anti-WDR77 / MEP50 (3F10)
- Known as:
- (anti-) to-WDR77 / MEP50 (3F10)
- Catalog number:
- lf-ma42047
- Product Quantity:
- USD
- Category:
- -
- Supplier:
- Abfrontier
- Gene target:
- anti-WDR77 / MEP50 (3F10)
Related genes to: anti-WDR77 / MEP50 (3F10)
- Gene:
- WDR77 NIH gene
- Name:
- WD repeat domain 77
- Previous symbol:
- -
- Synonyms:
- MEP50, p44
- Chromosome:
- 1p13.2
- Locus Type:
- gene with protein product
- Date approved:
- 2005-06-27
- Date modifiied:
- 2015-08-11
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- Cancer cells acquire distinct metabolic and signaling dependencies driven by oncogenic mutations. Defining these mutation-specific liabilities can uncover therapeutic opportunities. Here, we identify dihydroorotate dehydrogenase (DHODH) as a selective metabolic dependency in PIK3CA-mutant colorectal cancer (CRC). DHODH sustains WDR77 O-GlcNAcylation and protein stability by promoting the generation of uridine diphosphate (UDP)-N-acetylglucosamine (UDP-GlcNAc), thereby maintaining PI3K-AKT signaling. Genetic or pharmacological inhibition of DHODH reduces WDR77 protein abundance, decreases phosphorylated AKT, and impairs cancer cell self-renewal and tumor initiation. Uridine supplementation restores WDR77 and AKT signaling, whereas O-GlcNAc transferase (OGT) depletion abrogates this rescue, establishing a UDP-dependent mechanism linking pyrimidine metabolism to signaling maintenance. Treatment with the DHODH inhibitor HL6 recapitulates the genetic phenotypes and suppresses tumor growth in xenograft, orthotopic, and patient-derived CRC models. This study demonstrates that the metabolic regulation of protein stability represents a critical mechanism underlying oncogene-specific dependencies in CRC. - Source: PubMed
Publication date: 2026/07/24
Liang JunLiu XiNi ZiyunZhang SenHe XiangliWang LiliHuang YukunXu ZhaominXu ZhongyuQian JiayiLu LixueAbuduaini NaijipuTian JingZeng MinghuaWang QiSun JingXie QiongWang YonghuiLu WeiqiangFeng BoHan BoHuang Jin - Bisphenol A (BPA) is an endocrine-disrupting chemical, and long-term low-level exposure is closely associated with ovarian cancer (OC). However, the underlying molecular mechanisms remain unclear. Utilizing the Comparative Toxicogenomics Database (CTD) and the Gene Expression Profiling Interactive Analysis (GEPIA) database, combined with LASSO Cox regression, we constructed a BPA exposure-associated OC risk prognosis model composed of , , , , and . Functional analysis indicated that the risk model and its constituent genes are associated with humoral immune activation and cellular immunosuppression. Furthermore, the activation of neuroactive ligand-receptor interaction signaling appears to be a common molecular mechanism through which these risk genes contribute to OC. Molecular docking, molecular dynamics simulations, and cellular experiments confirmed a stable binding interaction between BPA and the CDKN1B protein. These findings provide scientific data for a deeper understanding of the molecular mechanisms linking BPA to OC, aiding risk prediction and personalized prevention for exposed individuals. - Source: PubMed
Publication date: 2026/04/15
Geng HaiyanZhu JianjunZhang WentaoLiu Ming - The regulation of the programmed cell death protein 1 (PD-1) gene, PDCD1, has been widely explored at transcription and posttranslational levels in T cell function and tumor immune evasion. However, the mechanism for PDCD1 dysregulation at the posttranscriptional level remains largely unknown. Here, we identify protein arginine methyltransferase 5 (PRMT5) as a RNA binding protein in a methyltransferase activity-independent manner, which promotes PDCD1 decay with WD repeat domain 77 protein (WDR77) and Argonaute2. Furthermore, the type-I IFN/STAT1 pathway transcriptionally activates PRMT5 and WDR77, thus enhancing PRMT5/WDR77 binding on a conserved AU-rich element of PDCD1 3' UTR. Functionally, conditional knockout of either PRMT5 or WDR77 in T cells disrupts T cell effector function and sensitizes the tumors to anti-PD-1 therapy. Clinically, PRMT5 and WDR77 expression in tumor-infiltrating T cells are negatively correlated with PDCD1 expression and renders tumors resistant to PD-1-targeted immunotherapy. Moreover, fludarabine targeting STAT1 in combination with anti-PD-1 has a synergetic effect on suppressing tumor growth in mice. Overall, this study reveals that the RNA binding-dependent function of PRMT5 regulates PDCD1 and T cell effector function with WDR77 and identifies potential combinatorial therapeutic strategies for enhancing antitumor efficacy. - Source: PubMed
Publication date: 2026/02/02
Gu YinminPan YongboPan ChangPang QiangTang ZhantongChen YiwenZang HaojingWang XiaodongHuang ChangZhang QingqingYang FacaiZhu XiaofengZhang YibiZhao XujieGao Shan - Skin cutaneous melanoma (SKCM) is extremely malignant, leading to poor prognosis. Epigenetic dysregulation, particularly histone modifications, contributes to disease progression. However, effective histone-based prognostic biomarkers are still lacking in clinical practice. - Source: PubMed
Publication date: 2025/12/04
Zhang HaoxueTang KeLiu YuyaoLiu Shengxiu - Colorectal cancer (CRC) is one of the most common and lethal types of cancer worldwide. Understanding both the biological and clinical aspects of the patient is essential to uncover the mechanism underlying the prognosis of the disease. However, most current approaches focus primarily on clinical or biological elements, which can limit their ability to capture the full complexity of the prognosis of CRC. This study aims to enhance understanding of the mechanisms of CRC by combining clinical and biological data from CRC patients with machine learning techniques (ML) to explore the importance of features and predict patient survival. First, we performed differential expression analysis and inspected patient survival curves to identify relevant biological features. Then, we applied ML techniques to understand the individual impact of each clinical and biological feature on patient survival. , , and stood out as biological features, while pathological stage, age, new tumor event, lymph node count, and chemotherapy have shown themselves as interesting clinical features. Furthermore, our ML model achieved an accuracy of 89.58% to predict patient survival. The clinical and biological features proposed here in conjunction with ML can improve the interpretation of CRC mechanisms and predict patient survival. - Source: PubMed
Publication date: 2025/12/15
Vieira Lucas MJorge Natasha A NSousa João BSetubal João CStadler Peter FWalter Maria E M T