急性肺损伤的早期预警信号研究  

Study on Early Warning Signals of Acute Lung Injury

作　　者：王丽萍 唐旭清[1] WANG Li-ping;TANG Xu-qing(School of Science,Jiangnan University,Wuxi 214122,Jiangsu,China)

机构地区：[1]江南大学理学院,中国江苏无锡214122

出　　处：《生命科学研究》2021年第6期532-539,共8页Life Science Research

基　　金：国家自然科学基金资助项目(11371174)。

摘　　要：急性肺损伤是临床上常见的健康问题,具有较高的发病率和死亡率。识别疾病恶化的关键点及鉴定生物标志物对于有效的治疗至关重要,疾病的分子生物标志物一般依据分子表达水平的差异来获得,以区分疾病的正常状态和疾病状态,即只能用于疾病的诊断而不是预测。本文基于暴露在光气和空气中的小鼠急性肺损伤生物数据,采用单样本动态网络生物标志物的方法,构建了急性肺损伤的早期预警信号指标,以确定该疾病的临界点及相关的单样本特异性动态网络生物标志物。临界状态的基因功能分析和PPI网络分析表明:所获得的生物标志物与细胞衰老、凋亡、炎症反应等相关。通过MCC算法筛选出最大团中心性排名前10的关键基因并展示了其热图分布,结果显示它们在疾病进程中起着正调控的作用,并与细胞增殖、应激反应、癌症进展和肺纤维化等相关,进一步验证了该方法的有效性。Acute lung injury is a common clinical health problem with high morbidity and mortality.It is very important to identify the key points of disease deterioration and biomarkers for effective treatment.Molecular biomarkers of diseases are generally obtained according to differences in molecular expression levels,by which normal and disease states are distinguished.They therefore can only be used for disease diagnosis rather than for prediction.Herein,based on the biological data of acute lung injury in mice exposed to phosgene and air,an early warning signal index of acute lung injury was constructed by using the method of single-sample dynamic network biomarkers to determine the critical point of the disease and related singlesample specific dynamic network biomarkers.Gene function and PPI network analyses of the critical state showed that the obtained biomarkers are related to cell senescence,apoptosis,inflammation,etc.The MCC algorithm was used to screen the top 10 key genes with the largest cluster centrality and their heat map distributions were shown.It was found that they play a positive role in the disease process and are related to cell proliferation,stress response,cancer progression and pulmonary fibrosis,which further verifies the effectiveness of this method.

关 键 词：动态网络 生物标志物 急性肺损伤(ALI) 临界状态 早期预警信号 

分 类 号：O29[理学—应用数学] Q332[理学—数学]