Mao Sanying, Zhao Xiyao, Wang Lei, Man Yilong, Li Kaiyuan
Department of Neurology, The First People's Hospital of Jiande, Hangzhou, China.
Department of Cardiology, Center Hospital of Shandong First Medical University, Jinan, China.
Eur J Med Res. 2025 Jan 22;30(1):46. doi: 10.1186/s40001-025-02277-0.
The mechanism of palmitoylation in the pathogenesis of Alzheimer's disease (AD) remains unclear.
This study retrieved AD data sets from the GEO database to identify palmitoylation-associated genes (PRGs). This study applied WGCNA along with three machine learning algorithms-random forest, LASSO regression, and SVM-RFE-to further select key PRGs (KPRGs). The diagnostic performance of KPRGs was evaluated using Receiver Operating Characteristic (ROC) curve analysis. Immune cell infiltration analysis was conducted to assess correlations between KPRGs and immune cell types, and a competing endogenous RNA (ceRNA) regulatory network was constructed to explore their potential regulatory mechanisms.
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.
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.
棕榈酰化在阿尔茨海默病（AD）发病机制中的作用尚不清楚。
本研究从基因表达综合数据库（GEO数据库）中检索AD数据集，以识别与棕榈酰化相关的基因（PRGs）。本研究应用加权基因共表达网络分析（WGCNA）以及三种机器学习算法——随机森林、套索回归和支持向量机递归特征消除法（SVM-RFE）——进一步筛选关键PRGs（KPRGs）。使用受试者工作特征（ROC）曲线分析评估KPRGs的诊断性能。进行免疫细胞浸润分析以评估KPRGs与免疫细胞类型之间的相关性，并构建竞争性内源RNA（ceRNA）调控网络以探索其潜在调控机制。
从AD数据集中鉴定出17个PRGs，其中7个基因表达上调，10个基因表达下调。通过WGCNA和机器学习分析，选择锌指DHHC型棕榈酰转移酶22（ZDHHC22）作为KPRG。ROC曲线分析表明，ZDHHC22的曲线下面积值为0.659，表明其具有中等诊断潜力。免疫细胞浸润分析显示ZDHHC22表达与几种免疫细胞类型的浸润之间存在显著关联，包括幼稚B细胞、CD8 + T细胞和M1巨噬细胞。此外，预测有25个微小RNA（miRNAs）和55个长链非编码RNA（lncRNAs）可能靶向ZDHHC22，形成lncRNA-miRNA-mRNA ceRNA网络的基础。
本研究首次使用生物信息学方法将ZDHHC22鉴定为AD中的关键KPRG，突出了其在疾病诊断和免疫调节中的潜在作用。ZDHHC22的调控网络为AD的分子机制提供了新见解，并为未来的靶向治疗策略奠定了基础。
