Zhang Yanpeng, Sun Jingyang, Lin Yihan, Jiang Rongxuan, Dong Niuniu, Dong Huanhuan, Li Peng, Feng Jinteng, Zhu Zijiang, Zhang Guangjian
Department of Thoracic Surgery, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, China.
Key Laboratory of Enhanced Recovery After Surgery of Integrated Chinese and Western Medicine, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, China.
Front Pharmacol. 2025 Jun 5;16:1545111. doi: 10.3389/fphar.2025.1545111. eCollection 2025.
This study aims to explore potential ischemia-reperfusion injury (IRI) predictive biomarkers related to disulfidptosis following lung transplantation.
The study utilized datasets from the GEO database, specifically GSE145989 and GSE127003, which include samples of lung cold ischemia and reperfusion following transplantation. Differential expressed analysis and functional enrichment analysis were conducted to identify key genes associated with lung transplant IRI. Multiple machine learning algorithms (Generalized Linear Model, Support Vector Machine, and Random Forest) were applied for joint screening, leading to the construction of a predictive model. The CIBERSORT method was used to assess the infiltration levels of immune cells in lung tissue samples post-transplant. Finally, cell line and animal experiments were carried out to validate the effectiveness and applicability of the model.
A total of 14,592 hub differential-expressed genes were identified, showing significant changes in cold ischemia and reperfusion samples. Using the three machine learning algorithms for joint analysis, a predictive model composed of SLC7A11 and LRPPRC was constructed. This model demonstrated excellent predictive efficacy across multiple datasets, with area under the curve (AUC) values of 0.742 and 0.938, respectively. Additionally, significant differences in neutrophils and macrophages were observed in lung transplant cold ischemia and reperfusion samples. Based on the differential genes associated with disulfidptosis and utilizing the CMap database, we identified two potential drugs targeting IRI: olanzapine and vortioxetine. Ultimately, cell line and animal experiments validated the predictive model's reliability and potential clinical value, revealing that disulfidptosis presents in IRI, and high SLC7A11 expression promotes IRI, while low LRPPRC expression contributes to its occurrence.
SLC7A11 and LRPPRC can serve as predictive biomarkers for IRI following lung transplantation.
本研究旨在探索肺移植后与双硫死亡相关的潜在缺血再灌注损伤（IRI）预测生物标志物。
本研究利用了来自基因表达综合数据库（GEO数据库）的数据集，具体为GSE145989和GSE127003，其中包括肺移植后冷缺血和再灌注的样本。进行差异表达分析和功能富集分析，以鉴定与肺移植IRI相关的关键基因。应用多种机器学习算法（广义线性模型、支持向量机和随机森林）进行联合筛选，从而构建预测模型。采用CIBERSORT方法评估移植后肺组织样本中免疫细胞的浸润水平。最后，进行细胞系和动物实验以验证该模型的有效性和适用性。
共鉴定出14592个枢纽差异表达基因，在冷缺血和再灌注样本中显示出显著变化。使用这三种机器学习算法进行联合分析，构建了一个由溶质载体家族7成员11（SLC7A11）和亮氨酸丰富的五肽重复序列蛋白（LRPPRC）组成的预测模型。该模型在多个数据集中均表现出优异的预测效能，曲线下面积（AUC）值分别为0.742和0.938。此外，在肺移植冷缺血和再灌注样本中观察到中性粒细胞和巨噬细胞存在显著差异。基于与双硫死亡相关的差异基因并利用连通图（CMap）数据库，我们鉴定出两种靶向IRI的潜在药物：奥氮平和伏硫西汀。最终，细胞系和动物实验验证了预测模型的可靠性和潜在临床价值，揭示双硫死亡存在于IRI中，高SLC7A11表达促进IRI，而低LRPPRC表达促成其发生。
SLC7A11和LRPPRC可作为肺移植后IRI的预测生物标志物。
