PSMB9 antibody Host rabbit
- Known as:
- PSMB9 (anti-) Host host: rabbit
- Catalog number:
- 'ARP46081_P050
- Product Quantity:
- 50
- Category:
- -
- Supplier:
- ACR
- Gene target:
- PSMB9 antibody Host rabbit
Related genes to: PSMB9 antibody Host rabbit
- 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 rabbit
Related articles to: PSMB9 antibody Host rabbit
- Immune escape drives cancer progression and therapy resistance, yet its prognostic role and impact on the tumor immune microenvironment in thyroid cancer remain unclear. We integrated single-cell and bulk RNA sequencing data to systematically characterize immune escape and its clinical significance. scRNA-seq analysis characterized cellular heterogeneity and quantified immune escape activity via AUCell. A prognostic gene signature was constructed from differential expression analysis combined with univariate Cox and LASSO regression, and validated using Kaplan-Meier and time-dependent ROC analyses. The immune landscape was profiled using ssGSEA, CIBERSORT, and ESTIMATE, while immunophenoscore (IPS) was used to predict immunotherapy responsiveness. Functional enrichment, CellChat, SCISSOR, tumor mutation burden (TMB), and CellMiner analyses were further performed to explore underlying mechanisms and therapeutic implications. A three-gene signature (CD9, NPC2, PSMB9) effectively stratified patients into highand low-risk groups with distinct survival outcomes. Low-risk tumors exhibited an "immune-hot" phenotype with increased CD8+ T cells and activated NK cells, higher checkpoint expression, and elevated IPS, suggesting greater immunotherapy sensitivity. In contrast, high-risk tumors showed an immune-cold microenvironment with M2 macrophage enrichment. Despite higher TMB, high-risk tumors displayed reduced immune activity, indicating impaired immune recognition. Single-cell analysis further identified MIF and CCL signaling as key mediators of multicellular immune evasion. Overall, our single-cell-informed immune escape signature provides a promising framework for thyroid carcinoma risk stratification and offers insights into personalized immunotherapy. - Source: PubMed
Publication date: 2026/08/12
Jiang LingZou LiuyiLiu FeiqiXiong DonglinYi Chenggen - Esophageal squamous cell carcinoma (ESCC) represents a highly lethal malignancy. The FOXA2 was involved in cellular proliferation, differentiation, tumorigenesis, and metastasis. The precise regulatory mechanisms of FOXA2 in ESCC progression remain unclear. - Source: PubMed
Publication date: 2026/07/17
Xia WenqiangLi NingGong ChunxiaLi XinLi HaoShi BowenChen Hezhong - Sweet syndrome (SS) is an autoinflammatory neutrophilic dermatosis characterized by abrupt-onset inflammatory skin lesions and systemic symptoms, yet its molecular pathogenesis remains incompletely defined. To delineate disease-specific inflammatory programmes, we performed NanoString-based transcriptomic analysis of SS skin lesions and compared them with healthy control skin and pyoderma gangrenosum, a related neutrophilic dermatosis. SS exhibited a distinct inflammatory transcriptional signature marked by robust upregulation of type I and II interferon-stimulated genes, including CXCL9, CXCL10, GBP1, GBP5, IFIT2 and IRF7, distinguishing SS from both control groups. Cell type deconvolution analysis revealed enrichment of dendritic cells, consistent with a prominent type I interferon-driven immune response. In parallel, SS lesions demonstrated altered immunoproteasome gene expression, with upregulation of immunoproteasome subunits PSMB8, PSMB9 and PSMB10 and downregulation of the constitutive subunit PSMB7 suggesting functional remodelling of proteasomal activity. Together, these findings support a model in which dendritic cell-driven interferon signalling promotes immunoproteasome remodelling and sustains neutrophilic inflammation in Sweet. This study identifies a prominent interferon signalling as a defining molecular feature of SS and highlights potential therapeutic opportunities within the interferon-JAK/STAT and proteasome pathways. - Source: PubMed
Calabrese LauraMoltrasio ChiaraRomagnuolo MaurizioStadler Pia-CharlotteFiocco ZenoNeulinger-Muñoz MatthiasAoki RuiD'Onghia MartinaRubegni PietroKerl KatrinSatoh Takashi KMarzano Angelo VFrench Lars E - 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