Ask about this productRelated genes to: VCAM1 antibody
- Gene:
- VCAM1 NIH gene
- Name:
- vascular cell adhesion molecule 1
- Previous symbol:
- -
- Synonyms:
- CD106
- Chromosome:
- 1p21.2
- Locus Type:
- gene with protein product
- Date approved:
- 1991-07-10
- Date modifiied:
- 2016-10-05
Related products to: VCAM1 antibody
Related articles to: VCAM1 antibody
- Diabetic kidney disease (DKD) affects approximately 40% of patients with diabetes mellitus and remains a leading cause of end-stage renal disease worldwide. Early diagnosis and identification of therapeutic targets are critical for improving patient outcomes, yet reliable biomarkers are lacking. This study integrated transcriptomic data from the Gene Expression Omnibus (GEO) database (GSE96804, GSE30528, and GSE142025) with machine learning algorithms and Mendelian randomization (MR) to identify diagnostic biomarkers for DKD. Differentially expressed genes (DEGs) were identified and intersected with key modules from weighted gene co-expression network analysis (WGCNA). Four machine learning methods-least absolute shrinkage and selection operator (LASSO), random forest (RF), support vector machine-recursive feature elimination (SVM-RFE), and extreme gradient boosting (XGBoost)-were applied for feature selection. Five hub genes (SPP1, CD44, VCAM1, C3, and TIMP1) were identified at the intersection of these approaches. Two-sample MR analysis using eQTL data from the eQTLGen Consortium and kidney function GWAS from the CKDGen Consortium provided evidence supporting potential causal associations between SPP1, C3, and TIMP1 expression and estimated glomerular filtration rate decline. Immune infiltration analysis via CIBERSORT estimated elevated proportions of M1 macrophages and activated CD4 memory T cells in DKD samples, with all five hub genes showing correlations with macrophage infiltration. A diagnostic model based on these five genes achieved a cross-validated area under the receiver operating characteristic curve (CV-AUC) of 0.938 in the discovery dataset and AUC values of 0.917 and 0.889 in two independent external validation cohorts. Drug-gene interaction analysis identified 10 candidate compounds targeting the hub genes. These findings provide a computational framework for identifying candidate diagnostic biomarkers and generating hypotheses regarding potential therapeutic targets for DKD; however, all results are derived from in silico analyses and require experimental validation-including qPCR, immunohistochemistry, and prospective clinical cohort studies-before clinical applicability can be established. - Source: PubMed
Publication date: 2026/04/23
Liu HaiwenFu QiangChen Jing - Transforming growth factor-β1 (TGF-β1) and tumor necrosis factor-α (TNF-α) are central regulators of vascular inflammation and remodeling in coronary artery disease. However, their cell-type-specific and context-dependent effects in primary human coronary artery endothelial cells (ECs) and vascular smooth muscle cells (VSMCs) remain incompletely defined. Primary human coronary artery endothelial cells (pHCAECs) and smooth muscle cells (pHCASMCs) were stimulated with TGF-β1 (10 ng/mL), TNF-α (100 ng/mL), or their combination. Canonical SMAD2/3 activation, Krüppel-like factor 11 (KLF11) expression, cytoskeletal and junctional remodeling, vascular cell adhesion molecule-1 (VCAM-1) expression, migration dynamics (wound healing and confluent assays), and endothelial tube formation were assessed using immunofluorescence microscopy, live-cell imaging, and quantitative trajectory analysis. Both cytokines were associated with increased nuclear pSMAD2/3 signal in ECs and VSMCs, consistent with functional interplay between inflammatory and TGF-β-related signaling pathways. In pHCAECs, TNF-α robustly induced VCAM-1 functional expression and disrupted VE-cadherin continuity, whereas TGF-β1 primarily promoted cytoskeletal remodeling without strong inflammatory activation. TGF-β1 increased endothelial migration velocity and accumulated distance. In contrast, TNF-α preferentially enhanced Euclidean displacement and directional persistence, shifting the migratory pattern toward more directed movement most evident under combined TGF-β1 + TNF-α stimulation. Notably, TGF-β1 significantly reduced endothelial tube formation, indicating impaired network organization rather than proangiogenic activity. In pHCASMCs, TGF-β1 enhanced migratory activity, particularly in confluent monolayers, whereas TNF-α enhanced directional displacement. KLF11 was induced by TGF-β1 in both pHCAECs and pHCASMCs. In pHCAECs, TNF-α also increased KLF11 and co-stimulation promoted nuclear enrichment, whereas in pHCASMCs TNF-α alone was not effective and combined treatment amplified the TGF-β1 response, supporting cell-type-specific integration of inflammatory and TGF-β-dependent signals. TGF-β1 and TNF-α differentially regulate the inflammatory activation and migration of primary human coronary vascular cells in a cell-type- and structural-context-dependent manner. TGF-β1 enhances migratory force generation, whereas TNF-α reinforces directional polarization, and their integration determines effective vascular repair dynamics. Canonical SMAD2/3 activation does not uniformly predict functional outcome, and KLF11 was identified as a context-sensitive transcription-associated factor showing differential nuclear localization in response to cytokine stimulation, representing a hypothesis-generating observation for future mechanistic studies. - Source: PubMed
Publication date: 2026/04/29
Bonowicz-Kozłowska KlaudiaJerka DominikaTwardak DamianKleszczyński KonradGagat Maciej - Endothelial dysfunction (ED) arises in multiple pathologies, and its severity correlates with disease progression. Common ED biomarkers could provide prognostic value for associated complications. This study aims to identify shared ED biomarkers and assess their prognostic significance. Endothelial cells in culture (human microvascular endothelial cells, HMEC-1) were exposed to sera from patients in five disease groups ( = 20 patients/group)-liver cirrhosis with portal hypertension, idiopathic pulmonary arterial hypertension, placental disorders such as intrauterine growth restriction, coronary artery disease with acute myocardial infarction, and chronic kidney disease-or matched controls, in the absence/presence of anti-inflammatory (apixaban) and antioxidant (EUK134) compounds. We explored changes in: VCAM-1, ICAM-1, eNOS, VWF, extracellular matrix thrombogenicity, and reactive oxygen species (ROS). In serum samples, proteomics and metabolomics analyses (including lipids, amino acids, and polar metabolites) were performed through an extraction protocol to identify common ED biomarkers. Expression of VCAM-1, ICAM-1, VWF, platelet adhesion, and ROS increased in most groups versus controls ( < 0.05). Both drugs decreased all biomarker levels except eNOS ( = 6 for in vitro experiments). For serum ED biomarkers, 18 metabolites and 24 proteins showed AUC-ROC and hit rates >77.5%, and six metabolites were associated with event-free survival. These diseases share ED driven by systemic inflammatory, oxidative, and metabolic stress, are partially reversible in vitro, and are linked to biomarkers associated with clinical outcomes. Overall, ED emerges as a modifiable pathological axis with potential prognostic value. - Source: PubMed
Publication date: 2026/04/25
Martinez-Sanchez JuliaTorramadé-Moix SergiMoreno-Castaño Ana BelénLafoz EricaRovira JordiDiekmann FritzRodas Lida MariaCuadrado-Payán ElenaGalceran IsabelCases AleixDantas Ana PaulaBarberà Joan AlbertTura-Ceide OlgaCrispi FàtimaGratacós EduardGarcía-Calderó HéctorGarcía-Pagán Juan CarlosHernández-Gea VirginiaEscolar GinesPereira ArturoDiaz-Ricart Maribel - Sepsis remains a formidable challenge in critical care, and is characterized by profound circulatory and cellular abnormalities driven by both systemic inflammation and widespread endothelial dysfunction. However, the relative predictive utility of biomarkers representing these pathways versus standard clinical data is uncertain. In this analysis, we sought to conduct a comparative analysis of predictive models for forecasting two critical outcomes in sepsis patients: persistent vasopressor dependence and acute kidney injury (AKI). We prospectively enrolled a cohort of suspected sepsis patients recruited from the emergency departments of three secondary and tertiary-level teaching hospitals. We developed three distinct machine learning models via LightGBM: Model A (endothelial: angiopoietin-2, VCAM-1, and E-selectin), Model B (inflammatory: procalcitonin, CRP, and IL-6), and Model C (clinical: SOFA score and Lactate). The models were examined for their accuracy in predicting persistent vasopressor dependence and the development of KDIGO stage ≥2 AKI. For predicting persistent vasopressor dependence, the clinical model (Model C) secured a strikingly high Area Under the Receiver Operating Characteristic Curve (AUROC) of 0.92, which was statistically superior to both the endothelial Model A (AUROC 0.53, p=0.02) and the inflammatory Model B (AUROC 0.49). For predicting AKI, the clinical model again achieved optimal results with an AUROC of 0.81, followed by the endothelial model (AUROC 0.73), although this difference was not statistically significant (p=0.38). Our findings, contrary to our initial hypothesis, demonstrate that a model based on readily available clinical data (SOFA and lactate) provides superior predictive accuracy for vasopressor dependence and AKI compared with models based on specific endothelial or inflammatory biomarker panels. This highlights the robust, integrated nature of clinical scoring systems and underscores the importance of benchmarking novel biomarker models against established clinical standards. - Source: PubMed
Publication date: 2026/05/12
Ningthoujam Avichandra SinghThiyagarajan GomathiWani Niyaz AhmadSharma ShilpaChen Kuan FuNandi Avishek - Extracellular vesicles (EVs) play a critical role in intercellular communication, yet the contribution of the EV corona and associated surface structures, such as heparan sulphate glycosaminoglycan (HS GAG) chains, to EV function remains poorly understood. In this study, we highlight a hitherto unknown requirement of HS GAG chains for the simultaneous delivery of a myriad assortment of growth factors by EVs. We demonstrate an attenuated function following enzymatic removal of HS GAG chains from the surface of prostate cancer (PCa)-derived EVs, using heparinase III (HepIII). Our results confirm that digestion of HS GAG chains is specific and does not compromise EV integrity regarding size or tetraspanin expression. Enzymatic removal of HS GAG chains did, however, substantially altered the vesicular protein profile, reducing the expression of factors such as midkine, CYR61 and TFPI implicating HS GAG chains as a mode of tethering these factors to the EV surface. Importantly, EV-associated HS GAG chains are required for functional delivery of such factors, resulting in successful activation of cellular signalling pathways for SCF, IGF-1, midkine and VEGF in recipient fibroblasts. Furthermore, HS GAG chain removal attenuated EV-induced fibroblast production of pro-angiogenic factors VEGF and angiogenin as well as inflammatory factors VCAM-1 and IL-1α alpha/IL-1F1, underscoring the role of vesicular HS GAG chains in mediating functional outcomes. These findings highlight the importance of EV surface HS GAG chains in growth factor delivery and signalling, providing new insights into the EV corona and its relevance in pathological processes relating to modulation of the tissue microenvironment. - Source: PubMed
Publication date: 2026/05/09
Veiga SaraShephard Alex PMilward KateCocks AlexRoyo FélixFalcon-Perez Juan MClayton AledWebber Jason P