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Abstract：
The incidence of Alzheimer\'s disease (AD) is rising globally, yet its treatment and prediction of this condition remain challenging due to the complex pathophysiological mechanisms associated with it. Consequently, the objective of the present study was to analyze and characterize the molecular mechanisms underlying ferroptosis‑related genes (FEGs) in the pathogenesis of AD, as well as to construct a prognostic model. The findings will provide new insights for the future diagnosis and treatment of AD. First, the AD dataset GSE33000 from the Gene Expression Omnibus database and the FEGs from FerrDB were obtained. Next, unsupervised cluster analysis was used to obtain the FEGs that were most relevant to AD. Subsequently, enrichment analyses were performed on the FEGs to explore biological functions. Subsequently, the role of these genes in the immune microenvironment was elucidated through CIBERSORT. Then, the optimal machine learning was selected by comparing the performance of different machine learning models. To validate the prediction efficiency, the models were validated using nomograms, calibration curves, decision curve analysis and external datasets. Furthermore, the expression of FEGs between different groups was verified using reverse transcription quantitative PCR and western blot analysis. In AD, alterations in the expression of FEGs affect the aggregation and infiltration of certain immune cells. This indicated that the occurrence of AD is strongly associated with immune infiltration. Finally, the most appropriate machine learning models were selected, and AD diagnostic models and nomograms were built. The present study provided novel insights that enhance understanding with regard to the molecular mechanism of action of FEGs in AD. Moreover, the present study provided biomarkers that may facilitate the diagnosis of AD.
摘要：
阿尔茨海默病(AD)的发病率在全球范围内呈上升趋势,然而，由于与之相关的复杂病理生理机制，其治疗和预测仍具有挑战性。因此,本研究的目的是分析和表征铁凋亡相关基因（FEGs）在AD发病机理中的分子机制，以及构建预后模型。这些发现将为未来AD的诊断和治疗提供新的见解。首先,获得了来自基因表达综合数据库的AD数据集GSE33000和来自FerrDB的FEGs。接下来,无监督聚类分析用于获得与AD最相关的FEGs。随后，对FEGs进行富集分析以探索生物学功能。随后，通过CIBERSORT阐明了这些基因在免疫微环境中的作用。然后，通过比较不同机器学习模型的性能选择最优机器学习。为了验证预测效率，使用列线图对模型进行了验证，校正曲线,决策曲线分析和外部数据集。此外，使用逆转录定量PCR和Westernblot分析验证不同组间FEGs的表达.在AD中,FEGs表达的改变影响某些免疫细胞的聚集和浸润。这表明AD的发生与免疫浸润密切相关。最后,选择了最合适的机器学习模型，建立AD诊断模型和列线图。本研究提供了新的见解，可以增强对FEGs在AD中作用的分子机制的理解。此外，本研究提供了可能有助于AD诊断的生物标志物.
