PAICS Blocking Peptide, Blocking Peptides
- Known as:
- PAICS Blocking Peptide, Blocking Peptides
- Catalog number:
- 33R-1547
- Product Quantity:
- 100 ug
- Category:
- -
- Supplier:
- Fitzgerald
- Gene target:
- PAICS Blocking Peptide Peptides
Related genes to: PAICS Blocking Peptide, 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 Blocking Peptide, Blocking Peptides
Related articles to: PAICS Blocking Peptide, Blocking Peptides
- 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 - Breast cancer metastasis remains a major clinical challenge due to its complex molecular mechanisms, highlighting the need to identify key regulatory factors. - Source: PubMed
Publication date: 2026/04/14
Guo JianCen LichaoQian XinyeYou Shengban - Advanced Therapy Medicinal Products (ATMPs) often present substantial clinical uncertainties at the time of reimbursement evaluation, particularly due to the lack of appropriate comparators and the absence of long-term clinical endpoints. This study primarily examined two methodological areas relevant to these challenges: indirect treatment comparisons (ITCs) and surrogate endpoints. In addition, the review was supplemented with an assessment of innovative trial designs to explore how emerging approaches may contribute to evidence generation for ATMPs. - Source: PubMed
Publication date: 2026/03/12
Delemarre LotteHuys IsabelleVan Dyck WalterSimoens Steven - Population-adjusted indirect comparisons (PAICs), including Matching-Adjusted Indirect Comparison and Simulated Treatment Comparison, are increasingly used to inform health technology assessments. These methods offer a pragmatic approach to generating comparative evidence between treatments when head-to-head trial data are unavailable and standard indirect treatment comparison methods are unfeasible. In rare diseases, however, PAICs often face substantial methodological challenges arising from small sample sizes, limited covariate overlap, and the frequent use of unanchored comparisons that rely on unverifiable assumptions. These limitations can lead to unstable estimates, reduced precision, and bias that may undermine the reliability of findings. Methodological refinements-such as optimized weighting, Bayesian approaches, and doubly robust estimators-provide some improvements but do not resolve these fundamental issues. Current European Joint Clinical Assessment guidance recommends that anchored PAICs be applied with great caution, while unanchored PAICs are considered , and other methods . We argue that PAICs can play a supportive role within a multidimensional and deliberative HTA process, contributing to comparative assessment alongside other evidence sources when available data are limited. However, their results require careful interpretation and transparent communication of uncertainty. Future research should prioritize the further development of formal frameworks to quantify bias and systematically assess robustness, thereby preventing overstatement of the credibility of PAIC-derived evidence in rare disease contexts. - Source: PubMed
Publication date: 2026/03/06
Parkitny MikolajAballéa SamuelWojciechowski PiotrToumi Mondher