Table_2_Identification ferroptosis-related hub genes and diagnostic model in Alzheimer’s disease.XLSX

BackgroundFerroptosis is a newly defined form of programmed cell death and plays an important role in Alzheimer’s disease (AD) pathology. This study aimed to integrate bioinformatics techniques to explore biomarkers to support the correlation between ferroptosis and AD. In addition, further investigation of ferroptosis-related biomarkers was conducted on the transcriptome characteristics in the asymptomatic AD (AsymAD).
MethodsThe microarray datasets GSE118553, GSE132903, GSE33000, and GSE157239 on AD were downloaded from the GEO database. The list of ferroptosis-related genes was extracted from the FerrDb website. Differentially expressed genes (DEGs) were identified by R “limma” package and used to screen ferroptosis-related hub genes. The random forest algorithm was used to construct the diagnostic model through hub genes. The immune cell infiltration was also analyzed by CIBERSORTx. The miRNet and DGIdb database were used to identify microRNAs (miRNAs) and drugs which targeting hub genes.
ResultsWe identified 18 ferroptosis-related hub genes anomalously expressed in AD, and consistent expression trends had been observed in both AsymAD The random forest diagnosis model had good prediction results in both training set (AUC = 0.824) and validation set (AUC = 0.734). Immune cell infiltration was analyzed and the results showed that CD4+ T cells resting memory, macrophages M2 and neutrophils were significantly higher in AD. A significant correlation of hub genes with immune infiltration was observed, such as DDIT4 showed strong positive correlation with CD4+ T cells memory resting and AKR1C2 had positive correlation with Macrophages M2. Additionally, the microRNAs (miRNAs) and drugs which targeting hub genes were screened.
ConclusionThese results suggest that ferroptosis-related hub genes we screened played a part in the pathological progression of AD. We explored the potential of these genes as diagnostic markers and their relevance to immune cells which will help in understanding the development of AD. Targeting miRNAs and drugs provides new research clues for preventing the development of AD.

背景：铁死亡（ferroptosis）是一种新近定义的程序性细胞死亡形式，在阿尔茨海默病（Alzheimer’s disease, AD）的病理进程中发挥重要作用。本研究旨在整合生物信息学技术，探索可佐证铁死亡与阿尔茨海默病之间关联的生物标志物；此外，本研究还针对无症状阿尔茨海默病（asymptomatic AD, AsymAD）的转录组特征，进一步探究铁死亡相关生物标志物。
方法：本研究从GEO数据库（GEO database）下载了阿尔茨海默病相关的微阵列数据集GSE118553、GSE132903、GSE33000及GSE157239；从FerrDb网站获取铁死亡相关基因列表。通过R语言“limma”包筛选差异表达基因（differentially expressed genes, DEGs），并以此挖掘铁死亡相关核心基因（hub genes）。采用随机森林（random forest）算法，基于核心基因构建诊断模型。通过CIBERSORTx分析免疫细胞浸润情况；借助miRNet与DGIdb数据库，筛选靶向核心基因的微小RNA（microRNAs, miRNAs）及药物。
结果：本研究共筛选出18个在阿尔茨海默病中异常表达的铁死亡相关核心基因，且在无症状阿尔茨海默病中也观察到了一致的表达趋势。本研究构建的随机森林诊断模型在训练集（AUC=0.824）与验证集（AUC=0.734）中均表现出良好的预测性能。免疫细胞浸润分析结果显示，阿尔茨海默病患者样本中静息记忆CD4+T细胞、M2型巨噬细胞及中性粒细胞的浸润水平显著升高。核心基因与免疫细胞浸润水平存在显著关联，例如DDIT4与静息记忆CD4+T细胞呈强正相关，AKR1C2与M2型巨噬细胞呈正相关。此外，本研究还筛选出了靶向核心基因的微小RNA及药物。
结论：本研究结果表明，所筛选出的铁死亡相关核心基因参与了阿尔茨海默病的病理进程。本研究探讨了这些基因作为诊断标志物的潜力及其与免疫细胞的相关性，有助于深入理解阿尔茨海默病的发病机制；靶向核心基因的微小RNA及药物研究，可为阿尔茨海默病的预防提供全新的研究线索。