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作者

Sanying Mao, Xiyao Zhao, Lei Wang, Yilong Man, Kaiyuan Li

作者单位

Affiliations ¹ Department of Neurology, The First People's Hospital of Jiande, Hangzhou, China. ² Department of Cardiology, Center Hospital of Shandong First Medical University, Jinan, China. ³ Department of Cardiology, Center Hospital of Shandong First Medical University, Jinan, China. 1036474530@qq.com. ⁴ Graduate School of Dalian Medical University, Dalian Medical University, Dalian, China. 986970919@qq.com.

刊名

European journal of medical research

年份

2025

卷号

Vol.30 No.1

页码

46

ISSN

2047-783X

关键词

Alzheimer’s disease Immunomodulatory Machine learning Palmitoylation Weighted gene co-expression network analysis.

摘要

Background: The mechanism of palmitoylation in the pathogenesis of Alzheimer's disease remains unclear. Methods: This study retrieved AD data sets from the GEO database to identify palmitoylation-associated genes . This study applied WGCNA along with three machine learning algorithms-random forest, LASSO regression, and SVM-RFE-to further select key PRGs . The diagnostic performance of KPRGs was evaluated using Receiver Operating Characteristic curve analysis. Immune cell infiltration analysis...更多

Background: The mechanism of palmitoylation in the pathogenesis of Alzheimer's disease remains unclear. Methods: This study retrieved AD data sets from the GEO database to identify palmitoylation-associated genes . This study applied WGCNA along with three machine learning algorithms-random forest, LASSO regression, and SVM-RFE-to further select key PRGs . The diagnostic performance of KPRGs was evaluated using Receiver Operating Characteristic curve analysis. Immune cell infiltration analysis was conducted to assess correlations between KPRGs and immune cell types, and a competing endogenous RNA regulatory network was constructed to explore their potential regulatory mechanisms. Results: 17 PRGs were identified from the AD data sets, with 7 genes showing increased expression and 10 showing decreased expression. Through WGCNA and machine learning analyses, ZDHHC22 was selected as a KPRG. The ROC curve analysis demonstrated that ZDHHC22 had an area under the curve value of 0.659, indicating moderate diagnostic potential. Immune cell infiltration analysis revealed significant associations between ZDHHC22 expression and the infiltration of several immune cell types, including naïve B cells, CD8 + T cells, and M1 macrophages. In addition, 25 miRNAs and 55 lncRNAs were predicted to potentially target ZDHHC22, forming the basis for a lncRNA-miRNA-mRNA ceRNA network. Conclusions: This study is the first to use bioinformatics methods to identify ZDHHC22 as a key KPRG in AD, highlighting its potential role in disease diagnosis and immune regulation. The regulatory network of ZDHHC22 provides new insights into the molecular mechanisms of AD and lays the foundation for future targeted therapeutic strategies.收起

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