Associations of ARHGAP26 Polymorphisms with Alzheimer’s Disease and Cardiovascular Disease

Wang, Kesheng; Lu, Yongke; Morrow, Deana F.; Xiao, Danqing; Chun, Xu

doi:10.1007/s12031-022-01972-5

Cited by 8 publications

(2 citation statements)

References 76 publications

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“…Among the eight methylation-driven signature genes, ARHGAP26 is a GTPase-activating protein and inhibits the activity of Rho GTPases to affect tumorigenesis and progression of various tumors ( Zhang et al, 2022 ). Recently, it was reported to be significantly associated with neuropsychiatric diseases and neurodegenerative diseases, including PD ( Wang et al, 2022 ). SLC22A1 facilitates the transport, distribution, and elimination of levodopa, which is significantly associated with the occurrence of adverse events of dopaminergic treatment in PD ( Redenšek et al, 2019 ).…”

Section: Discussionmentioning

confidence: 99%

Integrative analysis of DNA methylation and gene expression data for the diagnosis and underlying mechanism of Parkinson’s disease

Ding

Liang²,

Guo³

et al. 2022

Front. Aging Neurosci.

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BackgroundParkinson’s disease (PD) is the second most common progressive neurodegenerative disorder and the leading cause of disability in the daily activities. In the management of PD, accurate and specific biomarkers in blood for the early diagnosis of PD are urgently needed. DNA methylation is one of the main epigenetic mechanisms and associated with the gene expression and disease initiation of PD. We aimed to construct a methylation signature for the diagnosis of PD patients, and explore the potential value of DNA methylation in therapeutic options.Materials and methodsWhole blood DNA methylation and gene expression data of PD patients as well as healthy controls were extracted from Gene Expression Omnibus database. Next, differentially expressed genes (DEGs) and differentially methylated genes (DMGs) between PD patients and healthy controls were identified. Least absolute shrinkage and selection operator cox regression analysis was carried out to construct a diagnostic signature based on the overlapped genes. And, the receiver operating characteristic (ROC) curves were drawn and the area under the curve (AUC) was used to assess the diagnostic performance of the signature in both the training and testing datasets. Finally, gene ontology and gene set enrichment analysis were subsequently carried out to explore the underlying mechanisms.ResultsWe obtained a total of 9,596 DMGs, 1,058 DEGs, and 237 overlapped genes in the whole blood between PD patients and healthy controls. Eight methylation-driven genes (HIST1H4L, CDC42EP3, KIT, GNLY, SLC22A1, GCM1, INO80B, and ARHGAP26) were identified to construct the gene expression signature. The AUCs in predicting PD patients were 0.84 and 0.76 in training dataset and testing dataset, respectively. Additionally, eight methylation-altered CpGs were also identified to construct the CpGs signature which showed a similarly robust diagnostic capability, with AUCs of 0.8 and 0.73 in training dataset and testing dataset, respectively.ConclusionWe conducted an integrated analysis of the gene expression and DNA methylation data, and constructed a methylation-driven genes signature and a methylation-altered CpGs signature to distinguish the patients with PD from healthy controls. Both of them had a robust prediction power and provide a new insight into personalized diagnostic and therapeutic strategies for PD.

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Section: Discussionmentioning

confidence: 99%

Integrative analysis of DNA methylation and gene expression data for the diagnosis and underlying mechanism of Parkinson’s disease

Ding

Liang²,

Guo³

et al. 2022

Front. Aging Neurosci.

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“…The 1 st discovered genetic variable is corresponded to apoB gene, which can influence serum concentration [53] in Alzheimer's disease [54]. The 2 nd one is associated with ARHGAP10 gene (also called GRAF2), which is an important paralogon of ARHGAP26 that closely related to the Alzheimer's disease [55] and affects the developmentally regulated expression of the GRAF proteins that promote lipid droplet clustering and growth, and is enriched at lipid droplet junctions [54], [56]. The 3 rd SNP GNPDA2 is discovered to show the environment and gene association with obesity.…”

Section: Human Alzheimer Diseasementioning

confidence: 99%

Sparse Variable Selection on High Dimensional Heterogeneous Data With Tree Structured Responses

Liu,

Diao

et al. 2024

IEEE Access

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We consider the problem of sparse variable selection on high dimension heterogeneous data sets, which has been taking on renewed interest recently due to the growth of biological and medical data sets with complex, non-i.i.d. structures and huge quantities of response variables. The heterogeneity is likely to confound the association between explanatory variables and responses, resulting in enormous false discoveries when Lasso or its variants are naïvely applied. Therefore, developing effective confounder correction methods is a growing heat point among researchers. However, ordinarily employing recent confounder correction methods will result in undesirable performance due to the ignorance of the convoluted interdependency among response variables. To fully improve current variable selection methods, we introduce a model, the tree-guided sparse linear mixed model, that can utilize the dependency information from multiple responses to explore how specifically clusters are and select the active variables from heterogeneous data. Through extensive experiments on synthetic and real data sets, we show that our proposed model outperforms the existing methods and achieves the highest ROC area.

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Screening of immune-related biological markers for aneurysmal subarachnoid hemorrhage based on machine learning approaches

Liu,

Sun,

Hong

et al. 2023

Biochemistry and Biophysics Reports

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Associations of ARHGAP26 Polymorphisms with Alzheimer’s Disease and Cardiovascular Disease

Cited by 8 publications

References 76 publications

Integrative analysis of DNA methylation and gene expression data for the diagnosis and underlying mechanism of Parkinson’s disease

Integrative analysis of DNA methylation and gene expression data for the diagnosis and underlying mechanism of Parkinson’s disease

Sparse Variable Selection on High Dimensional Heterogeneous Data With Tree Structured Responses

Screening of immune-related biological markers for aneurysmal subarachnoid hemorrhage based on machine learning approaches

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