CD53
- Known as:
- CD53
- Catalog number:
- 11-227-C100
- Product Quantity:
- 0.1 mg
- Category:
- -
- Supplier:
- Exbio
- Gene target:
- CD53
Related genes to: CD53
- Gene:
- CD53 NIH gene
- Name:
- CD53 molecule
- Previous symbol:
- MOX44
- Synonyms:
- TSPAN25
- Chromosome:
- 1p13.3
- Locus Type:
- gene with protein product
- Date approved:
- 1991-07-16
- Date modifiied:
- 2016-10-05
Related products to: CD53
Related articles to: CD53
- Diabetic Kidney Disease (DKD) is a primary cause of end-stage renal disease, characterized by podocyte dysfunction. While current clinical biomarkers including albuminuria and estimated glomerular filtration rate are foundational for diagnosis, they often function as lagging indicators and fail to detect early renal injury. Recently, N6-methyladenosine (m6A) RNA methylation has emerged as a key epitranscriptomic link between hyperglycemia and podocyte injury, offering a novel frontier for diagnostic biomarker discovery. This review shifts the traditional focus from basic molecular mechanisms to the preclinical translational potential of m6A regulators and their downstream targets as early detection tools. Current experimental evidence in cell and animal models indicates that the abnormal upregulation of , , and may reshape podocyte fate through complex regulatory pathways. These alterations appear to enhance the stability of harmful transcripts while degrading protective ones. Furthermore, specific regulators and their associated hub genes, such as and , show emerging promise as candidate non-invasive biomarkers that correlate with immune infiltration and early renal function decline in preclinical settings, though robust clinical validation remains necessary. - Source: PubMed
Publication date: 2026/07/22
Liu HuihuiLuo JunDai YinzhongWu ChenguangAn YanJiang Wei - In human acute myeloid leukemia (AML), a sub-population of leukemia stem cells (LSCs) drive disease initiation, therapeutic resistance, and relapse. However, the lack of reliable markers to distinguish LSCs from bulk leukemia cells has impeded progress in studying LSC pathogenesis and developing meaningful LSC-specific diagnostics and therapeutics. Existing LSC gene signatures, derived from bulk populations, cannot definitively identify LSCs at single-cell resolution. To address this, we analyzed large patient cohorts with bulk gene expression data and single-cell multi-omic assays to identify a prognostic gene signature that is specifically enriched in a clinically adverse AML sub-population. Using this signature, we defined and prospectively isolated CD34+CD90-CLL1-CD69+CD53- immunophenotypic LSCs that are significantly enriched for LSC content based on limiting dilution xenotransplantation assays. Our findings demonstrate the power of single-cell multi-omics to precisely identify a clinically relevant LSC population and establish a clear framework for future translational research in AML. - Source: PubMed
Publication date: 2026/07/13
Ediriwickrema AsiriNakauchi YusukeKöhnke ThomasFan Amy CHu XiaoyiBenard Brooks AKarigane DaikiLinde Miles HNewman Aaron MGentles Andrew JMajeti Ravindra - Lung adenocarcinoma (LUAD) is the most common subtype of lung cancer, often diagnosed at advanced stages with poor prognosis. CD53, a tetraspanin involved in immune regulation, has an unclear role in LUAD. - Source: PubMed
Publication date: 2026/06/11
Chen JunTan WeimingWei YichenHan ZexianZhu JichongWei Kanglai - Systemic lupus erythematosus (SLE) is a multifactorial autoimmune disease characterised by loss of immune tolerance and chronic inflammation, but its molecular pathogenesis remains incompletely understood. In this work we examine whether immune regulatory transcripts and pathways are recurrently detectable across heterogeneous publicly available SLE transcriptomic datasets, and we explore one of the recurrent pathways with a kinetic systems-biology model. We analyzed one microarray dataset (E-GEOD-46923, profiled on Affymetrix HG-U133A and HG-U133B), two bulk RNA-seq datasets (E-MTAB-7145, E-MTAB-11919), and two single-cell RNA-seq datasets (GSE135779, GSE163121) using a unified differential-expression criterion (|log2 fold change|≥ 1, Benjamini-Hochberg adjusted p-value < 0.05). KEGG pathway enrichment was performed with a per-dataset background gene universe. A mass-action kinetic model of Th1/Th2 differentiation was constructed in MATLAB SimBiology, and global sensitivity analysis was performed using the variance-based Sobol method. Across the heterogeneous datasets, CD53, IFITM1, and RPL11 were recurrently identified as differentially expressed transcripts, and the Th1/Th2 cell differentiation pathway, together with related cytokine-cytokine receptor and JAK-STAT pathways, emerged as a recurrent immune-regulatory signal. Systems-biology simulation under SLE-derived initial conditions predicted atypical IL-2 and GATA3 expression dynamics, which is consistent with, but does not by itself prove, the cytokine-signalling dysregulation reported in SLE. Sobol sensitivity analysis identified IL-4 and the modelled co-stimulatory and Notch ligand species (CDN1-6 and Jagged1/2) as the largest non-additive regulators of IL-2 in the model. Overall, this work integrates transcriptomic recurrence analysis with kinetic modelling to generate testable hypotheses regarding immune regulatory dysfunction in SLE. - Source: PubMed
Publication date: 2026/06/15
Saleem KashifParacha Rehan ZafarKhalid LintaManzoor AyeshaNisar MaryumMurad DidarDin Nazar MuhammadAmir Afreenish - BackgroundAxillary lymph node metastasis (ALNM) serves as a critical prognostic determinant in breast cancer, yet the molecular drivers governing lymphatic dissemination remain poorly characterized. Integrating single-cell transcriptomic profiling with Mendelian-randomization (MR)-based genetic prioritization may help reveal cell type-specific mechanisms underlying metastatic progression.MethodsWe analyzed the GSE195861 single-cell RNA sequencing dataset encompassing six invasive ductal carcinoma (IDC) samples and paired ALNM specimens. t-distributed Stochastic Neighbor Embedding-based clustering and SingleR annotation delineated cellular heterogeneity, while differential expression analysis identified metastasis-associated genes in epithelial compartments. MR analysis employing five robust methods (inverse variance-weighted, weighted median, MR-Egger, simple/weighted mode) integrated genome-wide association study data (GCST90018799) to establish causal gene-breast cancer associations. CellChat reconstructed ligand-receptor networks across nine annotated cell types.ResultsUnsupervised clustering resolved 27 cell clusters into nine lineages, revealing ALNM-specific expansion of monocytes, pre-B cells, and CD34+ hematopoietic stem cells (HSCs). Epithelial cells exhibited 2421 differentially expressed genes (DEGs) between IDC and ALNM, including 12 genes whose genetically predicted expression showed significant associations with breast cancer risk in MR analysis (P < 0.05). CD53 (odds ratio (OR) = 1.110, 95% confidence interval (CI) = 1.019-1.209, P = 0.017) and TCDD-inducible poly-ADP-ribose polymerase (TIPARP) (OR = 1.153, 95% CI = 1.032-1.288, P = 0.012) were prioritized as candidate genes, as their genetically predicted expression was associated with increased breast cancer risk in weighted median MR. Cell-cell communication analysis implicated macrophage-derived midkine-nucleolin signaling and B-cell-orchestrated macrophage migration inhibitory factor-(CD74 + CXCR4) axis in metastatic crosstalk. Functional enrichment linked DEGs to extracellular matrix remodeling and MAPK/PI3K-Akt activation.ConclusionThis multi-omics integration prioritizes CD53 and TIPARP as ALNM-associated candidate genes with genetically supported associations with breast cancer risk, with macrophage-epithelial and B-cell-HSC interactions serving as potential therapeutic targets. Our findings provide a roadmap for developing metastasis-interceptive strategies through precision targeting of the ALNM-associated tumor microenvironment. - Source: PubMed
Publication date: 2026/05/12
Qu LimengLi JinyangDing ShirongLong QianYi Wenjun