Anti_human CCR8 (EL) rabbit polyclonal
- Known as:
- Anti_human CCR8 (EL) host: rabbit pab
- Catalog number:
- ASA905-278
- Product Quantity:
- 100 µg
- Category:
- -
- Supplier:
- Other suppliers
- Gene target:
- Anti_human CCR8 () rabbit polyclonal
Related genes to: Anti_human CCR8 (EL) rabbit polyclonal
- Gene:
- CCR8 NIH gene
- Name:
- C-C motif chemokine receptor 8
- Previous symbol:
- CMKBRL2, CMKBR8
- Synonyms:
- CY6, TER1, CKR-L1, GPR-CY6, CDw198
- Chromosome:
- 3p22.1
- Locus Type:
- gene with protein product
- Date approved:
- 1997-04-21
- Date modifiied:
- 2016-10-05
Related products to: Anti_human CCR8 (EL) rabbit polyclonal
Related articles to: Anti_human CCR8 (EL) rabbit polyclonal
- Peritoneal metastasis is a major contributor to progression, recurrence, and treatment failure in epithelial ovarian cancer (EOC) and is closely associated with dynamic remodeling of the peritoneal tumor microenvironment (TME). Interactions among tumor cells, ascites, the omental niche, stromal components, and immune cells collectively shape metastatic dissemination and therapeutic response. CC chemokines are important regulators of these processes, linking immune-cell trafficking with tumor-cell plasticity, metabolic adaptation, angiogenesis, and therapy resistance. This review summarizes the roles of CC chemokines in EOC progression within a chemokine-driven peritoneal niche remodeling framework encompassing four key stages: early dissemination, survival in ascites, omental colonization, and therapy resistance. Among the major signaling axes, CCL2-CCR2 is primarily associated with monocyte recruitment and myeloid-dominant immunosuppression, whereas CCL5-CCR5 is linked to stromal immune regulation and cancer stem-like phenotypes. Additional pathways, including CCL18, CCL20-CCR6, CCL22-CCR4, and CCL1-CCR8, contribute to T-regulatory cell recruitment, immune suppression, and hypoxia-associated responses. The review further discusses the limited efficacy of chemokine-targeted monotherapy, highlighting challenges posed by signaling redundancy, compensatory pathways, spatial heterogeneity, and insufficient biomarker-guided patient selection. Recent advances in single-cell and spatial transcriptomic technologies have improved the characterization of compartment-specific chemokine programs within the EOC microenvironment. Finally, emerging combination strategies involving chemokine blockade together with immune checkpoint inhibitors, metabolic interventions, PARP inhibitors, and ferroptosis-related approaches are evaluated. However, successful clinical translation will require precise patient stratification, effective toxicity management, and validation in clinically annotated cohorts. - Source: PubMed
Publication date: 2026/06/30
Xu BingYao JinhanHan MengruZhang Yuquan - The human chemokine receptor 8 (CCR8) plays a role in various autoimmune disorders, such as multiple sclerosis and inflammatory bowel disease, spurring the interest in CC8 agonism as a potential therapeutic strategy. Triazolyl substituted phenoxybenzylpiperidine analogues have been previously synthesized and were shown to act as CCR8 agonists although with moderate potency. In this study, their structure-activity relationship was expanded by the synthesis of a series of 1,4-disubstituted 1,2,3-triazole analogues with structural modifications of the phenoxybenzylpiperidinyl and phenyl moieties. Evaluation in cell-based assays revealed potent and selective CCR8 agonistic activity of several derivatives. Molecular docking was applied to shed a light on their binding mode. Despite its suboptimal pharmacokinetic behaviour, a representative CCR8 agonist from this series, showed activity in a humanized model mimicking xenogeneic graft-versus-host disease. - Source: PubMed
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Li QifeiVan Eynde WoutVan Loy TomClaes SandraSkarka AdamAndrys RudolfMusilek KamilPollenus EmilieSchlenner Susan MSchols DominiqueVoet ArnoutDehaen WimDe Jonghe Steven - Tissue resident memory (TRM) cells are of interest in chronic inflammatory skin diseases as they are believed to facilitate flares in the same anatomical area. IL-15 is an essential growth factor for the survival of TRM in the skin compartment. A main source of IL-15 are tissue-resident cells. - Source: PubMed
Publication date: 2026/04/25
Alase Adewonuola AMann CarolineKolb AntoniaWagle Sakshi VasantKlein MatthiasKramer DanielaWittmann Miriam - Cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC) are the primary histological subtypes of cervical cancer. Although the AGPAT family of enzymes is implicated in various cancer types, the specific roles of its members in cervical cancer remain unclear. In the present study, we investigated the value of AGPAT1-5 as potential biomarkers and therapeutic targets in cervical cancer by assessing their impact on disease development and outcomes. AGPAT gene expression data and clinical information from 306 patients with CESC and three control subjects were collected from The Cancer Genome Atlas. Given the limited number of normal cervical samples in TCGA (n = 3), we utilized the GEPIA database to integrate GTEx normal cervical samples (n = 10) as controls for differential expression analysis. These data were analyzed for differential gene expression, gene-gene and protein-protein interactions, prognostic and diagnostic value, clinical correlations, functional enrichment, and various tumor-infiltrating immune cell types. Validation was performed using two independent Gene Expression Omnibus (GEO) datasets (GSE6791 and GSE63514). The study revealed that AGPAT4 mRNA expression was significantly downregulated in cervical cancer tissues compared to normal tissues in the GEPIA analysis, while AGPAT1, AGPAT2, AGPAT3, and AGPAT5 showed no significant differences. Validation in GEO datasets demonstrated that AGPAT1, AGPAT2, and AGPAT3 were consistently downregulated in tumor tissues, whereas AGPAT5 was upregulated, and AGPAT4 showed no significant change. High levels of AGPAT3 and AGPAT4 expression, in particular, were associated with a worse prognosis in CESC patients. Immune infiltration analysis restricted to CESC samples revealed that AGPAT3 expression was significantly correlated with multiple immune cell types, including positive correlations with Macrophages M1, T cells CD4 memory resting, and Monocytes. Furthermore, guided by functional enrichment analysis implicating immune-related pathways, we examined the correlation between AGPAT3 expression and key T cell regulatory molecules, including FOXP3, IL2RA, IKZF2, and CCR8, revealing significant positive associations. In vitro assays demonstrated that knocking out AGPAT3 expression significantly decreased the proliferation and migration of HeLa and C-33 A cervical cancer cell line. These results suggest that AGPAT3 could be a valuable biomarker and a promising therapeutic target in cervical cancer. - Source: PubMed
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Gui NannanPan HanyiLu WeijuanPan GuiqiongZhou XiaoyuChen YuzhenJin MingyangYang ChangyongDong MingyouLiang Yuexiu - Inferring cellular dynamics from static single-cell data remains a central challenge in genomics. We introduce ArchVelo, a computational framework for modeling gene regulation and inferring trajectories from paired single-cell chromatin accessibility (scATAC-seq) and transcriptomic (scRNA-seq) data. ArchVelo represents chromatin accessibility as archetypes-shared regulatory programs-to model their dynamic influence on transcription. It outperforms existing methods in trajectory inference accuracy and gene-level latent time alignment, enables trajectory decomposition into archetypal components, and identifies the underlying transcription factors. After benchmarking on mouse brain and human hematopoiesis datasets, we apply ArchVelo to CD8 T cells in viral infection and reveal distinct trajectories of differentiation and proliferation. Focusing on progenitor exhausted CD8 T cells, critical for sustained immunity and immunotherapy response, we identify differentiation from Ccr6 to Ccr6 progenitors, shared between acute and chronic infections. ArchVelo provides a principled framework for modeling dynamic gene regulation and trajectory inference in multi-omic single-cell data across biological systems. - Source: PubMed
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