Ccnb1
- Known as:
- Ccnb1
- Catalog number:
- 042883A
- Product Quantity:
- 250ul
- Category:
- -
- Supplier:
- ABM
- Gene target:
- Ccnb1
Related genes to: Ccnb1
- Gene:
- CCNB1 NIH gene
- Name:
- cyclin B1
- Previous symbol:
- CCNB
- Synonyms:
- -
- Chromosome:
- 5q13.2
- Locus Type:
- gene with protein product
- Date approved:
- 1991-12-10
- Date modifiied:
- 2016-10-05
Related products to: Ccnb1
anti-CCNB1 Cyclin B1 (V63)anti-CCNB1 Cyclin B1 (V63) type: Primary antibodies host: Mouseanti-CCNB1 (1B10)anti-CCNB1 (5G6)Anti-CCNB1 (V63), Mouse Monoclonal to CCNB1, Isotype IgG1, Host Mouseanti-CCNB1 / Cyclin B1 (V63)Antigens CCNB1, 1-433aa, Human, His tag, E.coli. Recombinantanti_Cyclin B1 (CCNB1)Bos taurus,Bovine,CCNB1,G2_mitotic-specific cyclin-B1Bovine cyclin B1 (CCNB1) ELISA kit, Species Bovine, Sample Type serum, plasmaBovine G2_mitotic-specific cyclin-B1(CCNB1) ELISA kitBovine G2_mitotic-specific cyclin-B1(CCNB1) ELISA kit SpeciesBovineCcn-2,Ccnb1,Ccnb1-rs13,Cycb,Cycb1,G2_mitotic-specific cyclin-B1,Mouse,Mus musculusCCNA1 Gene cyclin A1CCNB,CCNB1,G2_mitotic-specific cyclin-B1,Homo sapiens,Human Related articles to: Ccnb1
- Aflatoxin B1 (AFB1) is a potent group 1 carcinogen closely associated with hepatocellular carcinoma (HCC), particularly in regions with high dietary exposure and concurrent hepatitis B virus infection. However, the molecular mechanisms by which AFB1 promotes HCC progression remain incompletely understood, and there is a lack of prognostic models specifically tailored to AFB1-associated HCC. Integrating bioinformatics and network toxicology approaches may help identify key genes and construct reliable predictive tools for this unique subtype of liver cancer. This study aims to identify AFB1-liver cancer key genes and their functional pathways, to establish a prognosis prediction model for AFB1-liver cancer patient, and analyze its performance, and to reveal the binding characteristics between AFB1 and model proteins. - Source: PubMed
Publication date: 2026/06/24
Wang ChunmeiLi JingYuan YumanLiu LiliHe YanLei Bingxi - Nowadays, most cancer research still depends on traditional cell culture in Petri dishes or cell culture flasks which do not have the ability to mimic physiological-like conditions in vitro. However, the behavior of cancer cells strongly relies on the interaction with their extracellular microenvironment. Consequently, current advanced approaches focus on three-dimensional (3D) cell culture to overcome such limitations and to enable a better understanding of fundamental processes including cancer development, progression, apoptosis and invasion. However, transcriptional adaptation to and temporal stability within an in vitro 3D microenvironment still appear to be remarkably understudied. In our study, we compared the cellular behavior and whole transcriptome gene expression of three frequently used non-invasive cancer cell lines (HCT-116, A549 and T47D), embedded within a collagen I (Coll I)-based 3D microenvironment to its counterparts grown as simple monolayers in a time-dependent manner. Thereby, changes in morphology and doubling time became apparent between both cultivation systems, and RNA sequencing-based transcriptome-wide analysis revealed a remarkable increase in transcriptional complexity under 3D conditions. In line with the 3D-dependent phenotype, unidirectional shifts for genes involved in cell cycle regulation (e.g., , ), cell-matrix interaction (e.g., , ) and metabolic signaling (e.g., , ) were identified over time, being either activated or repressed. Interestingly, all three cell lines cultured in Coll I matrices displayed a highly distinct RNA content and composition, along with a significantly increased number of expressed protein-coding genes (increase of 3-6%) as well as long non-coding RNAs (increase of 26-48%), suggesting a more multifaceted transcription profile under 3D conditions. Our work clearly highlights that an in vitro 3D Coll I-based cell culture system has an incisive cell-specific impact on the whole transcriptome on a qualitative and quantitative level. This tremendous transcriptional reprogramming implies essential changes in gene regulatory networks and affects phenotypic cancer cell behavior, which should be considered when focusing on downstream applications. - Source: PubMed
Publication date: 2026/08/01
Wießner-Kroh TheresaHübschmann StefanieMarquardt GudrunSzczesny JenniferFaxel MiriamRubner StefanPapasotiriou Ioannis - : This study aimed to identify common target genes, molecular interaction networks, and signaling pathways shared across the investigated toxic metal(loid)s-lead (Pb), cadmium (Cd), methylmercury compounds (MMC), arsenic (As), nickel (Ni), and hexavalent chromium (Cr(VI))-and associated with lung cancer overall, non-small-cell lung cancer (NSCLC), and small-cell lung cancer (SCLC). : The Comparative Toxicogenomics Database (CTD) was used to identify associated genes, GeneMANIA to characterize molecular interaction networks, and ToppFun for pathway enrichment analysis. : Two common genes ( and ) were identified for SCLC, eight for NSCLC (, , , , , , , and ), and eighteen for lung cancer overall. Gene network analysis revealed predominantly physical interactions in SCLC, pathway interactions in NSCLC, and co-expression in lung cancer overall. Several toxic metal(loid)s exhibited concordant effects on TP53, VEGFA, HMOX1, and IL6 expression. was the only gene common to all investigated toxic metal(loid)s and lung cancer categories. Pathway enrichment identified PI3K-AKT-mTOR signaling in both SCLC and NSCLC, VEGF signaling in NSCLC, and MAPK, interleukin, and macrophage-stimulating protein (MSP) signaling in lung cancer overall. : The identified genes and pathways suggest molecular mechanisms potentially relevant to the association between exposure to the investigated toxic metal(loid)s and lung carcinogenesis, particularly those related to cell survival, inflammation, oxidative stress, and angiogenesis. may represent a common molecular link warranting further investigation. The identified pathways represent candidate biomarkers and mechanistic targets, highlighting the biological heterogeneity of lung cancer and the importance of subtype-specific analyses. - Source: PubMed
Publication date: 2026/07/25
Živančević KatarinaKök Kıvanç - Preoperative differentiation between uterine mesenchymal tumors and benign uterine fibroids remains challenging in postmenopausal women. This study aimed to develop an exploratory risk-stratification model for uterine mesenchymal tumors and to characterize complementary molecular features of uterine leiomyosarcoma using a public transcriptomic dataset. A retrospective case-control study was conducted in postmenopausal patients with uterine masses who underwent surgery between 2011 and 2021. A total of 23 uterine mesenchymal tumor cases and 92 frequency-matched fibroid controls were included. Clinical variables were analyzed using univariable and multivariable logistic regression to develop an exploratory risk-stratification model. Model performance was evaluated using ROC analysis, calibration analysis, decision curve analysis, and confusion matrix assessment. Exploratory transcriptomic analysis was performed using the GEO dataset GSE64763 to characterize molecular differences between uterine leiomyosarcoma and fibroid tissues. Pelvic pressure, abnormal uterine bleeding, and tumor diameter were independently associated with uterine mesenchymal tumors. The model achieved an apparent AUC of 0.859 and an optimism-corrected AUC of 0.846 after 1000 bootstrap resamples. Exploratory transcriptomic analysis revealed distinct expression patterns and upregulation of proliferation-associated genes, including CCNB1, BUB1B, PRC1, TOP2A, and FOXM1, with enrichment of cell cycle-related pathways. An exploratory model based on routinely available preoperative variables demonstrated preliminary discriminatory ability within the development cohort. Independent external validation in representative prospective cohorts is required before any assessment of clinical utility. - Source: PubMed
Publication date: 2026/07/31
Wan XiaojieZhang TaoLi JingyiSong ZhiminRuan FeiLuo Jie - Rheumatoid arthritis (RA) is a chronic autoimmune disease characterized by persistent synovial inflammation, immune dysregulation, and progressive joint destruction. Mast cells (MCs) are important components of the synovial immune microenvironment, but their heterogeneity and regulatory roles in RA remain unclear. - Source: PubMed
Publication date: 2026/07/28
Fang WenlongJin LuyuYu HuiminNie Yingkun