B4GALT2 Antibody
- Known as:
- B4GALT2 Antibody
- Catalog number:
- abx121292
- Product Quantity:
- EUR
- Category:
- -
- Supplier:
- Abbexa
- Gene target:
- B4GALT2 Antibody
Related genes to: B4GALT2 Antibody
- Gene:
- B4GALT2 NIH gene
- Name:
- beta-1,4-galactosyltransferase 2
- Previous symbol:
- -
- Synonyms:
- beta4Gal-T2
- Chromosome:
- 1p34.1
- Locus Type:
- gene with protein product
- Date approved:
- 1998-12-02
- Date modifiied:
- 2016-10-05
Related products to: B4GALT2 Antibody
Related articles to: B4GALT2 Antibody
- COPD is a progressive respiratory disorder characterized by persistent airflow limitation and chronic inflammation, yet the role of N4-acetylcytidine (ac4C) RNA modification in its pathogenesis remains largely unexplored. This study aimed to systematically screen for ac4C-related genes (ac4C-RGs) from a published database and investigate their regulatory networks in COPD, thereby identifying potential biomarkers for further mechanistic studies without assuming a direct regulatory relationship between any specific gene and ac4C modification. Differentially expressed genes (DEGs) were identified from transcriptomic profiles, and weighted gene co-expression network analysis (WGCNA) was applied to uncover key co-expression modules. Cross-analysis among DEGs, significant modules, and ac4C-RGs was conducted. Key genes were screened using LASSO regression, XGBoost, and random forest algorithms, followed by logistic regression‑based diagnostic model construction. Model performance was evaluated by receiver operating characteristic (ROC) curve analysis, area under the curve (AUC) with 95% confidence intervals, calibration curve assessment, and decision curve analysis (DCA). A total of 160 overlapping genes were identified, and six hub genes (PTRF, PRKCDBP, UPP1, TOR3A, FAM168B, and B4GALT2) were consistently selected by all three machine learning algorithms. The diagnostic model demonstrated good discriminative performance, with AUCs of 0.766, 0.759, and 0.723 in the training, internal test, and external validation sets, respectively. Regulatory network analysis suggested potential ceRNA axes and transcription factor interactions, while immune infiltration profiling revealed significant correlations between key genes and multiple immune cell subsets. Drug-gene interaction analysis and molecular docking indicated that fluorouracil, capecitabine, and 5-benzylacyclouridine may exhibit favorable predicted binding affinities with UPP1. In conclusion, PTRF, PRKCDBP, UPP1, TOR3A, FAM168B, and B4GALT2 were identified as potential ac4C-related biomarkers in COPD, potentially involved in immune and metabolic regulation, providing a foundation for future functional investigations and therapeutic exploration. - Source: PubMed
Publication date: 2026/09/03
Hu AnbangHong XuboMa YingWu JunlinZeng XiangxiaXu YinjiLi Hong - Osteoarthritis (OA) is a prevalent degenerative joint disease characterized by progressive cartilage destruction, yet the heterogeneity of transitional chondrocyte states and their contributions to pathogenesis remain incompletely understood. In this study, we constructed a single-cell transcriptomic atlas of human knee cartilage from 16 OA samples and 2 non-OA normal donors. High-resolution sub-clustering of the prehypertrophic chondrocytes (preHTC) compartment revealed eight transcriptionally distinct subtypes, among which CRIP1 preHTC emerged as the most significantly expanded population in OA, particularly in weight-bearing regions. Pseudotemporal trajectory inference and regulon analysis identified EGR3 as a candidate key regulator for CRIP1 preHTC. High-dimensional weighted gene coexpression network analysis (hdWGCNA) demonstrated matrix-remodeling and proteostasis-stress transcriptional programs were highly active in CRIP1 preHTC. Cell-cell communication analysis uncovered that CRIP1 preHTC acts as a central signaling hub in OA, with markedly enhanced FN1 signaling pathway. Spatial transcriptomics confirmed that CRIP1 preHTC co-localizes with prefibroblasts (preFC) in pathological niches and interacts with other cells in the OA microenvironment. Finally, a machine-learning-based 10-gene classifier (JUN, MCOLN3, RHOC, AKR7A2, HSPG2, AK4, B4GALT2, CLSTN1, LRRC41, and GADD45A) derived from CRIP1 preHTC-associated hub genes accurately discriminated OA from normal tissues in two independent bulk RNA-seq cohorts. Collectively, these findings suggest that CRIP1 preHTC may represent a pathogenic cell state in OA, provide a multi-layered molecular framework that links a specific chondrocyte transitional subset to cartilage degradation, and offer a potentially useful gene signature for OA diagnosis. - Source: PubMed
Xie ZikangWang YuLiu JinzhuLi HeBian HuweiGu Chonghao - Immune evasion is one of the critical factors contributing to the advanced progression of colorectal cancer (CRC). Our research identified that CRC cells exhibit high expression of the zinc finger protein ZNF30, which transcriptionally activates the expression of the glycosyltransferase B4GALT2. B4GALT2 mediates the N-glycosylation of MUC20, which subsequently interacts with sialic acid-binding immunoglobulin-like lectin 7 (Siglec-7) on the surface of macrophages. This interaction induces M2 polarization of the macrophages, thereby promoting immune evasion in CRC and ultimately accelerating malignant progression. In vivo experiments also verified that the ZNF30/B4GALT2/MUC20 axis promotes the growth and metastasis of CRC. The discovery of this regulatory mechanism highlights the critical role of aberrant glycosylation of MUC20 in CRC immune evasion and offers new predictive markers and therapeutic targets for patients with advanced CRC. - Source: PubMed
Publication date: 2026/04/11
Zhi YingruZhang YifengFei HuanhuanYuan JieLiu XiaobeiLiu Wanli - Lethal prostate cancer is marked by tumor heterogeneity and resistance to androgen receptor signaling inhibitors (ARSIs). In this study we identify glycolysis as a driver of disease progression and therapy resistance. Using single-sample gene set enrichment analysis (ssGSEA) on the SU2C cohort, we demonstrate that elevated glycolysis activity is associated with poor progression-free and overall survival. The glycolysis-based prognostic score (GLY score) is derived from the HALLMARK_GLYCOLYSIS gene set which includes , , , , , and , via LASSO-Cox regression. The GLY score effectively stratifies risk in the SU2C and WDCT cohorts, with higher scores predicting worse outcomes and increased SYNE1 mutation frequency. Pan-cancer analysis across TCGA datasets confirm its prognostic value. , enzalutamide-resistant prostate cancer cell lines exhibit heightened glycolysis, and 2-DG inhibition reverses this effect, restoring drug sensitivity. knockdown reduces glycolytic activity and cell proliferation. The GLY score offers robust prognostic value, and CLN6 represents a promising therapeutic target for precision medicine in lethal prostate cancer. - Source: PubMed
Cai ZhoudaLu JianmingMo ShanshanLiu JipuZhong ChuanfanWu YongdingZou FenYe JianhengHan ZhaodongLiang YuxiangZhang LeLiu FengpingZhong Weide - Chronic pain affects one-fifth of American adults, contributing significant public health burden. Chronic pain can be further understood through investigating brain gene expression, potentially informing on brain regions, cell types, and gene pathways. We tested for differentially expressed genes (DEGs) in chronic pain, migraine, lifetime fentanyl and oxymorphone use, and with chronic pain genetic risk in 4 brain regions (dorsal anterior cingulate cortex [dACC], dorsolateral prefrontal cortex [DLPFC], medial amygdala [MeA], and basolateral amygdala [BLA]) and imputed cell type expression data from 304 deeply phenotyped postmortem donors, potentially highlighting variation relevant to factors such as predisposition to chronic pain development, mechanisms of chronic pain development and persistence, and indirect effects of chronic pain and associated treatment or medication, and substance use. We also investigated sex differences in chronic pain differential gene expression. At the brain region level, we identified 2 chronic pain DEGs: B4GALT2 and VEGFB in dACC. At the cell level, we found more than 2000 chronic pain cell-type DEGs, significantly enriched in microglia of the basolateral amygdala. The findings were enriched for mouse microglia pain genes, and for hypoxia and immune response pathways. Small amounts of cross-trait DEG overlap in migraine and chronic pain highlighted medial amygdala cells, and in chronic pain and oxymorphone use suggested the amygdala as a key region. Chronic pain differential gene expression was not significantly different between men and women. Overall, chronic pain-associated gene expression is heterogeneous across region and cell type, is largely distinct from that in pain-related factors and migraine, and our results highlight BLA microglia as a key brain cell type in chronic pain. - Source: PubMed
Publication date: 2025/07/07
Collier LilySeah CarinaHicks Emily MHoltzheimer Paul EKrystal John HGirgenti Matthew JHuckins Laura MJohnston Keira J A