PSMB9 antibody Host Mouse
- Known as:
- PSMB9 (anti-) Host Mouse
- Catalog number:
- 'H00005698-A01
- Product Quantity:
- 50
- Category:
- -
- Supplier:
- ACR
- Gene target:
- PSMB9 antibody Host Mouse
Related genes to: PSMB9 antibody Host Mouse
- Gene:
- PSMB9 NIH gene
- Name:
- proteasome subunit beta 9
- Previous symbol:
- LMP2
- Synonyms:
- RING12, beta1i, PSMB6i
- Chromosome:
- 6p21.32
- Locus Type:
- gene with protein product
- Date approved:
- 1991-12-18
- Date modifiied:
- 2016-10-05
Related products to: PSMB9 antibody Host Mouse
Related articles to: PSMB9 antibody Host Mouse
- This study explored Polyamine metabolism (PM)-related biomarkers and their regulatory processes in Crohn's disease (CD) using three transcriptome datasets (GSE179285, GSE126124, and GSE102133) and 59 PM-related genes (PMRGs). Differential expression analysis and weighted gene co-expression network analysis (WGCNA) were employed to identify candidate genes, resulting in four key candidates: PSMB10, PSMB8, PSMB9, and PSME2, selected from 796 differentially expressed genes (DEGs). Machine learning and expression validation confirmed that PSMB9, PSMB10, and PSME2 were overexpressed in the CD group, with ROC curve analysis revealing AUC values above 0.8 in all three datasets, supporting their potential as biomarkers. Functional enrichment analysis showed their involvement in the degradation of valine, leucine, and isoleucine, and cell adhesion molecules. Immune infiltration analysis, inferred from the same transcriptomic data, showed that the expression levels of these genes were positively correlated with the abundance of 27 immune cell types in CD tissues. Additionally, these biomarkers were linked to other digestive diseases, such as colon disease and proctitis. Notably, CARFILZOMIB demonstrated significant binding to these biomarkers, suggesting a potential mechanistic link that warrants further experimental investigation. This research emphasizes the role of PSMB9, PSMB10, and PSME2 as promising biomarkers for CD and provides a foundation for further exploration of the regulatory mechanisms of PM in CD. - Source: PubMed
Publication date: 2026/07/04
Qi HongbinDing XueLeng GuangxianZhang MeifengLuo SiqiZhou YongjinYang YunshanYang HongxinWang QianJin XiangrenDai Li - Neoantigens are critical targets for cancer immunotherapy, yet the relationship between experimentally validated neoantigen burden and antigen processing machinery (APM) expression in determining clinical outcomes remains unclear. We mapped CEDAR-annotated neoantigens (CENs) onto mutation data from 43,980 patients across 14 cancer types using cBioPortal. APM gene expression was correlated with survival outcomes across 13 cohorts. Machine learning approaches (elastic net stability selection, random survival forest, univariable Cox regression) identified prognostically important APM genes across 11 cohorts. Findings were validated in the IMvigor210 immunotherapy trial (n=348 metastatic urothelial carcinoma patients) and single-cell RNA-sequencing data (GSE161529; n=29 breast cancers). Overall, 40.4% of patients harbored at least one CEN, with high prevalence in pancreatic (>75%) and skin cancers (>70%). CENs predominantly arose from driver oncogenes including PIK3CA, KRAS, BRAF, TP53, and EGFR. High APM expression was associated with improved survival, particularly in CEN-positive tumors. Machine learning identified immunoproteasome components (PSME1, PSMB8, PSMB9, PSMB10) as the dominant prognostic contributors within the 12-gene APM signature. A simplified 4-gene immunoproteasome score performed equivalently to the full APM score in leave-one-cohort-out cross-validation (median C-index 0.545 vs 0.545; p=0.31). In IMvigor210, immunoproteasome-high patients achieved a 3.2-fold higher response rate to atezolizumab (19.8% vs 6.2%; p=0.010). Single-cell analysis confirmed that tumor-intrinsic immunoproteasome expression correlated with increased CD8+ T cell infiltration (p=0.0014) and total immune fraction (p=0.0002). The 4-gene immunoproteasome signature demonstrates robust prognostic and predictive value across bulk sequencing, clinical trial, and single-cell platforms, warranting prospective validation as an immunotherapy biomarker. - Source: PubMed
Publication date: 2026/06/27
Lai Jiun-ILiu Chun-YuTsai Yi-FangHuang Chi-ChengTseng Ling-MingChao Ta-Chung - SOCS2 is known to regulate myoblast differentiation and skeletal muscle development via the GH-IGF1 axis, while its role in Hu sheep myoblast differentiation and its potential alternative signaling pathways remain largely unexplored. Proteomic profiling of SOCS2-knockout C2C12 monoclonal cell lines identified significant enrichment of differentially expressed proteins in the proteasome pathway, with PSMB9 serving as one of the most prominent candidates, though the underlying mechanism remained unclear. In this present study, the regulatory relationship between SOCS2 and PSMB9 was firstly validated, and the results showed that SOCS2 positively regulated PSMB9 expression in Hu sheep myoblasts. Functional assays further confirmed SOCS2 negatively regulated Hu sheep myoblast differentiation. Mechanistically, JASPAR database predicted potential STAT3 transcription factor binding sites in the PSMB9 promoter region, and dual-luciferase reporter assays verified that STAT3 indeed repressed PSMB9 transcriptional activity. SOCS2 regulated STAT3 protein abundance and phosphorylation by interacting with JAK1. Functionally, STAT3 promoted Hu sheep myoblast differentiation, whereas PSMB9 inhibited Hu sheep myoblast differentiation. Moreover, either STAT3 knockdown or PSMB9 overexpression could rescue the enhanced differentiation phenotype induced by SOCS2 knockdown. Collectively, this study demonstrates a novel mechanism by which SOCS2 regulates Hu sheep myoblast differentiation through STAT3/PSMB9 signaling axis. - Source: PubMed
Publication date: 2026/06/24
Xie B Y TGuo D DZhang JMeng C HZhang J LQian YCao S XLi Y X - Childhood-onset systemic lupus erythematosus (cSLE) is associated with significant morbidity and mortality. While numerous variants have been associated with adult-onset SLE, limited data exist on genetic variation within cSLE. We aimed to investigate genetic factors of early-onset cSLE, defined as onset of cSLE prior to age 10. - Source: PubMed
Publication date: 2026/06/23
Nelson MeghanMurthy ShantaMaddipatla SushmaShenoy SreekalaPonder LoriChandrakasan ShanmuganathanKugathasan SubraCutler DavePrahalad Sampath - Rheumatoid arthritis (RA) is a systemic immune-related disease characterized by chronic synovial inflammation and progressive joint destruction. However, the molecular mechanisms and diagnostic biomarkers underlying RA remain unclear. In this study, we aimed to identify potential biomarkers for clinical diagnosis of RA and to investigate their association with immune infiltration. By integrating differentially expressed genes analysis (DEGs) and weighted gene co-expression network analysis (WGCNA), we identified PSMB9 as a hub gene associated with RA. Gene set enrichment analysis (GSEA) and immune infiltration analysis revealed a strong association between RA and macrophage infiltration. Single-cell RNA sequencing datasets also suggest that PSMB9 is not only highly expressed in macrophage but is also present in synovial cells. We employed cellular thermal shift assay (CETSA) combined with Western blot to validate the interaction between sinomenine (SIN) and the target protein. CETSA results demonstrated that, compared with the control group, SIN increased the thermal stability of PSMB9, suggesting direct binding between the two. Western blot experiments further confirmed that PSMB9 protein expression was significantly downregulated following SIN treatment. PSMB9 may serve as potential diagnostic biomarker and therapeutic targets for RA. Moreover, our data suggest SIN may exert anti-inflammatory effects through regulation of PSMB9. This study also provides an additional insight into the underlying mechanisms involved in the progression of RA and discover a new molecular target for SIN. - Source: PubMed
Publication date: 2026/05/29
Zhang CuiLio ChonkitLi NanaYu YangLuo Jinfang