Last update 01 Nov 2024

ATAD2

Last update 01 Nov 2024

Basic Info

Synonyms

AAA nuclear coregulator cancer-associated protein, ANCCA, ATAD2

+ [7]

Introduction

May be a transcriptional coactivator of the nuclear receptor ESR1 required to induce the expression of a subset of estradiol target genes, such as CCND1, MYC and E2F1. May play a role in the recruitment or occupancy of CREBBP at some ESR1 target gene promoters. May be required for histone hyperacetylation. Involved in the estrogen-induced cell proliferation and cell cycle progression of breast cancer cells.

Drugs associated with ATAD2

AZ-13824374

Target

ATAD2

Mechanism

ATAD2 inhibitors

Active Org.

AstraZeneca PLC

Originator Org.

AstraZeneca PLC

Active Indication

Neoplasms

Inactive Indication-

Drug Highest PhasePreclinical

First Approval Ctry. / Loc.-

First Approval Date20 Jan 1800

BAY-850

Target

ATAD2 x BRPF2

Mechanism

ATAD2 inhibitors [+1]

Active Org.-

Originator Org.

Bayer AG

Active Indication-

Inactive Indication

Neoplasms

Drug Highest PhasePending

First Approval Ctry. / Loc.-

First Approval Date20 Jan 1800

100 Clinical Results associated with ATAD2

100 Translational Medicine associated with ATAD2

0 Patents (Medical) associated with ATAD2

177

Literatures (Medical) associated with ATAD2

01 Nov 2024·Gene

Elevated expression of ECT2 as a diagnostic marker and prognostic indicator in endometrial cancer

Article

Author: Wu, Xiang-Guang ; Huang, Si-Yuan ; Chen, Jin-Jiao ; Wang, Wei ; Zhou, Xiao-Xia ; Fan, Liang-Sheng ; Ding, Zi-Ang ; Qiu, Yang-Zhi ; Pan, Yu-Hua ; Zhong, Xiao-Qing ; Wu, Yu

OBJECTIVESThe study aims to investigate genes associated with endometrial cancer (EC) progression to identify new biomarkers for early detection.METHODSDifferentially expressed genes (DEGs), Series test of cluster (STC) and protein-protein interaction analyses identified hub genes in EC. Clinical samples were utilized to examine the expression pattern of ECT2, assess its prognostic value, and evaluate its diagnostic potential.RESULTSUpregulated DEGs were significantly enriched in cancer-related processes and pathways. Validations across databases identified ASPM, ATAD2, BUB1B, ECT2, KIF14, NUF2, NCAPG, and SPAG5 as potential hub genes, with ECT2 exhibiting the highest diagnostic efficacy. The expression levels of ECT2 varied significantly across different clinical stages, pathological grades, and metastasis statuses in UCEC. Furthermore, ECT2 mRNA was upregulated in the p53abn group, indicating a poorer prognosis, and downregulated in the MMRd and NSMP groups, suggesting a moderate prognosis. In clinical samples, ECT2 expression increased from normal endometria and endometrial hyperplasia without atypia (EH) to atypical endometrial hyperplasia (AH) and EC, effectively distinguishing between benign and malignant endometria. High ECT2 expression was associated with an unfavourable prognosis.CONCLUSIONSECT2 expression significantly rises in AH and EC, showing high accuracy in distinguishing between benign and malignant endometria. ECT2 emerges as a promising biomarker for diagnosing endometrial neoplasia and as a prognostic indicator in EC.

07 Jul 2024·Journal of Lasers in Medical Sciences

Assessment of Recovery Time Effects on Human Primary Neonatal Dermal Fibroblasts After Exposure to Solar‐Simulated Ultraviolet Radiation

Article

Author: Rostami Nejad, Mohammad ; Jahani Sherafat, Somayeh ; Arjmand, Babak ; Razi, Farideh ; Rezaei-Tavirani, Mostafa ; Bandarian, Fatemeh

Introduction: Photoaging that is accompanied by gene expression alteration is known as early aging of the skin due to overexposure to natural and/or artificial ultraviolet radiation (UVR). The assessment of gene expression alteration in human primary neonatal dermal fibroblasts depending on recovery time after exposure to solar simulated ultraviolet radiation (ssUVR) is the main aim of this bioinformatic study. Methods: Data are extracted from Gene Expression Omnibus (GEO). The pre-evaluation is done via the GEO2R program. The Significant differentially expressed genes (DEGs) were assessed via protein-protein interaction (PPI) network analysis, and the central genes were identified. The central genes were enriched via gene ontology assessment. Results: Among 224 significant DEGs, 20 central genes including TOP2A, MKI67, BRCA1, HELLS, MAD2L1, ANLN, KIF11, MSH2, KRAS, NCAPG, RFC3, PLK4, WDHD1, BLM, CDKN3, KIF15, SMARCA5, and ATAD2 as hub genes and TOP2A, MKI67, BRCA1, ANLN, KRAS, PLK4, SMARCA5, MMP2, and TLR4 as bottleneck genes were determined. Eight central genes were associated with 16 biological terms. Conclusion: In conclusion, significant differences appeared between gene expression conditions of the cells after 1-day and 5-day recovery. Molecular events include the repair and continuation of photodamages. It is possible to introduce drug targets to prevent the progress of induced damages.

27 May 2024·Journal of Chemical Information and Modeling

PharmaCore: The Automatic Generation of 3D Structure-Based Pharmacophore Models from Protein/Ligand Complexes

Article

Author: Chini, Maria Giovanna ; Lauro, Gianluigi ; Colarusso, Ester ; Bifulco, Giuseppe ; De Vita, Simona

In this work, we present PharmaCore: a new, completely automatic workflow aimed at generating three-dimensional (3D) structure-based pharmacophore models toward any target of interest. The proposed approach relies on using cocrystallized ligands to create the input files for generating the pharmacophore hypotheses, integrating not only the three-dimensional structural information on the ligand but also data concerning the binding mode of these molecules put in the protein cavity. We developed a Python library that, starting from the specific UniProt ID of the protein under investigation as the only element that requires user intervention, subsequently collects and aligns the corresponding structures bearing a known ligand in a fully automated fashion, bringing them all into the same coordinate system. The protocol includes a final phase in which the aligned small molecules are used to produce the pharmacophore hypotheses directly onto the protein structure using a specific software, e.g., Phase (Schrödinger LLC). To validate the entire procedure and highlight the possible applications in the field of drug discovery and repositioning, we first generated pharmacophores for soluble epoxide hydrolase (sEH) and compared with already-published ones. Then, we reproduced the binding profile of a reported selective binder of ATAD2 bromodomain (AM879), testing it against a panel of 1741 pharmacophores related to 16 epigenetic proteins and automatically generated with PharmaCore, finally disclosing putative unprecedented off-targets. The computational predictions were successfully validated with AlphaScreen assays, highlighting the applicability of the proposed workflow in drug discovery and repositioning. Finally, the process was also validated on tankyrase 2 and SARS-CoV-2 M^Pro, confirming the robustness of PharmaCore.

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ATAD2

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