Identification of Neutrophil Extracellular Trap-Related Gene Expression Signatures in Ischemia Reperfusion Injury During Lung Transplantation: A Transcriptome Analysis and Clinical Validation - Gao J; Zhang Z; Yu J; Zhang N; Fu Y; Jiang X; Xia Z; Zhang Q; Wen Z (Dove Medical Press, 2024)
Jiameng Gao,1,2,* Zhiyuan Zhang,1,2,* Jing Yu,1,2,* Nan Zhang,1,2 Yu Fu,1,2 Xuemei Jiang,1,2 Zheyu Xia,3 Qingqing Zhang,1,2 Zongmei Wen1,2 1Department of Anesthesiology, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai, People's Republic of China; 2Shanghai Engineering Research Center of Lung Transplantation, Shanghai, People's Republic of China; 3School of Medicine, Tongji University, Shanghai, People's Republic of China*These authors contributed equally to this workCorrespondence: Zongmei Wen; Qingqing Zhang, Department of Anesthesiology, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, 507 Zhengmin Road, Shanghai, 200433, People's Republic of China, Tel +86 13761635280 ; +86 13764542055, Email wzm1103@126.com; elfe666@163.comPurpose: Ischemia reperfusion injury (IRI) unavoidably occurs during lung transplantation, further contributing to primary graft dysfunction (PGD). Neutrophils are the end effectors of IRI and activated neutrophils release neutrophil extracellular traps (NETs) to further amplify damage. Nevertheless, potential contributions of NETs in IRI remain incompletely understood. This study aimed to explore NET-related gene biomarkers in IRI during lung transplantation.Methods: Differential expression analysis was applied to identify differentially expressed genes (DEGs) for IRI during lung transplantation based on matrix data (GSE145989, 127003) downloaded from GEO database. The CIBERSORT and weighted gene co-expression network analysis (WGCNA) algorithms were utilized to identify key modules associated with neutrophil infiltration. Moreover, the least absolute shrinkage and selection operator regression and random forest were applied to identify potential NET-associated hub genes. Subsequently, the screened hub genes underwent further validation of an external dataset (GSE18995) and nomogram model. Based on clinical peripheral blood samples, immunofluorescence staining and dsDNA quantification were used to assess NET formation, and ELISA was applied to validate the expression of hub genes.Results: Thirty-eight genes resulted from the intersection between 586 DEGs and 75 brown module genes, primarily enriched in leukocyte migration and NETs formation. Subsequently, four candidate hub genes (FCAR, MMP9, PADI4, and S100A12) were screened out via machine learning algorithms. Validation using an external dataset and nomogram model achieved better predictive value. Substantial NETs formation was demonstrated in IRI, with more pronounced NETs observed in patients with PGD ≥ 2. PADI4, S100A12, and MMP9 were all confirmed to be up-regulated after reperfusion through ELISA, with higher levels of S100A12 in PGD ≥ 2 patients compared with non-PGD patients.Conclusion: We identified three potential NET-related biomarkers for IRI that provide new insights into early detection and potential therapeutic targets of IRI and PGD after lung transplantation.Keywords: ischemia reperfusion injury, neutrophil extracellular traps, lung transplantation, primary graft dysfunction, WGCNA, machine learning