Abstract：Abstract: 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 single-sample 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 dis-tributions 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 effec-tiveness of this method.
引用本文:
王丽萍, 唐旭清. 急性肺损伤的早期预警信号研究[J]. 生命科学研究, 2021, 25(6): 532-539. WANG Li-ping, TANG Xu-qing. Study on Early Warning Signals of Acute Lung Injury. Life Science Research, 2021, 25(6): 532-539.
