardiomyopathy (SCM) often leads to high mortality in septic patients, and the diagnostic criteria still remains unclear. Ferroptosis as the pathogenic mechanism of SCM could help predict its progression and clinical outcomes. STAT3 and MYC are related with ferroptosis and may serve as potential SCM predictor biomarkers.
铁死亡是脓毒症性心肌病（SCM）的著名机制。采用生物信息学分析来鉴定与铁死亡相关的SCM差异表达基因（DEG）。探索了DEG的功能富集情况。采用加权基因共表达网络分析（WGCNA）来形成基因簇。通过生成受试者工作特征（ROC）曲线和列线图预测模型，对鉴定出的关键基因信号转导和转录激活因子3（STAT3）和髓细胞瘤（MYC）进行了进一步评估。此外，在脓毒症小鼠模型中评估了生存率、心脏损伤标志物、心脏功能和铁死亡标志物。通过免疫组织化学（IHC）染色、实时聚合酶链反应（qPCR）和蛋白质印迹分析，测定了SCM心脏组织中的STAT3和MYC水平。分析鉴定出225个DEG，并揭示了22个交集基因。在7个关键基因中，STAT3和MYC在脓毒症心脏组织中表现出富集，并且基于AUC值具有很强的预测能力。SCM模型中发生了心脏损伤、铁代谢和脂质过氧化。通过实验发现，SCM模型中STAT3和MYC的表达增加。使用铁死亡抑制剂Fer-1可逆转损伤。结论是，STAT3和MYC与铁死亡相关，可能作为潜在的SCM预测指标。关键信息：脓毒症性心肌病（SCM）常导致脓毒症患者高死亡率，且诊断标准仍不明确。铁死亡作为SCM的致病机制有助于预测其进展和临床结果。STAT3和MYC与铁死亡相关，可能作为潜在的SCM预测生物标志物。
