REG1B antibody - N-terminal region (ARP34029_P050)
- Known as:
- REG1B (anti-) - N-terminal region (ARP34029_P050)
- Catalog number:
- arp34029_p050
- Product Quantity:
- USD
- Category:
- -
- Supplier:
- Aviva Systems Biology
- Gene target:
- REG1B antibody - N-terminal region (ARP34029_P050)
Related genes to: REG1B antibody - N-terminal region (ARP34029_P050)
- Gene:
- REG1B NIH gene
- Name:
- regenerating family member 1 beta
- Previous symbol:
- -
- Synonyms:
- REGL, PSPS2, REGH, REGI-BETA
- Chromosome:
- 2p12
- Locus Type:
- gene with protein product
- Date approved:
- 1994-07-04
- Date modifiied:
- 2015-11-18
Related products to: REG1B antibody - N-terminal region (ARP34029_P050)
Related articles to: REG1B antibody - N-terminal region (ARP34029_P050)
- The aggressive nature and poor prognosis of pancreatic ductal adenocarcinoma (PDAC) are closely linked to its complex tumor microenvironment (TME). However, a detailed mapping of the TME's cellular landscape and its interactions with cancer cells at single-cell resolution is still lacking, limiting our understanding of the disease. This study aims to utilize single-nucleus RNA sequencing technology to construct a panoramic single-cell transcriptomic map of the PDAC TME. Furthermore, the expression characteristics of the key differential genes and in the early progression of PDAC and the regulation of the tumor microenvironment are validated through clinical samples. - Source: PubMed
Publication date: 2026/03/19
Yan XinZhao JingYan JingWu GangYan RanxingChen LiangLuo ShiruiLiu JiawuHou XianghuiYang ZhiyongZhu Qian - This study employed an integrative computational and systems biology framework to define a diagnostic gene signature for hepatocellular carcinoma (HCC) and to explore its potential translational relevance in a hypothesis-generating manner. Differential expression analysis of transcriptomic data from 230 samples identified 2748 significantly differentially expressed genes (DEGs), including 2283 upregulated and 465 downregulated genes, with FGF4 (log2FC = 10.08) and REG1B (log2FC = 10.02) among the top hits. Four machine learning classifiers were trained using this signature and demonstrated consistently high predictive performance, with XGBoost emerging as the top-performing model (accuracy = 0.97, F1-score = 0.96, ROC-AUC = 0.981). Logistic Regression (L1) and Random Forest achieved comparable performance (ROC-AUC = 0.980 and 0.979, respectively), while SVM-linear also showed high robustness (ROC-AUC = 0.978). All models showed good calibration, with low Brier scores (<0.04) and precision consistently exceeding 0.90 across most recall thresholds, indicating strong but not perfect classification performance. SHAP-based explainability analysis was used to rank and prioritise the most influential predictors, refining the biomarker panel to 81 genes that collectively accounted for approximately 50 % of the model's explanatory contribution, and highlighting key downregulated predictors in HCC, including GDF2, COLEC10, BMP10, LRAT, and DNASE1L3. Protein-protein interaction and functional enrichment analyses revealed five major molecular clusters and provided systems-level insights into dysregulated biological processes associated with HCC. Drug-gene interaction mining mapped 78 target proteins to clinically relevant compounds, including tolrestat, alcuronium, metyrosine, and 4-phenylbutyric acid. Molecular docking suggested favorable binding propensities for several complexes, including alcuronium-3UON (-8.5 kcal/mol), tolrestat-1ZUA (-8.3 kcal/mol), metyrosine-2XSN (-6.7 kcal/mol), and 4-phenylbutyric acid-2NZ2 (-5.9 kcal/mol). A 100 ns molecular dynamics simulation of the tolrestat-AKR1B10 (1ZUA) complex indicated structural stability, with protein backbone RMSD stabilising at 1.5-3.0 Å, ligand RMSD at 0.6-1.4 Å, and persistent interactions involving Trp22, His110, Glu111, and Phe122. Physicochemical and pharmacokinetic profiling further prioritised tolrestat as a computationally favourable candidate (MW = 357.35, LogP = 3.64, TPSA = 81.86 Ų), exhibiting acceptable drug-likeness, high predicted gastrointestinal absorption, and low synthetic complexity (SA = 2.34), in contrast to alcuronium (MW = 666.89, SA = 7.86), which showed multiple rule violations. Collectively, this in silico study proposes a robust diagnostic gene signature for HCC and identifies tolrestat as a promising repurposing candidate that warrants experimental validation, demonstrating the utility of integrating machine learning, network biology, and molecular simulation in translational cancer research. - Source: PubMed
Publication date: 2026/02/08
Alfaifi MohammedKamli HossamKhan Najeeb UllahUnar Ahsanullah - - Source: PubMed
Publication date: 2026/01/10
Wei LaiZhao YingZhu JingXia Zhijun - Reliable tools for early identification of Crohn's disease (CD) remain lacking. We analyzed 2736 plasma proteins in 39,634 UK Biobank (UKB) participants and identified 44 associated with incident CD. CD274, CHI3L1, REG1B, ITGAV, PRSS8, ITGA11, GDF15, DEFA1_DEFA1B, and IL6 ranked highest in protein importance ordering. A machine learning model based on these 9 proteins achieved high prediction for CD in a geographically distinct UKB testing cohort (n = 13,262, AUC 0.76), outperforming clinical risk models. It was externally validated in EPIC-Norfolk (n = 2944, AUC 0.73) and exhibited high discriminatory capacity for CD in the cross-sectional Southern China cohort (n = 74, AUC 0.79). In the UKB testing cohort, combining proteins with clinical data improved predictive performance (AUC 0.78) up to 16 years pre-diagnosis. In the same cohort, individuals at high risk stratified by the protein model were 4.23 times more likely to develop CD. Our findings highlight proteomics-based models as a promising approach to predict CD up to 16 years before diagnosis, offering opportunities for early screening and intervention. - Source: PubMed
Publication date: 2025/12/12
Feng JingChen ShuoLi QinmingLong YuMa YuyingZhang LijunZeng RuijieLuo DonglingMeng MeijunYu ShiyiChen ChunlingWu YanjunHuang WentaoZhang HanLi LingyiLeung Felix WDuan ChongyangSha WeihongChen Hao - PurposeVenous thromboembolism (VTE), including deep vein thrombosis and pulmonary embolism, is a leading cause of morbidity and mortality in cancer patients. Prostate cancer is associated with an elevated risk of VTE, yet the molecular drivers remain poorly defined.MethodsIn this study, we employed high-throughput proteomic profiling using the SomaLogic platform to analyze plasma from 85 prostate cancer patients, including 43 with and 42 without VTE. Samples were collected at cancer diagnosis, with VTE diagnosed at a mean of 96.8 months later.ResultsPrincipal component analysis showed modest proteomic separation between groups. Differential expression analysis identified enriched pathways in VTE patients, including hemostasis (TIMP1, JAM2, TMX3, F3, and ESAM), cell adhesion (CXCL12, CCL11, CCN5, COL18A1, and ADGRB1) and cell proliferation (TIMP1, REG1B, REG1A, CRLF2, and ALDH1A2). Receiver Operating Characteristic analysis using top fifteen proteins achieved an area under the curve of 0.859, indicating strong predictive value for VTE in this cohort.ConclusionsWe identified a specific cluster of circulating proteins associated with development of VTE in patients with prostate cancer. This work deepens understanding of systemic mediators of cancer-associated VTE and, pending validation in other cohorts, paves the way for improved risk stratification and long-term monitoring in this population. - Source: PubMed
Publication date: 2025/12/05
Jose AshaLu SimonZhang ChaoLa JenniferYoung MelissaGaziano J MichaelFillmore Nathanael RRavid KatyaChitalia Vipul C