PAICS Antibody (N-term) Blocking Peptides
- Known as:
- PAICS Antibody (N-terminus) Blocking Peptides
- Catalog number:
- BP12945a
- Product Quantity:
- 2
- Category:
- -
- Supplier:
- Abgen
- Gene target:
- PAICS Antibody (N-term) Blocking Peptides
Related genes to: PAICS Antibody (N-term) Blocking Peptides
- Gene:
- PAICS NIH gene
- Name:
- phosphoribosylaminoimidazole carboxylase and phosphoribosylaminoimidazolesuccinocarboxamide synthase
- Previous symbol:
- PAIS
- Synonyms:
- ADE2H1, AIRC
- Chromosome:
- 4q12
- Locus Type:
- gene with protein product
- Date approved:
- 1991-03-11
- Date modifiied:
- 2016-10-13
Related products to: PAICS Antibody (N-term) Blocking Peptides
Related articles to: PAICS Antibody (N-term) Blocking Peptides
- Biallelic variants in PAICS, one of the 10 genes involved in the de novo purine synthesis (DNPS), were originally associated with an extremely rare phenotype characterized by multiple and severe congenital abnormalities, such as polyhydramnios due to esophageal atresia, congenital heart disease, and urogenital anomalies, which were lethal within the first days of life. Vertebral and limb defects, as well as distinctive craniofacial features, were also described. To date, three patients with these clinical characteristics but without neurologic involvement have been described in the literature. More recently, two siblings with none of these features but presenting a severe neurodevelopmental phenotype with regression and ocular involvement have been reported. We describe a new patient with two compound heterozygous variants in the PAICS gene, p.Ser35Phe and p.Cys281Ter, born with multiple congenital malformations overlapping those reported in the originally described patients: esophageal atresia, lung hypoplasia, vertebral anomalies, cryptorchidism, short stature, and dysmorphic facial features. He also had developmental delay; however, epilepsy did not occur. The features presented by our patient and the relatively longer follow up allowed us to detect in a single patient almost all the features previously described in different families. - Source: PubMed
Publication date: 2026/08/08
Monti LuigiIsidori FedericaCapriotti EmidioMinicucci AndreinaMichelangelo BaldazziDi Mitri MarcoScarano EmanuelaLima MarioPippucci TommasoSeveri Giulia - Lung adenocarcinoma (LUAD) is the most common type of lung cancer, often diagnosed in the advanced stage of diffuse metastasis, highly aggressive and rapidly fatal, lacking early diagnostic markers and effective therapeutic targets. Enzyme phosphoribosylaminoimidazole carboxylase/phosphoribosylaminoimidazole succinocarboxamide synthetase (PAICS) is an important bifunctional enzyme in purine de novo synthesis, and rapidly dividing cancer cells are heavily dependent on de novo synthesis of the adenine and guanine pathways. PAICS has been found to be highly expressed in a variety of cancers and has been shown to promote cancer proliferation or metastasis. However, the specific mechanism of action of PAICS in LUAD is unknown. Our study revealed that PAICS mRNA and protein levels were significantly increased in LUAD tumor tissues compared to adjacent normal tissues. Bioinformatics analysis showed that high PAICS expression was associated with LUAD metastasis and could be used as a factor for diagnosis and assessment of prognosis in LUAD patients. PAICS knockdown suppressed tumor cell migration and invasion, whereas overexpression enhanced these phenotypes. Mechanistically, PAICS promotes tumor metastasis by activating the Focal Adhesion Kinase (FAK) signaling through regulating the expression of Integrin α10 (ITGA10). FAK inhibitor Defactinib (VS6063) successfully inhibited PAICS overexpression-induced lung metastasis in vivo. In addition, we found that the high expression of PAICS in LUAD was regulated by mA modification. METTL3 (Methyltransferase-like 3) enhances the modification level of PAICS mRNA and IGF2BP2 (Insulin-like growth factor 2 mRNA binding protein 2) binds to its mRNA upon recognition of the mA modification site, which increased the stability of the mRNA and the expression. These results provide valuable insights into the biological functions of PAICS and potential avenues for new therapeutic approaches. - Source: PubMed
Publication date: 2026/07/23
Zhang MengzhuZhu JianjieZhang HuilingZhang JianshengZhao JianChen QiongjuZeng YuanyuanLi JianjunHuang Jian-AnLiu Zeyi - Indirect treatment comparisons (ITCs) are essential in the context of joint clinical assessments (JCAs) under Regulation (European Union [EU]) 2021/2282, bridging evidence gaps where head-to-head data are lacking and enabling assessment across diverse national patient, intervention, comparator, and outcome (PICO) requirements. This paper critically reviews the EU Health Technology Assessment Coordination Group's (HTACG) guidelines on direct and indirect comparisons, with particular focus on ITCs. While the guidelines promote transparency and rigorous evaluation of assumptions, they adopt a restrictive stance on assumption violations, the use of unanchored comparisons, and population-adjusted methods such as matching-adjusted indirect comparisons (MAIC) and simulated treatment comparisons (STC). The guidance shows limited support for Bayesian methods and undervalues meta-regression in favor of subgroup analyses. Operational implications for health technology developers (HTDs) are substantial, including new requirements for dual systematic reviews, multiple network structures, and shifted null hypothesis testing. Moreover, the guidelines effectively dissuade the use of non-randomized comparisons in rare or rapidly evolving indications and may inadvertently hinder access to effective treatments. Emerging practices such as external control arms (ECA) or target trial emulation are underdeveloped. Notably, there is no indication that the guidelines are grounded in systematic methodological validation studies. As JCAs evolve, greater methodological flexibility, empirical grounding, and clear operational guidance will be essential. Refining the guidelines along these principles would enhance their practical utility, mitigate intrinsic assessment variability, support consistent assessments across Member States (MS), and ultimately improve patient access to innovative therapies. - Source: PubMed
Publication date: 2026/05/07
Aballéa SamuelToumi MondherWojciechowski PiotrClay EmilieFalissard BrunoSimoens StevenAuquier PascalCapri StefanoBernardini RenatoRuof JoergFricke Frank-UlrichMorales Oriol SolaBoyer Laurent - In the original publication [...]. - Source: PubMed
Publication date: 2026/04/30
Huang Chin-ShengHsieh Ming-ShouYadav Vijesh KumarWu Yang-CheLiu Shao-ChengYeh Chi-TaiHuang Mao-Suan - Diffuse large B-cell lymphoma (DLBCL) features an immunosuppressive tumor microenvironment (TME), yet the molecular drivers connecting metabolic reprogramming to immune evasion remain poorly defined. Here, we deployed an integrative single-cell transcriptomic analysis combined with a machine learning (ML) framework to systematically identify key immune-suppressive hubs in DLBCL. Through ML-driven prioritization of a 33-gene panel, PAICS emerged as a central node within an immunosuppressive B-cell subgroup. Functional assays confirmed that PAICS promotes lymphoma proliferation, survival, and tumor growth while establishing an immunosuppressive TME-marked by reduced IFN‑γ, elevated TGF‑β and IL‑10, and enhanced CD8⁺ T cell exhaustion. Mechanistically, we uncovered the IRF4-PAICS-LDHA axis: IRF4 transcriptionally activates PAICS, which physically interacts with LDHA to augment its activity, thereby skewing the NAD⁺/NADH balance toward metabolic immunosuppression. Importantly, our AI-aided approach not only identified this axis but also predicted its vulnerability to metabolic intervention: both methotrexate treatment and LDHA knockdown restored metabolic balance, reversed T‑cell exhaustion, and suppressed tumor growth. These findings highlight the power of ML in uncovering multi-targetable metabolic-immune networks and in guiding therapeutic strategies to overcome immune evasion in DLBCL. - Source: PubMed
Publication date: 2026/04/16
Wang ZeyuanWang LiyeQian SiyuZhang YueYang QingYang ZhenzhenWu ShaoxuanDong MengZhang ZhiqiWei XufengYang MingleiMeng HuiLiu EnjieJiang GuozhongZhang XudongLi WencaiChen Qingjiang