基于生物信息学分析靶向铜死亡相关基因治疗心源性脑卒中

Bioinformatics analysis of targeting cuproptosis-related genes for the treatment of cardiogenic stroke

目的基于生物信息学和机器学习探究靶向心源性脑卒中的铜死亡相关特征基因,分析特征基因与心源性脑卒中之间的潜在关系。方法通过基因表达综合数据库(Gene expression omnibus,GEO)获取心源性脑卒中患者的相关数据集,基于该数据集对铜死亡相关基因进行差异性分析,采用聚类分析和机器学习筛选铜死亡特征基因,通过接收者操作特征曲线验证特征基因的准确性和构建疾病预测模型以预测心源性脑卒中患病的风险;利用免疫细胞浸润分析和基因集富集分析探究疾病和铜死亡亚型与免疫微环境的关系及铜死亡特征基因与免疫细胞的相关性和生理病理学过程。结果共筛选出3个铜死亡差异特征基因分别为丙酮酸脱氢酶E1亚基alpha 1(Pyruvate dehydrogenase E1 subunit alpha 1,PDHA1)、二氢硫辛酰胺脱氢酶(Dihydrolipoamide dehydrogenase,DLD)、溶质载体家族31成员1(Solute carrier family 31 member 1,SLC31A1),3个特征基因表现出良好的准确性,并且其构建的疾病预测模型对心源性脑卒中的患病风险有良好的预测性。免疫浸润分析发现,心源性脑卒中与巨噬细胞、肥大细胞具有较强的相关性,3个特征基因同样与巨噬细胞、肥大细胞有一定的相关性,并且与嗜酸性粒细胞、中性粒细胞和γδT细胞有很强的相关性。基因集富集分析表明,铜死亡主要与免疫炎症反应、三羧酸循环、丙酮酸代谢和神经营养因子信号传导通路集ErbB受体酪氨酸激酶家族(ErbB receptor tyrosine kinase family,ERBB)信号通路等密切相关。结论铜死亡特征基因PDHA1、DLD、SLC31A1和心源性脑卒中与免疫微环境之间存在很强的相关性,为心源性脑卒中的病理机制提供了依据,并为靶向药物的开发提供了方向,对心源性脑卒中的认识和治疗具有重要意义。

Objective This study utilized bioinformatics and machine learning techniques to investigate the signature genes associated with cuproptosis, specifically those related to cardiogenic stroke. The study also analyzed the potential relationship between these signature genes and cardiogenic stroke.Methods The relevant dataset of cardiogenic stroke patients was obtained from the Gene Expression Omnibus(GEO) database. Differential expression analysis of the genes related to cuproptosis was performed on the basis of this dataset. The cuproptosis-related genes were screened using cluster analysis and machine learning techniques. The accuracy of the signature genes was verified, and a disease prediction model was constructed to predict the risk of cardiogenic stroke using receiver operating characteristic(ROC) curves. Subsequently, immune cell infiltration analysis and gene set enrichment analysis were utilized to investigate the relationships between the disease and different subtypes of cuproptosis, as well as between the disease and the immune microenvironment. This analysis aimed to determine the correlation and physiological processes associated with the relationship between cuproptosis-related genes and immune cells.Results In this study, we identified three cuproptosis-related genes: PDHA1, DLD, and SLC31A1. These signature genes showed high accuracy and were used to construct disease prediction models, which proved to be effective in predicting the risk of cardiogenic stroke. Our immunoinfiltration analysis revealed a significant correlation between cardiogenic stroke and macrophages and mast cells. Furthermore, the three characterized genes(PDHA1, DLD, and SLC31A1) exhibited similar correlations with macrophages and mast cells, as did those with eosinophils, neutrophils, and γδ T cells. Gene set enrichment analysis indicated that the genes related to cuproptosis were primarily associated with the immune-inflammatory response, the tricarboxylic acid cycle, pyruvate metabolism, the neurotrophic factor signaling pathway, and the ERBB signaling pathway.Conclusion The strong correlations between the copper death signature genes PDHA1, DLD, and SLC31A1 and cardiac stroke incidence, as well as their impact on the immune microenvironment, offer valuable insights into the pathomechanisms of cardiac stroke. This correlation also provides a potential avenue for the development of targeted drugs, which holds great significance in advancing our understanding and treatment of cardiac stroke.