PSMD6 antibody - N-terminal region (ARP32400_P050)
- Known as:
- PSMD6 (anti-) - N-terminal region (ARP32400_P050)
- Catalog number:
- arp32400_p050
- Product Quantity:
- USD
- Category:
- -
- Supplier:
- Aviva Systems Biology
- Gene target:
- PSMD6 antibody - N-terminal region (ARP32400_P050)
Related genes to: PSMD6 antibody - N-terminal region (ARP32400_P050)
- Gene:
- PSMD6 NIH gene
- Name:
- proteasome 26S subunit, non-ATPase 6
- Previous symbol:
- -
- Synonyms:
- S10, p44S10, KIAA0107, Rpn7
- Chromosome:
- 3p14.1
- Locus Type:
- gene with protein product
- Date approved:
- 1995-12-06
- Date modifiied:
- 2015-08-12
Related products to: PSMD6 antibody - N-terminal region (ARP32400_P050)
Related articles to: PSMD6 antibody - N-terminal region (ARP32400_P050)
- Pediatric gliomas, comprising both low-grade (LGGs) and high-grade gliomas (HGGs), exhibit significant molecular and clinical heterogeneity. While LGGs generally have a favorable prognosis, HGGs are associated with poor long-term survival despite aggressive treatment. Advances in molecular profiling have enabled targeted therapies, but treatment resistance and tumor heterogeneity remain major challenges. The integration of artificial intelligence (AI) and transcriptomic data holds promise for refining prognostic models and guiding personalized treatment strategies, yet its application in pediatric gliomas remains underexplored. - Source: PubMed
Publication date: 2026/03/09
Li GanglongPei FuyuWang Weizhen - Hepatocellular carcinoma (HCC) is a lethal malignancy with limited diagnostic biomarkers. The present study aims to comprehensively investigate the expression, clinical prognostic significance, and potential mechanisms of PSMD6 in HCC through comprehensive bioinformatics analyses and experimental validation. expression in HCC was analyzed using data from the UALCAN, SANGERBOX and TIMER databases. The association between PSMD6 expression and clinicopathological features, patient prognosis, and immune cell filtration was evaluated. Functional enrichment analyses (Gene Ontology and Kyoto Encyclopedia of Genes and Genomes) were performed to identify PSMD6-related signaling pathways, and a protein-protein interaction (PPI) network was constructed using the BioGRID and STRING databases. Key findings from bioinformatics analyses were validated using reverse transcription-quantitative (RT-qPCR) and western blotting in HCC cell lines. expression was significantly upregulated in HCC compared with adjacent normal tissues (P<0.001), which has been consistently validated by various databases and confirmed by experiments using RT-qPCR and western blotting. High PSMD6 expression was significantly associated with advanced tumor grade and patient age and served as an independent predictor of poor overall survival (P<0.001). In addition, demonstrated high diagnostic accuracy for HCC (area under the curve=0.877). Moreover, expression showed a positive correlation with the infiltration levels of CD4 T cells and B cells in HCC (P<0.05), independent of tumor purity (P>0.05). Functional enrichment analysis indicated that PSMD6 was involved in critical oncogenic pathways, including the cell cycle. PPI network analysis revealed that interact with several key proteins, such as and to achieve a regulatory function in HCC. In conclusion, is significantly overexpressed in HCC and is strongly associated with tumor progression and poor prognosis. It represents a promising diagnostic biomarker and a potential therapeutic target for HCC. - Source: PubMed
Publication date: 2026/02/24
Liu JianWang TingZhu Zhenhua - Sarcopenia is a major health concern characterized by progressive loss of muscle mass and function among the elderly. Its prevalence ranges from 10% to 27% in individuals over 60 and increases further in those above 80. The condition reduces mobility and strength, increasing risks of falls, fractures, and other age-related health issues. The ubiquitin-proteasome system (UPS) plays a central role in muscle protein degradation and contribute to muscle wasting. However, therapeutic strategies targeting this pathway remain poorly understood. This study aimed to identify ubiquitination-related genes as potential therapeutic targets for sarcopenia using integrative bioinformatics analysis. This study utilized bioinformatics approaches to identify potential therapeutic targets for sarcopenia. Six GEO datasets (GSE136344, GSE28422, GSE1428, GSE8479, GSE9103, and GSE38718) were analyzed. Data were pre-processed, normalized, and batch effects were removed using the R packages "affy" and "sva." Differentially expressed genes were identified using the "limma" package. Weighted gene co-expression network analysis was performed to identify disease-associated genes. Functional enrichment analyses were conducted using the "clusterProfiler" package for Gene Ontology (GO) is a standardized, structured vocabulary (ontology) for describing the functions of genes and gene products across all species and Kyoto Encyclopedia of Genes and Genomes (KEGG) is a comprehensive database resource that integrates genomic, chemical, and systems-level functional information pathways. Differentially ubiquitinated genes were identified by intersecting differentially expressed genes with ubiquitination-related genes from MSigDB. Protein-protein interaction networks were constructed using STRING, and machine learning algorithms were applied to screen for diagnostic signature genes. Drug-gene interactions were analyzed using DGIdb, and molecular docking was performed using AutoDock. A total 734 differentially expressed genes were identified, including 373 up-regulated and 361 down-regulated genes. Gene set enrichment analysis revealed significant enrichment in 3 KEGG pathways. Weighted gene co-expression network analysis identified 1016 disease-associated genes for functional enrichment. Thirteen differentially ubiquitinated genes were identified, and 6 key diagnostic signature genes (CBLB, PSMD6, RNF115, SMAD3, UCHL3, and ZBTB16) were selected through machine learning. Five genes (CBLB, RNF115, SMAD3, UCHL3, and ZBTB16) exhibited significant differences between older and younger groups, with ROC AUC values > 0.7. Drug prediction identified ASPARTIC ACID and CETYLPYRIDINIUM as potential agents targeting CBLB and SMAD3. This study identified key differentially ubiquitinated genes with potential therapeutic implications for sarcopenia. The findings highlight the importance of the UPS in muscle wasting and provide a foundation for further mechanistic and therapeutic research. - Source: PubMed
Liu XiaoMingLi RenKuang YingYan - Congenital heart defects (CHDs) occur in about 1% of live births and are the leading cause of infant death due to birth defects. While there have been remarkable efforts to pursue large-scale whole-exome and genome sequencing studies on CHD patient cohorts, it is estimated that these approaches have thus far accounted for only about 50% of the genetic contribution to CHDs. We sought to take a new approach to identify genetic causes of CHDs. By combining analyses of genes that are under strong selective constraint along with published embryonic heart transcriptomes, we identified over 200 new candidate genes for CHDs. We utilized protein-protein interaction (PPI) network analysis to identify a functionally-related subnetwork consisting of known CHD genes as well as genes encoding proteasome factors, in particular POMP, PSMA6, PSMA7, PSMD3, and PSMD6. We used CRISPR targeting in zebrafish embryos to preliminarily identify roles for the PPI subnetwork genes in heart development. We then used CRISPR to create new mutant zebrafish strains for two of the proteasome genes in the subnetwork: pomp and psmd6. We show that loss of proteasome gene functions leads to defects in zebrafish heart development, including dysmorphic hearts, myocardial cell blebbing, and reduced outflow tracts. We also identified deficits in cardiac function in pomp and psmd6 mutants. These heart defects resemble those seen in zebrafish mutants for known CHD genes and other critical heart development genes. Our study provides a novel systems genetics approach to further our understanding of the genetic causes of human CHDs. - Source: PubMed
Publication date: 2025/08/26
Farr Gist HReid WhitakerHasegawa Eva HAzzam AzzamYoung IsabelleLi Mona LOlson Aaron KBeier David RMaves Lisa - We aimed to develop a predictive model integrating G2M-related genes to enhance the prognostication of colon cancer. - Source: PubMed
Publication date: 2025/08/10
Song XinDou YanShen Xiaoyong