Data_Sheet_1_Machine learning models identify ferroptosis-related genes as potential diagnostic biomarkers for Alzheimer’s disease.CSV

Alzheimer’s disease (AD) is a complex, and multifactorial neurodegenerative disease. Previous studies have revealed that oxidative stress, synaptic toxicity, autophagy, and neuroinflammation play crucial roles in the progress of AD, however, its pathogenesis is still unclear. Recent researches have indicated that ferroptosis, an iron-dependent programmed cell death, might be involved in the pathogenesis of AD. Therefore, we aim to screen correlative ferroptosis-related genes (FRGs) in the progress of AD to clarify insights into the diagnostic value. Interestingly, we identified eight FRGs were significantly differentially expressed in AD patients. 10,044 differentially expressed genes (DEGs) were finally identified by differential expression analysis. The following step was investigating the function of DEGs using gene set enrichment analysis (GSEA). Weight gene correlation analysis was performed to explore ten modules and 104 hub genes. Subsequently, based on machine learning algorithms, we constructed diagnostic classifiers to select characteristic genes. Through the multivariable logistic regression analysis, five features (RAF1, NFKBIA, MOV10L1, IQGAP1, FOXO1) were then validated, which composed a diagnostic model of AD. Thus, our findings not only developed genetic diagnostics strategy, but set a direction for further study of the disease pathogenesis and therapy targets.

阿尔茨海默病（Alzheimer’s disease, AD）是一类复杂且多因素共同参与的神经退行性疾病。既往研究已证实，氧化应激、突触毒性、自噬与神经炎症在AD的疾病进展中发挥关键作用，但其具体发病机制至今尚未阐明。近期研究表明，铁死亡（ferroptosis）——一种铁依赖性程序性细胞死亡——可能参与AD的发病过程。为此，本研究旨在筛选AD进展过程中相关的铁死亡相关基因（ferroptosis-related genes, FRGs），以明确其在AD诊断中的潜在价值。值得注意的是，本研究发现8个铁死亡相关基因在AD患者中呈显著差异表达。本研究通过差异表达分析最终筛选得到10044个差异表达基因（differentially expressed genes, DEGs）；随后采用基因集富集分析（gene set enrichment analysis, GSEA）对上述差异表达基因进行功能富集解析；通过加权基因共表达网络分析，筛选得到10个基因模块及104个核心基因。后续基于机器学习算法构建诊断分类器以筛选特征基因；经多变量logistic回归分析验证，最终确定RAF1、NFKBIA、MOV10L1、IQGAP1、FOXO1共5个特征基因，以此构建AD诊断模型。本研究结果不仅建立了AD的遗传诊断策略，同时为该疾病的发病机制研究与治疗靶点开发指明了新的研究方向。