Palmitoylation-related gene ZDHHC22 as a potential diagnostic and immunomodulatory target in Alzheimer’s disease: insights from machine learning analyses and WGCNA
Abstract Background The mechanism of palmitoylation in the pathogenesis of Alzheimer's disease (AD) remains unclear. Methods 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 alg...
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BMC
2025-01-01
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| Series: | European Journal of Medical Research |
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| Online Access: | https://doi.org/10.1186/s40001-025-02277-0 |
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| author | Sanying Mao Xiyao Zhao Lei Wang Yilong Man Kaiyuan Li |
| author_facet | Sanying Mao Xiyao Zhao Lei Wang Yilong Man Kaiyuan Li |
| author_sort | Sanying Mao |
| collection | DOAJ |
| description | Abstract Background The mechanism of palmitoylation in the pathogenesis of Alzheimer's disease (AD) remains unclear. Methods 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. 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. |
| format | Article |
| id | doaj-art-a4142981f51045f5896e6e4f594638fc |
| institution | Kabale University |
| issn | 2047-783X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | BMC |
| record_format | Article |
| series | European Journal of Medical Research |
| spelling | doaj-art-a4142981f51045f5896e6e4f594638fc2025-01-26T12:21:39ZengBMCEuropean Journal of Medical Research2047-783X2025-01-0130111010.1186/s40001-025-02277-0Palmitoylation-related gene ZDHHC22 as a potential diagnostic and immunomodulatory target in Alzheimer’s disease: insights from machine learning analyses and WGCNASanying Mao0Xiyao Zhao1Lei Wang2Yilong Man3Kaiyuan Li4Department of Neurology, The First People’s Hospital of JiandeDepartment of Neurology, The First People’s Hospital of JiandeDepartment of Cardiology, Center Hospital of Shandong First Medical UniversityDepartment of Cardiology, Center Hospital of Shandong First Medical UniversityGraduate School of Dalian Medical University, Dalian Medical UniversityAbstract Background The mechanism of palmitoylation in the pathogenesis of Alzheimer's disease (AD) remains unclear. Methods 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. 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.https://doi.org/10.1186/s40001-025-02277-0Alzheimer’s diseasePalmitoylationMachine learningWeighted gene co-expression network analysisImmunomodulatory |
| spellingShingle | Sanying Mao Xiyao Zhao Lei Wang Yilong Man Kaiyuan Li Palmitoylation-related gene ZDHHC22 as a potential diagnostic and immunomodulatory target in Alzheimer’s disease: insights from machine learning analyses and WGCNA European Journal of Medical Research Alzheimer’s disease Palmitoylation Machine learning Weighted gene co-expression network analysis Immunomodulatory |
| title | Palmitoylation-related gene ZDHHC22 as a potential diagnostic and immunomodulatory target in Alzheimer’s disease: insights from machine learning analyses and WGCNA |
| title_full | Palmitoylation-related gene ZDHHC22 as a potential diagnostic and immunomodulatory target in Alzheimer’s disease: insights from machine learning analyses and WGCNA |
| title_fullStr | Palmitoylation-related gene ZDHHC22 as a potential diagnostic and immunomodulatory target in Alzheimer’s disease: insights from machine learning analyses and WGCNA |
| title_full_unstemmed | Palmitoylation-related gene ZDHHC22 as a potential diagnostic and immunomodulatory target in Alzheimer’s disease: insights from machine learning analyses and WGCNA |
| title_short | Palmitoylation-related gene ZDHHC22 as a potential diagnostic and immunomodulatory target in Alzheimer’s disease: insights from machine learning analyses and WGCNA |
| title_sort | palmitoylation related gene zdhhc22 as a potential diagnostic and immunomodulatory target in alzheimer s disease insights from machine learning analyses and wgcna |
| topic | Alzheimer’s disease Palmitoylation Machine learning Weighted gene co-expression network analysis Immunomodulatory |
| url | https://doi.org/10.1186/s40001-025-02277-0 |
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