ADAP2

Last update 08 May 2025

Basic Info

Synonyms

ADAP2, Arf-GAP with dual PH domain-containing protein 2, ArfGAP with dual PH domains 2

+ [3]

Introduction

GTPase-activating protein for the ADP ribosylation factor family (Potential). Binds phosphatidylinositol 3,4,5-trisphosphate (PtdInsP3) and inositol 1,3,4,5-tetrakisphosphate (InsP4). Possesses a stoichiometry of two binding sites for InsP4 with identical affinity.

100 Clinical Results associated with ADAP2

100 Translational Medicine associated with ADAP2

0 Patents (Medical) associated with ADAP2

479

Literatures (Medical) associated with ADAP2

01 Apr 2025·Journal of Diabetes

Association of Lifestyle‐Induced Weight Loss With Gene Expression in Subcutaneous Adipose Tissue in Metabolic Syndrome

Article

Author: Keller, Maria ; Blüher, Matthias ; Biemann, Ronald ; Ghosh, Adhideb ; Böttcher, Yvonne ; Isermann, Berend ; Biemann, Kathleen ; Martin, Hugo N. G. ; Struck, Manuel Florian ; Landgraf, Kathrin ; Hoffmann, Anne ; Noé, Falko ; Roomp, Kirsten ; Mathew, Akash ; Zimmermann, Silke ; Körner, Antje ; Schneider, Jochen G. ; Meyer, Hans‐Jonas ; Wolfrum, Christian

ABSTRACTAimsLifestyle‐induced weight loss (LIWL) is considered an effective therapy for the treatment of metabolic syndrome (MetS). The role of differentially expressed genes (DEGs) in adipose tissue function and in the success of LIWL in MetS is still unclear. We investigated the effect of 6 months of LIWL on transcriptional regulation in subcutaneous adipose tissue (SAT). Aiming to identify a LIWL‐associated “gene signature” in SAT, DEGs were fitted into a linear regression model.Materials and MethodsThe study is embedded in a prospective, two‐arm, controlled, monocentric, randomized, 6‐month interventional trial in individuals with MetS following LIWL. The trial included 43 nonsmoking, nondiabetic men aged 45–55 years with MetS.ResultsIn total, we identified 642 DEGs in SAT after 6 months of LIWL. The identified DEGs were validated in two cross‐sectional cohorts analyzing SAT from individuals with and without obesity. Gene enrichment analysis of the DEGs revealed the strongest association with cholesterol metabolic processes. Accordingly, DEGs were correlated with the lipid parameters HDL cholesterol, LDL cholesterol, and triglycerides in corresponding serum samples. We identified 3 genes with an AUC of 0.963 (95% CI: 0.906–1.0) associated with a loss of more than 10% of initial body weight that was maintained for at least 12 months after LIWL, namely SUMO3 (Small ubiquitin‐related modifier 3), PRKG2 (Protein Kinase CGMP‐Dependent 2), and ADAP2 (ArfGAP with Dual PH Domains 2).ConclusionIn summary, we have identified DEGs in SAT after LIWL, which may play an important role in metabolic functions. In particular, altered gene expression in SAT may predict sustained weight loss.

01 Dec 2024·International Immunopharmacology

Identification of a nomogram predicting overall survival based on ADAP2-related apoptosis genes in gliomas

Article

Author: Niu, Zihui ; Yu, Dong ; Zhang, Kang ; Wang, Jiajun ; Wang, Chengcheng ; Zhang, Chao ; Hou, Shiqiang ; Wang, Shuai ; Lin, Ning

BACKGROUNDApoptosis continues to be a pivotal area of investigation in glioma research. ADAP2 mediates the malignant progression of gliomas through the inhibition of apoptosis and predicts the overall survival(OS) of glioma patients based on prognostic modeling of the apoptotic gene set.METHODSThe study encompassed 686 glioma patients, with 413 allocated to the training group and 273 to the validation group. Differential expression of ADAP2 across various glioma subtypes was assessed through bioinformatics analysis and Western blotting. The correlation between ADAP2 and apoptosis was examined using Gene Set Enrichment Analysis (GSEA). Multivariate Cox regression analysis and LASSO dimension reduction analysis were employed to identify apoptosis-related genes with prognostic significance in glioma patients and to construct a nomogram. Biological functions and mechanisms associated with risk scores were explored via Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and GSEA analyses, with validation through Western blotting, flow cytometry, and AM/PI staining.RESULTSADAP2 was found to be enriched in more aggressive glioma subtypes and was closely linked to glioma cell apoptosis, modulating this process via the NF-κB and P53 signaling pathway. A nomogram for OS in glioma patients was constructed using thirteen apoptosis-related genes. Additionally, ROC curves, calibration curves, and C-indices confirmed the robust applicability of the nomogram.CONCLUSIONADAP2 functions as a prognostic biomarker for glioma patients, regulating glioma cell apoptosis through the NF-κB and P53 signaling pathway. Moreover, prognostic models based on apoptosis-related genes can accurately predict OS for glioma patients at 1, 2, 3, 5, and 10 years.

01 Dec 2024·Iranian biomedical journal

25th National and 11th International Annual Congress on Research and Technology of Iranian Medical Sciences Students, Urmia, Iran, 5-7 September, 2024

Article

Author: Avan, Amir ; Azinfar, Anahita ; Khazaei, Yeganeh ; Ibrahim Alshakarchi, Hawraa ; A Ferns, Gordon ; Khooei, Alireza ; Aliakbarian, Mohsen ; Sardarzadeh, Newsha

IntroductionPancreatic ductal adenocarcinoma (PDAC) is associated with an inferior prognosis. Therefore, a focus has been on identifying new biomarkers for early diagnosis and predicting patient survival. Recent studies on the entire human genome have pinpointed prevalent genetic alterations on chromosome 9p21 that heighten the vulnerability to cancer. This study aimed to examine the potential partnership of a genetic variant, rs1333049, in CDKN2A in patients diagnosed with pancreatic cancer. Moreover, genome-wide RNA and microRNA sequencing, bioinformatics, and machine learning approaches were undertaken to identify differentially expressed genes (DEGs), followed by validation in an additional PDAC patient cohort.Methods and MaterialsTo identify DEGs, genome RNA sequencing and clinical data from pancreatic cancer patients were extracted from the Cancer Genome Atlas Database. We used Kaplan-Meier analysis of survival curves to assess prognostic biomarkers. Ensemble learning, Random forest, Max-voting, Adaboost, Gradient Boosting Machines (GBM), and Extreme Gradient Boosting were used, and GBM was selected with 100% accuracy for analysis. Additionally, protein-protein interaction, molecular pathways, concomitant expression of DEGs, and correlations between DEGs and clinical data were analyzed. We also evaluated candidate genes and miRNAs, and a combination of these was obtained from machine learning algorithms and survival analysis. The process involved extracting DNA samples from these individuals and utilizing TaqMan real-time PCR for genotyping and gene expression analysis. Logistic regression was employed to evaluate the relationship between risk and genotypes, and all prognostic factors of significance identified in the univariate evaluation were incorporated into the multivariate evaluation.ResultsThe machine learning results identified 23 and 5 genes and 7 and 20 microRNAs with negative and positive regulation in PDAC, respectively. Essential genes BMF, FRMD4A, ADAP2, PPP1R17, and CACNG3 had the highest coefficient in the advanced stages of the disease. In addition, the survival analysis showed decreased expression of hsa.miR.642a, has.mir.363, CD22, BTNL9, and CTSW and overexpression of hsa.miR.153.1, and hsa.miR.539, hsa.miR, which was associated with a reduced survival rate of 412 patients. Compared to the patients with PDAC, the control group had a decreased occurrence of a TT genotype at the rs1333049 locus. The latter group of cases with a recessive genetic pattern (GG vs. GC+ CC) showed enhanced susceptibility to promoting PDAC (OR = 1.7; 95% CI: 1.2-2.9; p = 0.04).Conclusion and DiscussionOur findings indicate that genetic variation in CDKN2A was linked to the susceptibility of extending PDAC, suggesting the need for additional research in a broader, multi-center context to approve the possible significance of this gene as a novel indicator for the stratification of PDAC.

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