Ask about this productRelated genes to: REG1B antibody
- 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
Related articles to: REG1B antibody
- 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 - Proteomics serves as a primary source of therapeutic targets. In this study, we performed a Mendelian randomization (MR) analysis within the proteomic scope to identify candidate protein markers and potential therapeutic targets for duodenal ulcer (DU). This study utilized MR and co-localization analysis within the proteomic framework. Data on 2088 plasma proteins were carefully collected from a study that detected 4907 protein quantitative trait loci. The genetic association data for DU were sourced from the UK Biobank, which encompassed 1908 cases and 461,025 controls. MR used single nucleotide polymorphisms as a genetic tool to estimate the causal effects of exposure on outcomes, in order to screen candidate proteins associated with DU. Meanwhile, Bayesian co-localization analysis is used to determine the probability of shared causal genetic variation between features. Additionally, 2-step MR was employed to quantify the proportion of protein-mediated risk factors for DU. Finally, protein-protein interaction analysis was conducted to elucidate the potential link between proteins and drugs currently used for treating DU. Using the Drug Signature Database, potential targeted drugs for druggable proteins were explored. We identified 11 plasma proteins that were significantly associated with DU. Elevated levels of FLT4, IGSF3, IL6ST, EPHB4, DPEP2, SEMA6A, and IL1R1 were found to have a risk-conferring effect. Conversely, increased levels of REG1B, GOLM1, FAM3D, and QSOX2 exhibited a protective effect. Notably, none of these 11 proteins demonstrated evidence of reverse causality. Bayesian co-localization analysis indicated that REG1B, FLT4, GOLM1, EPHB4, and FAM3D shared the same genetic variations as those associated with DUs. Additionally, the protein target IL1R1, which is related to DU drugs, and 6 pharmaceutically relevant proteins, namely REG1B, IL6ST, FLT4, DPEP2, QSOX2, and EPHB4, were identified. Our research found that REG1B, FLT4, IGSF3, IL6ST, GOLM1, EPHB4, DPEP2, FAM3D, QSOX2, SEMA6A, and IL1R are associated with DU. Among them, IL1R1, REG1B, IL6ST, FLT4, DPEP2, QSOX2, and EPHB4 may become drug targets for further clinical research on DU. Targeting these proteins during drug development may provide a preferred and cost-effective approach for treating DU. - Source: PubMed
Luo XuLuo DanLiu ChenhaoZhang HuizeLong MingyueCao SiminLiu Yi