Yang Jie, Tan YaoXi, Liu Xing
Department of Infectious Diseases, Affiliated hospital of Jiangnan University, Wuxi, Jiangsu, China.
Department of Emergency, Affiliated Wuxi Fifth Hospital of Jiangnan University, Wuxi, Jiangsu, China.
Ann Med. 2025 Dec;57(1):2492830. doi: 10.1080/07853890.2025.2492830. Epub 2025 Apr 29.
Programmed cell death (PCD) plays a key role in the progression of coronavirus disease 2019 (COVID-19). However, PCD-relevant biomarkers have not been fully discovered. The aim of this study was to explore the PCD-relevant biomarkers for the treatment and prevention of COVID-19.
Bioinformatic analyses were performed to explore the clinical relevant PCD genes with differential expression (DE) in COVID-19 compared with matched controls. PPI network was used for hub genes screening and machine learning methods were employed for filtering feature genes. The biomarker genes were screened by Venn diagram. The correlations between biomarkers with clinical features and immune microenvironment were further explored. Biomarker validation was performed in clinical samples by real-time reverse transcriptase-polymerase chain reaction (RT-qPCR).
In total, 118 clinically relevant and PCD associated differential expressed genes (DEGs) were screened, which were mainly related with apoptosis related pathways, among which six biomarkers (Cyclin B1 (CCNB1), cyclin-dependent kinase 1 (CDK1), interferon regulatory factor 4 (IRF4), lipoteichoic acid (LTA), matrix metallopeptidase 9 (MMP9) and Oncostatin M (OSM)) were identified. The excellent or good diagnostic performance of biomarkers was determined by receiver operating characteristic (ROC) curve analysis. The biomarkers showed diverse correlations with clinical indicators, such as age, sex and Intensive Care Unit (ICU) admission. Total 14 types of immune cells exerted differential infiltration between COVID-19 and controls. Biomarkers were correlated with immune cells at varying levels. COVID-19 was classified in three clusters, which showed differential expression of biomarker genes and significant associations with clinical information, such as sex, age and ICU admission. The DEGs of biomarkers were determined in COVID-19 patients relative to controls.
The six biomarkers (CCNB1, CDK1, IRF4, LTA, MMP9 and OSM) can be served as the biomarkers for the treatment and prevention of COVID-19.
程序性细胞死亡（PCD）在2019冠状病毒病（COVID-19）进展中起关键作用。然而，与PCD相关的生物标志物尚未被完全发现。本研究旨在探索用于COVID-19治疗和预防的与PCD相关的生物标志物。
进行生物信息学分析，以探索与匹配对照相比在COVID-19中具有差异表达（DE）的临床相关PCD基因。蛋白质-蛋白质相互作用（PPI）网络用于筛选枢纽基因，机器学习方法用于筛选特征基因。通过维恩图筛选生物标志物基因。进一步探索生物标志物与临床特征和免疫微环境之间的相关性。通过实时逆转录-聚合酶链反应（RT-qPCR）在临床样本中进行生物标志物验证。
共筛选出118个临床相关且与PCD相关的差异表达基因（DEG），这些基因主要与凋亡相关途径有关，其中鉴定出六个生物标志物（细胞周期蛋白B1（CCNB1）、细胞周期蛋白依赖性激酶1（CDK1）、干扰素调节因子4（IRF4）、脂磷壁酸（LTA）、基质金属肽酶9（MMP9）和抑瘤素M（OSM））。通过受试者工作特征（ROC）曲线分析确定了生物标志物的优异或良好诊断性能。这些生物标志物与临床指标（如年龄、性别和重症监护病房（ICU）入院情况）显示出不同的相关性。COVID-19和对照之间共有14种免疫细胞表现出差异浸润。生物标志物与免疫细胞在不同水平上相关。COVID-19被分为三个簇，其显示出生物标志物基因的差异表达以及与临床信息（如性别、年龄和ICU入院情况）的显著关联。相对于对照，在COVID-19患者中确定了生物标志物的DEG。
六个生物标志物（CCNB1、CDK1、IRF4、LTA、MMP9和OSM）可作为COVID-19治疗和预防的生物标志物。
