Ask about this productRelated genes to: WDR46 antibody
- Gene:
- WDR46 NIH gene
- Name:
- WD repeat domain 46
- Previous symbol:
- C6orf11
- Synonyms:
- BING4, UTP7
- Chromosome:
- 6p21.32
- Locus Type:
- gene with protein product
- Date approved:
- 2001-04-06
- Date modifiied:
- 2016-10-05
Related products to: WDR46 antibody
Related articles to: WDR46 antibody
- The role of RNA N6-methyladenosine (m6A) modifications in modulating the immune microenvironment during ischemic stroke (IS) pathogenesis remains poorly characterized. This investigation systematically explores m6A-mediated immune regulation in IS and identifies critical immune-related biomarkers. Transcriptomic profiles from 108 IS samples were analyzed to discern m6A regulatory patterns. Single-sample gene set enrichment analysis (ssGSEA) and gene set variation analysis (GSVA) quantified immune cell infiltration and pathway activity across IS subtypes and controls. Weighted gene co-expression network analysis (WGCNA) identified m6A-associated gene modules. Two complementary machine learning approaches were applied to identify the key immune-related genes implicated in IS pathogenesis. The robustness of these findings was subsequently confirmed through a comprehensive meta-analysis integrating six independent datasets. Eight dysregulated m6A regulators distinguished IS from controls. Unsupervised clustering delineated two distinct m6A modification patterns (Clusters A/B) with divergent immune landscapes: Cluster B exhibited heightened infiltration of natural killer cells, eosinophils, and activated CD4 T cells, coupled with IL6/JAK/STAT3 pathway activation, whereas Cluster A demonstrated enrichment of immature dendritic cells and monocytes, alongside oxidative phosphorylation signaling. WGCNA identified a conserved immune-related module (black module, = 0.68 with Cluster B) containing 322 co-expressed genes. Cross-validation by machine learning nominated five candidate biomarkers (, , , , C19orf24) showing consistent expression trends in internal validation cohorts (Control vs. IS and Cluster A vs. B). External validation via meta-analysis confirmed as a protective factor against IS susceptibility (odds ratio [OR] = 0.74, 95% confidence interval [CI]: 0.57–0.97), while (OR = 1.46, 95% CI: 1.01–2.10) and (OR = 1.57, 95% CI: 1.12–2.22) were significantly associated with increased IS susceptibility, establishing their roles as risk genes. Subsequent RT-qPCR analysis in clinical samples further validated the results of the aforementioned external validation. Moreover, ROC analysis revealed an AUC of 0.88 (95% CI: 0.82–0.94) for , 0.82 (95% CI: 0.74–0.89) for , and 0.90 (95% CI: 0.84–0.96) for . This study establishes m6A epitranscriptomic remodeling as a pivotal orchestrator of immune microenvironment heterogeneity in IS. The identification of , , and as promising biomarkers not only enhances the potential for precise diagnosis but also provides actionable targets for immunomodulatory therapy in IS. - Source: PubMed
Publication date: 2025/08/28
Zheng Peng-FeiHuang Cheng-ChengWang Chang-LuZhou TingPan Hong-WeiHe JinHong Xiu-QinHuang Li-ZhongRong Jing-JingShi Xiang-Jiang - The HBV core protein (HBC) is crucial for the progression of HCC. WD repeat-containing (WDR) 46 (WDR46) is implicated in the development of different tumors. Nevertheless, whether WDR46 is controlled by HBC to drive hepatocarcinogenesis remains unclear. - Source: PubMed
Publication date: 2025/05/06
Kong FanyunBao EnsiZhong YujieWang YuxinLiu RuyuZhang HuanyangYang LuJiang RongLiu XuankeLi ChenLiu XiangyePan XiuchengZheng KuiyangYou HongjuanTang Renxian - Acquired immunodeficiency syndrome is a systemic infectious disease caused by human immunodeficiency virus infection, which could attack the bones and heart. However, the relationship between Nuclear Complex Associated 3 Homolog (NOC3L) and DEAD box helicase 17 (DDX17) and acquired immunodeficiency complicated with viral myocarditis and osteoporosis is unclear. The acquired immune deficiency dataset GSE140713, GSE147162 and the osteoporosis dataset (GSE230665), and viral myocarditis dataset (GSE150392) configuration files were generated from gene expression omnibus. The differentially expressed genes (DEGs) were screened and performed weighted gene co-expression network analysis. Construction and analysis of protein-protein interaction network. Functional enrichment analysis, gene set enrichment analysis, immune infiltration analysis, gene expression heatmap, and comparative toxicogenomics database analysis were performed. TargetScan screens miRNAs of DEGs. Thousand three hundred thirty-five DEGs were identified. According to gene ontology, they are mainly concentrated in the regulation of RNA biosynthesis, cytoplasmic ribosome, and the DNA binding transcription factor activity. In Kyoto Encyclopedia of Genes and Genomes analysis, they are mainly concentrated in TGF-β signal pathway, Notch signaling pathway, cAMP signaling pathway, and Apelin signaling pathway. Gene set enrichment analysis shows that DEGs are mainly enriched in cytoplasmic ribosome, transcriptional regulator activity, DNA binding transcription factor activity, TGF-β signal pathway, and Notch signal pathway. In the enrichment project of Metascape, tyrosine kinase receptor signaling, growth regulation, and enzyme-linked receptor protein signaling pathways can be seen in the gene ontology enrichment project. Four core genes (NOC3L, WDR46, SDAD1, and DDX17) were obtained. Core genes (NOC3L, WDR46, SDAD1, and DDX17) were low expressed in both acquired immunodeficiency and osteoporosis samples. Comparative toxicogenomics database analysis showed that core genes (NOC3L, WDR46, SDAD1, and DDX17) were associated with inflammation necrosis. The expressions of NOC3L and DDX17 are low in acquired immunodeficiency combined with viral myocarditis and osteoporosis. - Source: PubMed
Xiao LipingLi XinWang Jing-JingQuan Xue-MinZhao Chang-Song - Air pollution and transportation noise pollution has been linked to gastrointestinal (GI) diseases, but their relationship remains unclear. - Source: PubMed
Publication date: 2024/08/18
Zhan Zhi-QingLi Jia-XinChen Ying-XuanFang Jing-Yuan - About 40% of patients with diffuse large B-cell lymphoma (DLBCL) develop drug resistance after first-line chemotherapy, which remains a major cause of morbidity and mortality. The emergence of DLBCL drug resistance is mainly related to Adriamycin. Our previous research shows that Paclitaxel could be a potential therapeutic drug for the treatment of Adriamycin-resistant DLBCL. Based on the results of RNA-seq and integrated network analysis, we study the potential molecular mechanism of Paclitaxel in the treatment of Adriamycin-resistant DLBCL in multiple dimensions. A CCK-8 assay showed that the inhibitory effect of Paclitaxel on Pfeiffer and Pfeiffer/ADM (Adriamycin-resistant DLBCL cell lines) is significantly higher than that of Adriamycin ( ＜ 0.05). Five hub genes (UBC, TSR1, WDR46, HSP90AA1, and NOP56) were obtained via network analysis from 971 differentially expressed genes (DEGs) based on the RNA-seq of Paclitaxel-intervened Pfeiffer/ADM. The results of the network function module analysis showed that the inhibition of Pfeiffer/ADM by Paclitaxel was closely related to ribosome biosynthesis in eukaryotes. The results of RT-qPCR showed that the mRNA levels of the five hub genes in the Pfeiffer/ADM group were significantly lower than those in the Pfeiffer group and the Pfeiffer/ADM Paclitaxel-treated group ( ＜ 0.05). Consistent with studies, Paclitaxel exhibited a significant inhibitory effect on Adriamycin-resistant DLBCL, which may have played a role in the five hub genes (UBC, TSR1, WDR46, HSP90AA1 and NOP56) and ribosome biosynthesis in eukaryotes pathway, but the specific regulation needs further experimental verification. - Source: PubMed
Hong HaoyuanLuo BinQin YingyingLi SizhuPeng Zhigang