Kong Fanyu, Zhu Yuxin, Xu Jiani, Ling Bingrui, Wang Chunxue, Ji Jinlu, Yang Qian, Liu Xiandong, Shao Li, Zhou Xiaohui, Chen Kun, Yang Min, Tang Lunxian
Department of Internal Emergency Medicine, Shanghai East Hospital, School of Medicine, Tongji University ,Shanghai, China; School of Medicine, Tongji University, Shanghai 200092, China.
The Second Department of Critical Care Medicine, The Second Affiliated Hospital of Anhui Medical University, Hefei, Anhui 230601, China; Laboratory of Cardiopulmonary Resuscitation and Critical Care, The Second Affiliated Hospital of Anhui Medical University, Hefei, Anhui 230601, China.
Int Immunopharmacol. 2025 Mar 6;149:114194. doi: 10.1016/j.intimp.2025.114194. Epub 2025 Feb 3.
Programmed cell death (PCD) has emerged as a pivotal progress in pathogenesis of sepsis, but its role in identification of sepsis has not been fully understood.
Differentially expressed genes (DEGs) were identified from the GEO database. PCD-related genes were intersected with DEGs, and key PcDEGs were identified through the protein-protein interaction (PPI) network. To pinpoint hub PcDEGs in sepsis, we applied Random Forest (RF), Support Vector Machine (SVM), Extreme Gradient Boosting (XGB), and Generalized Linear Model (GLM) algorithms. Additionally, the expression levels of five hub PcDEGs were validated in single cell RNA sequencing of sepsis patients and peripheral blood mononuclear cells (PBMCs) from a clinical cohort by quantitative real-time PCR (qRT-PCR). LCK expression was further determined by ELISA, and its diagnostic and prognostic value was evaluated using ROC analysis. LCK levels in the cecal ligation and puncture (CLP)-induced sepsis mouse model were assessed by Western blot and Immunofluorescence (IF). Finally, we assessed the regulatory role of LCK in cell apoptosis using flow cytometry and Western blot analysis.
70 PcDEGs were identified by intersecting 690 DEGs and 1254 PCD-related genes. PPI analysis identified top 15 genes based on Degree algorithm. We then identified five hub PcDEGs (LCK, IL10RA, CD3E, CD5 and ITGAM) that could serve as biomarkers through machine learning. As the expressions of LCK, IL10RA, CD3E and CD5 decreased and ITGAM expression was upregulated in septic patients. Consistently, Serum LCK concentration was reduced in septic patients, and the area under the ROC curve (AUC) of LCK was 0.753. Importantly, LCK displayed more pronounced reduction in non-survivors and those with septic shock than survivors and non-shock patients. The AUC for LCK was 0.726 in predicting mortality of septic patients. Moreover, we observed a decrease expression of LCK in the vital organs (liver, lung, spleen, thymus and PBMC) of septic mice model which mirrored observations in septic patients. Finally, we found that inhibiting LCK promoted apoptosis in Jurkat cells.
Our study reveals that PcDEGs are dysregulated in sepsis, and closely related to disease pathology. Our finding provides new insights into clinical identification and outcome prediction of sepsis. Of note, LCK is a new biomarker for diagnosis and prognosis, which might be a potential therapeutic target for the treatment of sepsis.
程序性细胞死亡（PCD）已成为脓毒症发病机制中的一个关键进展，但其在脓毒症诊断中的作用尚未完全明确。
从基因表达综合数据库（GEO数据库）中鉴定出差异表达基因（DEGs）。将PCD相关基因与DEGs进行交集分析，并通过蛋白质-蛋白质相互作用（PPI）网络鉴定关键的PCD相关差异基因（PcDEGs）。为了确定脓毒症中的核心PcDEGs，我们应用了随机森林（RF）、支持向量机（SVM）、极端梯度提升（XGB）和广义线性模型（GLM）算法。此外，通过定量实时聚合酶链反应（qRT-PCR）在脓毒症患者的单细胞RNA测序和来自临床队列的外周血单个核细胞（PBMCs）中验证了五个核心PcDEGs的表达水平。通过酶联免疫吸附测定（ELISA）进一步测定淋巴细胞特异性蛋白酪氨酸激酶（LCK）的表达，并使用受试者工作特征（ROC）分析评估其诊断和预后价值。通过蛋白质免疫印迹法（Western blot）和免疫荧光法（IF）评估盲肠结扎穿孔（CLP）诱导的脓毒症小鼠模型中LCK的水平。最后，我们使用流式细胞术和蛋白质免疫印迹分析评估了LCK在细胞凋亡中的调节作用。
通过将690个DEGs与1254个PCD相关基因进行交集分析，鉴定出70个PcDEGs。基于度算法的PPI分析确定了前15个基因。然后，我们通过机器学习鉴定出五个可作为生物标志物的核心PcDEGs（LCK、白细胞介素10受体α（IL10RA）、CD3e分子（CD3E）、CD5分子（CD5）和整合素αM（ITGAM））。脓毒症患者中，LCK、IL10RA、CD3E和CD5的表达降低，而ITGAM表达上调。同样，脓毒症患者血清LCK浓度降低，LCK的ROC曲线下面积（AUC）为0.753。重要的是，与幸存者和非休克患者相比，非幸存者和脓毒症休克患者中LCK的降低更为明显。LCK预测脓毒症患者死亡率的AUC为0.726。此外，我们观察到脓毒症小鼠模型的重要器官（肝脏、肺、脾脏和胸腺以及PBMC）中LCK表达降低，这与脓毒症患者的观察结果一致。最后，我们发现抑制LCK可促进人急性T淋巴细胞白血病细胞（Jurkat细胞）凋亡。
我们的研究表明，PcDEGs在脓毒症中表达失调，且与疾病病理密切相关。我们的发现为脓毒症的临床诊断和预后预测提供了新的见解。值得注意的是，LCK是一种用于诊断和预后的新型生物标志物，可能是治疗脓毒症的潜在治疗靶点。
