UGT3A2 Antibody (C_term) Blocking peptide
- Known as:
- UGT3A2 Antibody (C_term) Blocking short protein sequence
- Catalog number:
- BP10359b
- Product Quantity:
- 0.1 mg
- Category:
- -
- Supplier:
- Abgen
- Gene target:
- UGT3A2 Antibody (C_term) Blocking peptide
Related genes to: UGT3A2 Antibody (C_term) Blocking peptide
- Gene:
- UGT3A2 NIH gene
- Name:
- UDP glycosyltransferase family 3 member A2
- Previous symbol:
- -
- Synonyms:
- -
- Chromosome:
- 5p13.2
- Locus Type:
- gene with protein product
- Date approved:
- 2005-07-20
- Date modifiied:
- 2016-03-01
Related products to: UGT3A2 Antibody (C_term) Blocking peptide
Related articles to: UGT3A2 Antibody (C_term) Blocking peptide
- Hepatoblastoma is the most common primary liver malignancy in children, with metabolic reprogramming playing a critical role in its progression due to the liver's intrinsic metabolic functions. Enhanced glycolysis, glutaminolysis, and fatty acid synthesis have been implicated in hepatoblastoma cell proliferation and survival. In this study, we screened for altered overexpression of metabolic enzymes in hepatoblastoma tumors at tissue and single-cell levels, establishing and validating a hepatoblastoma tumor expression metabolic score using machine learning. Starting from the Mammalian Metabolic Enzyme Database, bulk RNA sequencing data from GSE104766 and GSE131329 datasets were analyzed using supervised methods to compare tumors versus adjacent liver tissue. Differential expression analysis identified 287 significantly regulated enzymes, 59 of which were overexpressed in tumors. Functional enrichment in the KEGG metabolic database highlighted a network enriched in amino acid metabolism, as well as carbohydrate, steroid, one-carbon, purine, and glycosaminoglycan metabolism pathways. A metabolic score based on these enzymes was validated in an independent cohort (GSE131329) and applied to single-cell transcriptomic data (GSE180665), predicting tumor cell status with an AUC of 0.98 (sensitivity 0.93, specificity 0.94). Elasticnet model tuning on individual marker expression revealed top tumor predictive markers, including FKBP10, ATP1A2, NT5DC2, UGT3A2, PYCR1, CKB, GPX7, DNMT3B, GSTP1, and OXCT1. These findings indicate that an activated metabolic transcriptional program, potentially influencing epigenetic functions, is observed in hepatoblastoma tumors and confirmed at the single-cell level. - Source: PubMed
Publication date: 2024/12/04
Monge ClaudiaFrancés RaquelMarchio AgnèsPineau PascalDesterke ChristopheMata-Garrido Jorge - Ewing Sarcoma (EWS) is the second most common osseous malignancy in children and young adults after osteosarcoma, while it is the fifth common osseous malignancy within adult age population. The clinical presentation of EWS is quite often non-specific, with the most common symptoms at presentation consisting of pain, swelling or general discomfort. The dearth of clinically relevant diagnostic or predictive biomarkers continues to remain a pressing clinical challenge. Identification of tumor specific biomarkers can lend towards an early diagnosis, expedited initiation of therapy, monitoring of therapeutic response, and early detection of recurrence of disease. We carried-out a complex analysis of cell lines and cell line derived small extracellular vesicles (sEVs) using label-free-based Quantitative Proteomic Profiling with an intent to determine shared and distinct features of these tumor cells and their respective sEVs. We analyzed EWS cells with different fusions ( type I, II, and III and ) and their corresponding sEVs. Non-EWS controls included osteosarcoma, rhabdomyosarcoma, and benign cells, osteoid osteoma and mesenchymal stem cells. Proteomic profiling identified new shared markers between cells and their corresponding cell-derived sEVs and markers which were exclusively enriched in EWS-derived sEVs. These exo-biomarkers identified were validated by approaches of publicly available protein databases and by capillary electrophoresis based western analysis (Wes). Here, we identified a protein biomarker named UGT3A2 and found its expression highly specific to EWS cells and their sEVs compared to control samples. Clinical validation of UGT3A2 expression in patient tumor tissues and plasma derived sEV samples demonstrated its specificity to EWS, indicating its potential as a EWS biomarker. - Source: PubMed
Publication date: 2023/04/13
Turaga Soumya MSardiu Mihaela EVishwakarma VikalpMitra AmritaBantis Leonidas EMadan RashnaMerchant Michael LKlein Jon BSamuel GlensonGodwin Andrew K - Acute myeloid leukemia (LAML) is the most widely known acute leukemia in adults. Chemotherapy is the main treatment method, but eventually many individuals who have achieved remission relapse, the disease will ultimately transform into refractory leukemia. Therefore, for the improvement of the clinical outcome of patients, it is crucial to identify novel prognostic markers. - Source: PubMed
Publication date: 2023/02/10
Tian ChuanGuo HuiWei Wei - The human UDP-glycosyltransferase (UGTs) superfamily has a critical role in the metabolism of anticancer drugs and numerous pro/anti-cancer molecules (e.g., steroids, lipids, fatty acids, bile acids and carcinogens). Recent studies have shown wide and abundant expression of genes in human cancers. However, the extent to which genes acquire somatic mutations within tumors remains to be systematically investigated. In the present study, our comprehensive analysis of the somatic mutation profiles of 10,069 tumors from 33 different TCGA cancer types identified 3427 somatic mutations in genes. Overall, nearly 18% (1802/10,069) of the assessed tumors had mutations in genes with huge variations in mutation frequency across different cancer types, ranging from over 25% in five cancers (COAD, LUAD, LUSC, SKCM and UCSC) to less than 5% in eight cancers (LAML, MESO, PCPG, PAAD, PRAD, TGCT, THYM and UVM). All 22 genes showed somatic mutations in tumors, with UGT2B4, UGT3A1 and UGT3A2 showing the largest number of mutations (289, 307 and 255 mutations, respectively). Nearly 65% (2260/3427) of the mutations were missense, frame-shift and nonsense mutations that have been predicted to code for variant UGT proteins. Furthermore, about 10% (362/3427) of the mutations occurred in non-coding regions (5' UTR, 3' UTR and splice sites) that may be able to alter the efficiency of translation initiation, miRNA regulation or the splicing of UGT transcripts. In conclusion, our data show widespread somatic mutations of genes in human cancers that may affect the capacity of cancer cells to metabolize anticancer drugs and endobiotics that control pro/anti-cancer signaling pathways. This highlights their potential utility as biomarkers for predicting therapeutic efficacy and clinical outcomes. - Source: PubMed
Publication date: 2022/11/21
Hu Dong GuiMarri ShashikanthHulin Julie-AnnMcKinnon Ross AMackenzie Peter IMeech Robyn - The pathogenesis of thymoma (THYM) remains unclear, and there is no uniform measurement standard for the complexity of THYM derived from different thymic epithelial cells. Consequently, it is necessary to develop novel biomarkers of prognosis estimation for patients with THYM. Consensus clustering and single-sample gene-set enrichment analysis were used to divide THYM samples into different immunotypes. Differentially expressed genes (DEGs) between those immunotypes were used to do the Kyoto Encyclopedia of Genes and Genomes analysis, Gene Ontology annotations, and protein-protein interaction network. Furthermore, the survival-related DEGs were used to construct prognostic model with lasso regression. The model was verified by survival analysis, receiver operating characteristic curve, and principal component analysis. Furthermore, the correlation coefficients of stemness index and riskscore, tumor mutation burden (TMB) and riskscore, drug sensitivity and gene expression were calculated with Spearman method. THYM samples were divided into immunotype A and immunotype B. A total of 707 DEGs were enriched in various cancer-related or immune-related pathways. An 11-genes signature prognostic model ( and ) was constructed from 177 survival-related DEGs. The prognostic model was significantly related to overall survival, clinical features, immune cells, TMB, and stemness index. The expression of some genes were significantly related to drug sensitivity. For the first time, a prognostic model of 11 genes was identified based on the immune microenvironment in patients with THYM, which may be helpful for diagnosis and prediction. The associated factors (immune microenvironment, mutation status, and stemness) may be useful for exploring the mechanisms of THYM. - Source: PubMed
Publication date: 2022/02/11
Yang YingXie LiqingLi ChenLiu LiangleYe XiuzhiHan Jianbang