PTPN3 Antibody
- Known as:
- PTPN3 Antibody
- Catalog number:
- XW-7831
- Product Quantity:
- 0.05 mg
- Category:
- -
- Supplier:
- Prosci
- Gene target:
- PTPN3 Antibody
Related genes to: PTPN3 Antibody
- Gene:
- PTPN3 NIH gene
- Name:
- protein tyrosine phosphatase non-receptor type 3
- Previous symbol:
- -
- Synonyms:
- PTPH1
- Chromosome:
- 9q31
- Locus Type:
- gene with protein product
- Date approved:
- 1992-02-12
- Date modifiied:
- 2019-02-14
Related products to: PTPN3 Antibody
Related articles to: PTPN3 Antibody
- Excessive adipose tissue accumulation in sheep disrupts insulin signaling, inducing insulin resistance, and alters energy partitioning mechanisms. These changes adversely affect both ovine health and production efficiency. This study employed whole-genome resequencing to conduct selection signal analysis in long-fat-tailed (Lanzhou fat-tailed sheep) and short-fat-tailed (Hu sheep) breeds, investigating the genetic basis underlying divergent lipid metabolism-related traits between these distinct tail phenotypes. Fifteen healthy adult individuals, each from long-fat-tailed (Lanzhou Large-tailed sheep) and short-fat-tailed (Hu sheep) breeds, underwent whole-genome resequencing. Whole-genome resequencing analyses via F, XP-CLR, and XP-EHH identified 75 significantly selected regions ( < 0.01), revealing eight key candidate genes (, , , , , , , ). Subsequent functional enrichment analysis demonstrated significant enrichment of and in lipid metabolic processes (GO:0006629). Employing whole-genome resequencing-based selection signal analysis in long-fat-tailed (Lanzhou Large-tailed sheep) and short-fat-tailed (Hu sheep) breeds, this study identified two key lipid metabolism-associated genes ( and ). These findings provide critical insights for conserving genetic resources and informing molecular breeding strategies targeting divergent tail phenotypes. - Source: PubMed
Publication date: 2025/10/20
Zhang XiaowenLi YufeiZhao YongqingGuo PenghuiCai YongXu HongweiCao XinLi QiongyiMa XiaoxiaZhang DerongBai Jialin - The prevalence of obesity is increasing year by year, but its characteristic molecular targets are still unclear, and the available therapeutic approaches are relatively limited. Therefore, it is crucial to elucidate the molecular mechanisms underlying the pathogenesis of obesity and to explore potential molecular targets for obesity drug therapy. The expression dataset (GSE73304) was downloaded from the gene expression omnibus database for between-group differential expression gene analyses (DEGs), genome enrichment analysis (GSEA), and weighted gene co-expression network analysis (WGCNA) in healthy and obese populations. Intersecting genes obtained from DEGs and WGCNA difference modules were analyzed with three machine learning methods (LASSO, RandomForest, SVM-REF) to obtain obesity characteristic Genes. Analysis of ROC curves, intergroup differences, and intergene correlations for Genes characterizing obesity. The results of the study showed that 10 specimens and their Gene expression matrices were collected from each of the normal and obese patient groups, yielding 1937 DEGs. GSEA results showed that DEGs were enriched for 32 significant KEGG pathways. Forty gene co-expression modules of the gene expression matrix were constructed by WGCNA. Forty-five intersecting genes were obtained from DEGs and WGCNA significant difference module, which were associated with cellular differentiation, mitochondria, and a variety of endocrine factors and hormones. Eleven genes, including XLOC_004699, RIMBP2, COX6B2, OR5T1, RXFP2, XLOC_003676, XLOC_013038, VAX1, Q07610, XLOC_011515, and PTPN3, were obtained as the obesity characterization Genes through machine learning analysis of intersecting Genes. Based on WGCNA and machine learning, this study found that 11 genes, including RIMBP2, COX6B2, and OR5T1, differed significantly between healthy and obese populations and were closely associated with multiple molecular mechanisms, and these genes may be potential targets for drug therapy and diagnostic biomarkers. - Source: PubMed
Publication date: 2025/09/16
Yuan YinYue ShujiaoWu ZixuanSun XuanWang Hongwu - Pancreatic cancer is a highly lethal and aggressive malignancy with a minimal five-year survival rate (5 %) and a high mortality rate. The most common and fast-growing type of pancreatic cancer is PDAC, which constitutes 90 % of all cases. - Source: PubMed
Publication date: 2025/05/21
Naeem MaryamAhmed KhursheedSultan Aneesa - PTPN3 regulates cellular signaling and is dysregulated in cancer. There has been less research about the oncogenic impact of PTPN3 in breast cancer patients. This study analyzed PTPN3 mRNA levels and their prognostic significance in breast cancer using TCGA datasets. qRT-PCR was used to assess PTPN3 expression in formalin-fixed, paraffin-embedded Indian breast cancer patient samples (tumor-74, control-36). PTPN3 protein levels (ER-positive 15; ER-negative: 15; distant normal breast tissues: 20) were also immunohistochemically assessed using the H-score method. The biomarker potential was examined using a receiver operating characteristic (ROC) analysis. Docking and molecular dynamics (MD) simulations were used to find PTPN3 inhibitors (PDB ID: 2B49) from 892 FDA-approved natural chemicals in the ZINC database. PTPN3 mRNA and protein expression were significantly higher in breast cancers and associated with clinicopathological variables such as age, ER status, tumor stage, grade, Ki-67 index, menopause, and lymph node metastasis (p < 0.05). ROC analysis revealed an AUC of 0.7654, indicating PTPN3's biomarker potential. Docking yielded three high-affinity inhibitors: Cyclocort (ZINC000003977777), Toposar (ZINC000003938684), and Tetracycline (ZINC000084441937), with binding energies of -9.3, -8.73, and -8.66 kcal/mol, respectively. MD simulations confirmed stable connections via hydrogen bonds and hydrophobic interactions under minimal constraints. In conclusion, PTPN3 overexpression supports its role as a prognostic biomarker, and Cyclocort, Toposar, and Tetracycline need further confirmation as potential PTPN3 inhibitors. - Source: PubMed
Publication date: 2025/04/22
Thankachan SanuBhardwaj Boddapati KalyaniPatel DimpleKp KavithaKabekkodu Shama PrasadaSuresh Padmanaban S - This study investigates the genetic underpinnings of wool traits, specifically fibre diameter (FD) and staple length (SL), in Middle Anatolian Merino sheep using multi-locus genome-wide association study (GWAS) approaches. Representing the first attempt to examine these polygenic traits with multi-locus methods, the analysis employed four techniques: mrMLM, FASTmrMLM, FASTmrEMMA, and ISIS EM-BLASSO. A total of 18 Quantitative Trait Nucleotides (QTNs) were identified for FD, with 7 co-detected by multiple methods, and 14 QTNs were identified for SL, with 5 co-detected by multiple methods. Post-hoc power analysis revealed high statistical power for both traits (FD: 0.95, SL: 0.91). Notably, three candidate genes-PTPN3, TCF4, and ZBTB8A-were found to be consistent with prior studies. Gene enrichment and pathway analyses reaffirmed the complex and multifactorial molecular mechanisms governing wool traits. These findings enhance our understanding of the polygenic nature of wool traits, shedding light on the intricate genetic regulation and pinpointing genomic regions potentially influencing wool physiology. By identifying specific QTNs associated with FD and SL, this research provides a foundation for elucidating the genetic mechanisms underlying these economically significant traits. Upon validation in diverse populations, these findings hold substantial promise for the application of marker-assisted selection (MAS) to improve wool traits. - Source: PubMed
Publication date: 2025/03/27
Yaman YalçınÖnaldi A TanerDoğan ŞükrüKirbaş MesutBehrem SedatKal Yavuz