目的：通过整合生物信息学与机器学习方法，系统筛选环境污染物全氟辛烷磺酸（perfluorooctane sul
fonate，PFOS）与阿尔茨海默病（Alzheimer's Disease，AD）关联的核心基因，探索其作为诊断和预后生物标志
物的潜力。方法：从GEO数据库获取GSE95587数据集，使用limma包进行差异表达分析，将差异表达基因
与 CTD 数据库中 PFOS 相关基因取交集，获得共同候选基因。通过 clusterProfiler 对交集基因进行 GO 和
KEGG富集分析。采用LASSO回归筛选核心基因，并构建逻辑回归模型评估其诊断效能及预测Braak评分
进展的能力。结果：共鉴定281个AD差异表达基因，其中18个与PFOS相关基因重叠。富集分析显示这些
基因显著参与神经炎症、星形胶质细胞活化及JAK-STAT等信号通路。LASSO回归筛选出5个关键基因
（MIR338、CCDC198、MMP13、FGF12、IL1β）。表达分析显示它们在AD组与对照组中存在显著差异。五基
因特征区分AD与对照的AUC达0.800，PCA显示两组样本明显分离。该特征预测Braak评分进展（Braak≥
4）的AUC为0.746，高危组疾病进展风险显著高于低危组。结论：PFOS可能通过调控神经炎症及胶质细胞
功能相关通路参与AD进程。筛选出的五基因特征在AD诊断和预后预测中表现出良好性能。
By integrating bioinformatics and machine learning methods, we aimed to
systematically screen core genes linking the environmental pollutant perfluorooctane sulfonate (PFOS) with
Alzheimer's Disease (AD) and explore their potential as diagnostic and prognostic biomarkers. Methods: The
GSE95587 dataset was obtained from the GEO database, and differential expression analysis was performed
using the limma package. The differentially expressed genes were intersected with PFOS-related genes from the
CTD database to identify common candidate genes. GO and KEGG enrichment analyses of the intersected genes
were conducted using clusterProfiler. LASSO regression was employed to screen for core genes, and a logistic
regression model was constructed to evaluate their diagnostic performance and ability to predict the progression
of Braak staging. Results: A total of 281 differentially expressed genes in AD were identified, of which 18
overlapped with PFOS-related genes. Enrichment analysis revealed that these genes were significantly involved
in pathways such as neuroinflammation, astrocyte activation, and the JAK-STAT signaling pathway. LASSO
regression identified five key genes (MIR338, CCDC198, MMP13, FGF12, IL1β). Expression analysis showed
significant differences between the AD and control groups for these genes. The five-gene signature achieved an
AUC of 0.800 in distinguishing AD from controls, and PCA demonstrated clear separation between the two
groups. The signature predicted the progression of Braak staging (Braak≥4) with an AUC of 0.746, and the
high-risk group exhibited a significantly higher risk of disease progression than the low-risk group.
Conclusion: PFOS may participate in the progression of AD by regulating pathways related to
neuroinflammation and glial cell function. The identified five-gene signature demonstrated good performance in
AD diagnosis and prognosis prediction.
PDF(2267 KB)
