Polyclonal Rabbit ABCA6 Antibody
- Known as:
- Polyclonal Rabbit ABCA6 Antibody
- Catalog number:
- KA0039
- Product Quantity:
- 100ul
- Category:
- -
- Supplier:
- KareBay
- Gene target:
- Polyclonal Rabbit ABCA6 Antibody
Related genes to: Polyclonal Rabbit ABCA6 Antibody
- Gene:
- ABCA6 NIH gene
- Name:
- ATP binding cassette subfamily A member 6
- Previous symbol:
- -
- Synonyms:
- EST155051
- Chromosome:
- 17q24.2-q24.3
- Locus Type:
- gene with protein product
- Date approved:
- 1999-10-26
- Date modifiied:
- 2018-02-13
Related products to: Polyclonal Rabbit ABCA6 Antibody
Related articles to: Polyclonal Rabbit ABCA6 Antibody
- Osteoarthritis (OA) is a chronic joint disorder characterized by pain, reduced mobility, and structural degeneration. Despite its complex etiology and multi-tissue involvement, the molecular mechanisms underlying OA remain poorly understood. This study aimed to identify tissue-specific diagnostic biomarkers using an integrative framework combining multiple machine learning (ML) algorithms and SHapley Additive exPlanations (SHAP). Gene expression profiles from cartilage, synovium, and peripheral blood were retrieved from the GEO database. DEGs were identified across tissues, followed by feature selection using Least Absolute Shrinkage and Selection Operator(LASSO), Support Vector Machine Recursive Feature Elimination (SVM-RFE), and Random Forest(RF). Functional enrichment, gene set variation analysis (GSVA), and immune infiltration analyses were conducted. 10 ML models were constructed to evaluate diagnostic performance. A total of 8, 28, and 61 DEGs were identified in cartilage, synovium, and blood, respectively. Enrichment analysis revealed the key roles in inflammatory signaling, metabolism, and immune pathways. Biomarkers identified included CSN1S1, ABCA6, RARRES1, NPTX2 (cartilage); SCRG1, CXCL2, PTGDS, CCL19, BGN, KLF9 (synovium); and GNL3L, C6orf111, NT5C3, ZNF148 (blood). Immune analysis indicated shifts in mast cells and CD8 + T cells in cartilage and dendritic cells in synovium, while no significant immune alterations were found in blood. Diagnostic models demonstrated strong performance, with AUCs of 0.839 (cartilage), 0.934 (synovium), and 0.892 (blood). SHAP analysis was employed to interpret each model by quantifying the contribution of individual genes to predict outcomes. In the optimal cartilage model, CSN1S1 and ABCA6 were the most influential features, with mean absolute SHAP values of 0.146 and 0.122, respectively. For synovium, SCRG1 (0.111) and CXCL2 (0.097) were top contributors, while in blood, GNL3L (0.148) and C6orf111 (0.143) showed the highest predictive importance. These results underscore the interpretability of the models and validate the functional relevance of selected biomarkers. Collectively, this study provides a robust ML-based framework for identifying and interpreting reliable OA biomarkers across multiple tissues, offering valuable insights into disease mechanisms and supporting the development of diagnostic tools. - Source: PubMed
Publication date: 2026/03/09
Zhao JifengTao JiashengSong YizheYang JiyongLin XiaodongYe ZhilongLu ChaoZeng MingzhuChen WeijianLiu Wengang - Non-small cell lung carcinoma (NSCLC) is a leading cause of cancer-related mortality worldwide, highlighting the urgent need for early detection and targeted therapies. While lipopolysaccharide (LPS) metabolism and circadian rhythm disruption are emerging as important factors in cancer progression, their specific roles in NSCLC remain poorly understood. - Source: PubMed
Publication date: 2026/02/14
Chen YuZhao YifanKong QinghuaLi YabinJiang PingFan Lichao - We describe three unrelated individuals with congenital generalized hypertrichosis with gingival hyperplasia (CGHGH), each carrying a distinct structural rearrangement (duplication, deletion, inversion) at 17q24.2-q24.3 identified by CMA and WGS. Despite differences in the type of rearrangement, all three patients seem to exhibit alterations affecting the genomic architecture of a cluster of genes, particularly involving the ABCA family (notably ABCA5, ABCA6, ABCA9, ABCA10), MAP2K6, and potassium channels (KCNJ16, KCNJ2). These findings suggest that disruption of the local chromatin organization, including topologically associating domains (TADs), may contribute to the pathogenesis of CGHGH. Although previous studies implicated deletions affecting ABCA5 as the likely cause of CGHGH, our findings emphasize a broader spectrum of structural variation capable of producing similar phenotypes. Interestingly, one patient involved a cryptic 1.2 Mb inversion that disrupted the region between ABCA9 and KCNJ2, detectable only by whole genome sequencing, reinforcing the need for advanced molecular diagnostics in patients with syndromic hypertrichosis. In all three individuals, gingival overgrowth co-occurred with typical facial features, coarse hair, and normal cognitive development, adding evidence to the phenotype-genotype correlation. Overall, this study strengthens the hypothesis that disruption of regulatory elements and chromatin architecture at 17q24.2-q24.3, rather than single nucleotide variants alone, can be a primary driver of CGHGH. These findings underscore the need to incorporate genome-wide structural variant analysis in the diagnostic workflow of rare developmental disorders, especially those with heterogeneous or subtle clinical presentations. - Source: PubMed
Publication date: 2026/01/23
Tenorio-Castano JairFeito Martade Lucas RaúlSendagorta ElenaGómez-Fernández CristinaParra AlejandroVallespin ElenaGallego-Zazo NataliaCazalla MarioJiménez-Estrada Juan AMiranda-Alcaraz LuciaMora-Gómez MónicaRodríguez-Canó Manuel JesúsVázquez-Amell ValeriaRamos SergioValle TomásMansilla ElenaSantiago Fe GarcíaGalán-Gómez EnriqueCalpena EduardoRuíz-Pérez Víctor LNevado JuliánLapunzina Pablo - Rivaroxaban is a direct oral anticoagulant (DOAC) that directly inhibits coagulation factor Xa and exerts its anticoagulant effects. Although rivaroxaban generally exhibits predictable pharmacokinetic (PK) and pharmacodynamic (PD) profiles, significant interindividual variability in therapeutic responses exists. Research on the role of genetic factors in the clinical variability of rivaroxaban is relatively new and extensive. In this review, 12 pharmacogenetic studies on rivaroxaban were summarised, and 25 reported gene polymorphic sites were summarised, including (rs1045642, rs4148738, rs1128503, rs2032582, rs4728709, rs3789243 and rs3213619, (rs2231142, rs2231137, rs3114018, rs2622604 and rs1481012), (rs35599367, rs2242480, rs4646437 and rs12333983), (rs776746, rs15524, rs4646450), (rs890293), (rs4244285 and rs12248560), (rs7212506), (rs1738023 and rs1738025). The review provided an overview of the current state of research on rivaroxaban gene polymorphisms. However, due to the significant heterogeneity of existing studies and the lack of consistency in results, the evidence to date has limited impact. Therefore, larger-scale, global, multi-centre clinical trials are needed in the future to validate potential gene loci for testing. - Source: PubMed
Publication date: 2025/08/25
Wang LingChen GuoquanHu WeiChen JialeHe Yiling - JOURNAL/nrgr/04.03/01300535-202606000-00065/figure1/v/2026-02-11T151048Z/r/image-tiff Few studies have investigated alterations in the immune cell microenvironment of the dorsal root ganglia following spinal cord injury and whether these modifications facilitate axonal regeneration. In this study, we used a single-cell RNA sequencing dataset to create a comprehensive profile of the diverse cell types in the dorsal root ganglia and spinal cord of a mid-thoracic contusion injury model in cynomolgus monkeys. Cell communication analysis indicated that specific signaling events among various dorsal root ganglia cell types occur in response to spinal cord injury. Single-cell analysis using dimensionality reduction clustering identified distinct molecular signatures for nine cell types, including macrophage subpopulations, and differential gene expression profiles between dorsal root ganglia cells and spinal cord cells following spinal cord injury. The macrophage subpopulations were categorized into 11 clusters (MC0-MC10) based on differentially expressed genes, with the top 10 genes being ABCA6 , RBMS3 , EBF1 , LAMA4 , ANTXR2 , LAMA2 , SOX5 , FOXP2 , GHR , and APOD . MC0, MC1, and MC2 constituted the predominant macrophage populations. MC4, MC6, and MC9 were nearly absent in the spinal cord, but exhibited significant increases in the dorsal root ganglia post-spinal cord injury. Notably, these subpopulations possess a strong capacity for regulating axonal regeneration. The developmental progression of dorsal root ganglia macrophages after spinal cord injury was elucidated using cell trajectory and pseudo-time analyses. Genes such as EBF1 (MC6 and MC9 marker), RBMS3 (MC6 and MC9 marker), and ABCA6 (MC6 marker) showed high expression levels in the critical pathways of macrophage function. Through ligand-receptor pair analysis, we determined that the effects of macrophages on microglia are predominantly mediated through interaction pairs (e.g., SPP1-CD44, LAMC1-CD44, and FN1-CD44), potentially facilitating specific cellular communications within the immune microenvironment. The single-cell RNA sequencing dataset used in this study represents the first comprehensive transcriptional analysis of the dorsal root ganglia after spinal cord injury in cynomolgus monkeys, encompassing nearly all cell types within the dorsal root ganglia region. Using this dataset, we evaluated diverse subtypes of macrophages in the post- spinal cord injury dorsal root ganglia area and examined the signaling pathways that facilitate interactions among immune response-related macrophages in the dorsal root ganglia. Findings from this study provide a theoretical basis for understanding how the immune microenvironment influences the regenerative capacity of dorsal root ganglia neurons after spinal cord injury and offer novel insights into the complex processes underlying the pathobiology of spinal cord injury. - Source: PubMed
Publication date: 2025/03/25
Ren YimingLi BoYang BoFan BaoyouHuang ShenghuiShi GuidongLiu LiangWei ZhijianFeng Shiqing