Ask about this productRelated genes to: ZDHHC18 antibody
- Gene:
- ZDHHC18 NIH gene
- Name:
- zinc finger DHHC-type containing 18
- Previous symbol:
- -
- Synonyms:
- DKFZp667O2416
- Chromosome:
- 1p36.11
- Locus Type:
- gene with protein product
- Date approved:
- 2003-03-21
- Date modifiied:
- 2016-02-15
Related products to: ZDHHC18 antibody
Related articles to: ZDHHC18 antibody
- Although the acylation modification (AM) significantly influences the development of lung adenocarcinoma (LUAD), the specific mechanisms of acylation modification in this context have not been widely researched. This investigation sought to discover novel therapeutic avenues related to acylation modification for the precision treatment of LUAD. - Source: PubMed
Publication date: 2026/04/07
Xiao QifanBu JianlongLiu MengfengShi LeiQu Changfa - - Source: PubMed
Publication date: 2026/03/06
Guo ABaoLu YaoWang JunJia ZhanKuiYang JinJianNing Xianghui - Clear cell renal cell carcinoma (ccRCC) is a highly aggressive cancer with a poor prognosis. Palmitoylation, a posttranslational modification, plays a key role in regulating cancer progression and immune responses. However, its influence on ccRCC prognosis and immune therapy efficacy remains underexplored. - Source: PubMed
Publication date: 2025/11/29
Zhang DongZhang KeDeng MinghaoMa JiakangZhu JianShen ShuijieXie JianjunChen Chao - This work sought to examine the causal link between palmoyl-protein-modifying genes (ZDHHC family) and epilepsy by Mendelian randomisation (MR), utilising multi-level genomic data. A two-sample MR analysis was performed utilising publicly accessible blood and brain tissue expression quantitative trait locus (eQTL) data as exposure variables and epilepsy genome-wide association study (GWAS) data from the FinnGen as the outcome measure. The major analysis method utilised was inverse variance weighting (IVW), with robustness validation conducted by weighted median and MR-Egger procedures. Subsequently, summary-data-based MR (SMR) analysis confirmed signal colocalization, supplemented by single-cell transcriptomic data (GSE302285) to investigate target gene expression patterns at a cellular granularity. MR analysis indicated that heightened expression of ZDHHC3 (OR = 0.69, 95% CI: 0.57-0.84, p = 0.0002) and ZDHHC20 (OR = 0.88, 95% CI: 0.82-0.94, p = 0.0002) was significantly linked to a decreased risk of epilepsy, while increased expression of ZDHHC8 and ZDHHC18 was associated with an elevated risk. SMR analysis further corroborated the protective roles of ZDHHC3 and ZDHHC20. Layered MR analysis showed that the results are more significant in focal epilepsy. An eQTL study specific to brain cells revealed cell-type specificity in these correlations, with ZDHHC20 demonstrating the most significant protective impact in excitatory neurones (OR = 0.89, p = 0.0273). Single-cell transcriptomics demonstrated that ZDHHC20 was significantly expressed in astrocytes and neurones in the brain tissue of epilepsy patients, while ZDHHC3 was primarily concentrated in neurones. This work genetically confirms that certain palmitoylation genes, notably ZDHHC3 and ZDHHC20, may have causative protective effects against the risk of focal epilepsy, highlighting cell-type-specific processes. This establishes innovative theoretical frameworks for exploring the pathophysiology of epilepsy and formulating targeted treatments. - Source: PubMed
Publication date: 2025/10/16
Qiu JinXian DehaiYang Kaiwen - : Gliomas are complex and heterogeneous brain tumors characterized by an unfavorable clinical course and a fatal prognosis, which can be improved by an early determination of tumor kind. Here, we developed explainable machine learning (ML) models for classifying three major glioma subtypes (astrocytoma, oligodendroglioma, and glioblastoma) and predicting survival rates based on RNA-seq data. : We analyzed publicly available datasets and applied feature selection techniques to identify key biomarkers. Using various ML models, we performed classification and survival analysis to develop robust predictive models. The best-performing models were then interpreted using Shapley additive explanations (SHAP). : Thirteen key genes (, , , , , , , , , , , , and ) proved to be closely associated with glioma subtypes as well as survival. Support Vector Machine (SVM) turned out to be the optimal classification model with the balanced accuracy of 0.816 and the area under the receiver operating characteristic curve (AUC) of 0.896 for the test datasets. The Case-Control Cox regression model (CoxCC) proved best for predicting survival with the Harrell's C-index of 0.809 and 0.8 for the test datasets. Using SHAP we revealed the gene expression influence on the outputs of both models, thus enhancing the transparency of the prediction generation process. : The results indicated that the developed models could serve as a valuable practical tool for clinicians, assisting them in diagnosing and determining optimal treatment strategies for patients with glioma. - Source: PubMed
Publication date: 2025/08/09
Vershinina OlgaTurubanova VictoriaKrivonosov MikhailTrukhanov ArseniyIvanchenko Mikhail