Tian Mei, Shen Jing, Qi Zhiqiang, Feng Yu, Fang Peidi
The Affiliated Jiangsu Shengze Hospital of Nanjing Medical University, Nanjing, China.
Medicine and Health, The University of New South Wales, Kensington, NSW, Australia.
Front Aging Neurosci. 2023 Jul 13;15:1201142. doi: 10.3389/fnagi.2023.1201142. eCollection 2023.
Alzheimer's disease (AD) is a neurodegenerative disease whose origins have not been universally accepted. Numerous studies have demonstrated the relationship between AD and alcohol dependence; however, few studies have combined the origins of AD, alcohol dependence, and programmed cell death (PCD) to analyze the mechanistic relationship between the development of this pair of diseases. We demonstrated in previous studies the relationship between psychiatric disorders and PCD, and in the same concerning neurodegeneration-related AD, we found an interesting link with the Ferroptosis pathway. In the present study, we explored the bioinformatic interactions between AD, alcohol dependence, and Ferroptosis and tried to elucidate and predict the development of AD from this aspect.
We selected the Alzheimer's disease dataset GSE118553 and alcohol dependence dataset GSE44456 from the Gene Expression Omnibus (GEO) database. Ferroptosis-related genes were gathered through Gene Set Enrichment Analysis (GSEA), Kyoto Encyclopedia of Genes and Genomes (KEGG), and relevant literature, resulting in a total of 88 related genes. For the AD and alcohol dependence datasets, we conducted Limma analysis to identify differentially expressed genes (DEGs) and performed functional enrichment analysis on the intersection set. Furthermore, we used ferroptosis-related genes and the DEGs to perform machine learning crossover analysis, employing Least Absolute Shrinkage and Selection Operator (LASSO) regression to identify candidate immune-related central genes. This analysis was also used to construct protein-protein interaction networks (PPI) and artificial neural networks (ANN), as well as to plot receiver operating characteristic (ROC) curves for diagnosing AD and alcohol dependence. We analyzed immune cell infiltration to explore the role of immune cell dysregulation in AD. Subsequently, we conducted consensus clustering analysis of AD using three relevant candidate gene models and examined the immune microenvironment and functional pathways between different subgroups. Finally, we generated a network of gene-gene interactions and miRNA-gene interactions using Networkanalyst.
The crossover of AD and alcohol dependence DEG contains 278 genes, and functional enrichment analysis showed that both AD and alcohol dependence were strongly correlated with Ferroptosis, and then crossed them with Ferroptosis-related genes to obtain seven genes. Three candidate genes were finally identified by machine learning to build a diagnostic prediction model. After validation by ANN and PPI analysis, ROC curves were plotted to assess the diagnostic value of AD and alcohol dependence. The results showed a high diagnostic value of the predictive model. In the immune infiltration analysis, functional metabolism and immune microenvironment of AD patients were significantly associated with Ferroptosis. Finally, analysis of target genes and miRNA-gene interaction networks showed that hsa-mir-34a-5p and has-mir-106b-5p could simultaneously regulate the expression of both CYBB and ACSL4.
We obtained a diagnostic prediction model with good effect by comprehensive analysis, and validation of ROC in AD and alcohol dependence data sets showed good diagnostic, predictive value for both AD (AUC 0. 75, CI 0.91-0.60), and alcohol dependence (AUC 0.81, CI 0.95-0.68). In the consensus clustering grouping, we identified variability in the metabolic and immune microenvironment between subgroups as a likely cause of the different prognosis, which was all related to Ferroptosis function. Finally, we discovered that hsa-mir-34a-5p and has-mir-106b-5p could simultaneously regulate the expression of both CYBB and ACSL4.
阿尔茨海默病（AD）是一种神经退行性疾病，其病因尚未得到普遍认可。众多研究已证实AD与酒精依赖之间的关系；然而，很少有研究将AD的病因、酒精依赖和程序性细胞死亡（PCD）结合起来分析这两种疾病发展之间的机制关系。我们在先前的研究中证明了精神障碍与PCD之间的关系，在同样涉及神经退行性变相关的AD研究中，我们发现了与铁死亡途径的有趣联系。在本研究中，我们探索了AD、酒精依赖和铁死亡之间的生物信息学相互作用，并试图从这方面阐明和预测AD的发展。
我们从基因表达综合数据库（GEO）中选择了阿尔茨海默病数据集GSE118553和酒精依赖数据集GSE44456。通过基因集富集分析（GSEA）、京都基因与基因组百科全书（KEGG）以及相关文献收集铁死亡相关基因，共得到88个相关基因。对于AD和酒精依赖数据集，我们进行了Limma分析以鉴定差异表达基因（DEG），并对交集集进行功能富集分析。此外，我们使用铁死亡相关基因和DEG进行机器学习交叉分析，采用最小绝对收缩和选择算子（LASSO）回归来鉴定候选免疫相关核心基因。该分析还用于构建蛋白质-蛋白质相互作用网络（PPI）和人工神经网络（ANN），以及绘制用于诊断AD和酒精依赖的受试者工作特征（ROC）曲线。我们分析了免疫细胞浸润，以探讨免疫细胞失调在AD中的作用。随后，我们使用三个相关候选基因模型对AD进行共识聚类分析，并检查不同亚组之间的免疫微环境和功能途径。最后，我们使用Networkanalyst生成基因-基因相互作用和miRNA-基因相互作用网络。
AD和酒精依赖DEG的交叉包含278个基因，功能富集分析表明AD和酒精依赖均与铁死亡密切相关，然后将它们与铁死亡相关基因交叉，得到7个基因。最终通过机器学习鉴定出3个候选基因，构建诊断预测模型。经ANN和PPI分析验证后，绘制ROC曲线以评估AD和酒精依赖的诊断价值。结果显示预测模型具有较高的诊断价值。在免疫浸润分析中，AD患者的功能代谢和免疫微环境与铁死亡显著相关。最后，对靶基因和miRNA-基因相互作用网络的分析表明，hsa-mir-34a-5p和has-mir-106b-5p可同时调节CYBB和ACSL4的表达。
我们通过综合分析获得了一个效果良好的诊断预测模型，在AD和酒精依赖数据集中的ROC验证显示，该模型对AD（AUC 为0.75，CI为[0.91, 0.60]）和酒精依赖（AUC为0.81，CI为[0.95, 0.68]）均具有良好的诊断和预测价值。在共识聚类分组中，我们发现亚组之间代谢和免疫微环境的差异可能是不同预后的原因，这均与铁死亡功能相关。最后，我们发现hsa-mir-34a-5p和has-mir-106b-5p可同时调节CYBB和ACSL4的表达。
