DataSheet1_Identification of cuproptosis-related biomarkers and analysis of immune infiltration in allograft lung ischemia-reperfusion injury.docx

Background: Allograft lung ischemia-reperfusion injury (ALIRI) is a major cause of early primary graft dysfunction and poor long-term survival after lung transplantation (LTx); however, its pathogenesis has not been fully elucidated. Cell death is a mechanism underlying ALIRI. Cuproptosis is a recently discovered form of programmed cell death. To date, no studies have been conducted on the mechanisms by which cuproptosis-related genes (CRGs) regulate ALIRI. Therefore, we explored the potential biomarkers related to cuproptosis to provide new insights into the treatment of ALIRI.Materials and methods: Datasets containing pre- and post-LTx lung biopsy samples and CRGs were obtained from the GEO database and previous studies. We identified differentially expressed CRGs (DE-CRGs) and performed functional analyses. Biomarker genes were selected using three machine learning algorithms. The ROC curve and logistic regression model (LRM) of these biomarkers were constructed. CIBERSORT was used to calculate the number of infiltrating immune cells pre- and post-LTx, and the correlation between these biomarkers and immune cells was analyzed. A competing endogenous RNA network was constructed using these biomarkers. Finally, the biomarkers were verified in a validation set and a rat LTx model using qRT-PCR and Western blotting.Results: Fifteen DE-CRGs were identified. GO analysis revealed that DE-CRGs were significantly enriched in the mitochondrial acetyl-CoA biosynthetic process from pyruvate, protein lipoylation, the tricarboxylic acid (TCA) cycle, and copper-transporting ATPase activity. KEGG enrichment analysis showed that the DE-CRGs were mainly enriched in metabolic pathways, carbon metabolism, and the TCA cycle. NFE2L2, NLRP3, LIPT1, and MTF1 were identified as potential biomarker genes. The AUC of the ROC curve for each biomarker was greater than 0.8, and the LRM provided an excellent classifier with an AUC of 0.96. These biomarkers were validated in another dataset and a rat LTx model, which exhibited good performance. In the CIBERSORT analysis, differentially expressed immune cells were identified, and the biomarkers were associated with the immune cells.Conclusion:NFE2L2, NLRP3, LIPT1, and MTF1 may serve as predictors of cuproptosis and play an important role in the pathogenesis of cuproptosis in ALIRI.

背景：同种异体肺移植术后缺血再灌注损伤（ALIRI）是早期原发性移植物功能障碍及肺移植术后长期生存率低的主要原因；然而，其发病机制尚未完全阐明。细胞死亡是ALIRI的潜在机制之一。铜死亡是一种近期发现的程序性细胞死亡新形式。截至目前，尚无关于铜死亡相关基因（CRGs）调控ALIRI机制的研究。因此，本研究旨在探索与铜死亡相关的潜在生物标志物，以期为ALIRI的治疗提供新的视角。材料与方法：从GEO数据库及既往研究中获取包含肺移植前后活检样本及CRGs的数据集。我们鉴定了差异表达CRGs（DE-CRGs）并进行了功能分析。采用三种机器学习算法筛选生物标志基因。构建了这些生物标志物的ROC曲线和逻辑回归模型（LRM）。使用CIBERSORT计算肺移植前后浸润免疫细胞的数量，并分析这些生物标志物与免疫细胞之间的相关性。利用这些生物标志物构建竞争性内源RNA网络。最后，通过qRT-PCR和Western blotting在验证集和鼠肺移植模型中验证了这些生物标志物。结果：鉴定出15个DE-CRGs。GO分析显示，DE-CRGs在苹果酸转化为乙酰辅酶A的线粒体生物合成过程、蛋白质脂酰化、三羧酸（TCA）循环以及铜转运ATP酶活性中显著富集。KEGG富集分析表明，DE-CRGs主要富集在代谢途径、碳代谢和TCA循环中。NFE2L2、NLRP3、LIPT1和MTF1被鉴定为潜在的生物标志基因。每个生物标志物的ROC曲线AUC均大于0.8，LRM提供了优异的分类器，其AUC达到0.96。这些生物标志物在另一个数据集和鼠肺移植模型中得到了验证，表现出良好的性能。在CIBERSORT分析中，鉴定出了差异表达的免疫细胞，并且生物标志物与免疫细胞相关。结论：NFE2L2、NLRP3、LIPT1和MTF1可能作为铜死亡的预测因子，在ALIRI铜死亡的发病机制中发挥着重要作用。