KIF23 antibody - middle region (ARP33910_P050)
- Known as:
- KIF23 (anti-) - middle region (ARP33910_P050)
- Catalog number:
- arp33910_p050
- Product Quantity:
- USD
- Category:
- -
- Supplier:
- Aviva Systems Biology
- Gene target:
- KIF23 antibody - middle region (ARP33910_P050)
Related genes to: KIF23 antibody - middle region (ARP33910_P050)
- Gene:
- KIF23 NIH gene
- Name:
- kinesin family member 23
- Previous symbol:
- KNSL5
- Synonyms:
- MKLP1, MKLP-1
- Chromosome:
- 15q23
- Locus Type:
- gene with protein product
- Date approved:
- 1999-09-07
- Date modifiied:
- 2019-04-23
Related products to: KIF23 antibody - middle region (ARP33910_P050)
Related articles to: KIF23 antibody - middle region (ARP33910_P050)
- Many cell division events are regulated by protein phosphorylation, which can result from cross-regulatory mechanisms among mitotic kinases and phosphatases that have yet to be fully elucidated. Here, we report the characterization of a novel mechanism by which CDK1 and Aurora B (AURKB) kinases regulate the distribution and interactions of Citron kinase (CIT-K). We show that CDK1 phosphorylates serine 440 and AURKB serine 699, both residues located adjacent to or within CIT-K coiled coil domain. S440 and S699 temporal phosphorylation profiles reflect the activity of the kinases responsible for their phosphorylation. Functional analyses using phospho mutants indicate that S699 phosphorylation is important for CIT-K localization and successful cytokinesis, while perturbing S440 phosphorylation leads to abnormal midbody formation and accumulation of post-mitotic midbody remnants (MBRs). Furthermore, we found that phosphorylation at either residue reduces the ability of CIT-K to interact with its midbody partners AURKB, KIF14 and KIF23/MKLP1. Together, our findings indicate that phosphorylation of CIT-K by CDK1 and AURKB regulates midbody formation and MBR stability by controlling the association of CIT-K with its partners. They expand our understanding of the mechanisms that regulate abscission and can lead to further insights into the role of MBRs in post-mitotic events. - Source: PubMed
Publication date: 2026/04/20
Capalbo LuisaHalcrow Ella F JBassi Zuni ID'Avino Pier Paolo - Kinesin family member 23 (KIF23) is recognised as an important tumour promoter involved in the pathogenesis of various cancers. However, its role and underlying molecular mechanisms in regulating cervical cancer (CC) growth and primary chemoresistance remain to be fully elucidated. - Source: PubMed
Zhu YingWang QianZhang YilinLiu YahuiFu HainiYang ZikeDeng XiaojieGuo Suiqun - Cervical cancer is one of the major threats to women's health worldwide. Nuclear Cap Binding Protein 2(NCBP2) plays a significant role in various cancers, and mitophagy, as a cellular homeostasis regulation mechanism, is closely related to tumorigenesis and development. However, the specific mechanisms by which NCBP2 regulates mitophagy in cervical cancer remain unclear. Bioinformatics was used to screen cervical cancer-related genes and mechanisms. The effects of NCBP2 on the viability, migration, and mitochondrial function of cervical cancer cells were investigated using CCK-8, EdU, and Transwell assays. Comprehensive experimental methods, including RT-qPCR and Western blot, were employed to elucidate the potential mechanisms of NCBP2. NCBP2 was found to be significantly upregulated in cervical cancer and promoted the in vitro proliferation, migration, and invasion of cervical cancer cells. Mechanistically, NCBP2 regulated the alternative splicing of KIF23 to facilitate cervical cancer progression. NCBP2 also regulated mitophagy in cervical cancer cells via the KIF23-PGAM5 axis. Moreover, FBXW8 inhibited the overactivation of mitophagy and exerted tumor-suppressive effects by ubiquitinating and degrading NCBP2. This study reveals that NCBP2 regulates alternative splicing and mitophagy to influence cervical cancer progression, providing new potential therapeutic targets and strategies for cervical cancer treatment. - Source: PubMed
Publication date: 2026/03/24
Su YingZhang JuxinWu HenghuiZhang YuZhou Wenlei - Oral Squamous Cell Carcinoma (OSCC) is one of the most occurred cancer types with yearly 377,713 cases and 177,000 deaths. Traditional risk factors of OSCC include smoking, alcohol consumption, excessive sun exposure, family history of cancer, and human papillomavirus (HPV). Last few years, the prevalence of OSCC is growing big in numbers particularly among younger people for their lifestyle. From the Gene Expression Omnibus, 2 gene-expression profiles (GSE23558 and GSE146483) were identified based on some conditions. The GEO2R tool was used to analyze those datasets to extract all the genes. Statistical cut-off criteria were applied to find out DEGs from both datasets, and after that common DEGs were identified by comparing both datasets. Common DEGs were used to perform bioinformatics analysis such as gene ontology and pathway analysis, protein-protein interaction (PPI) network construction, and generating Transcription factor - miRNA network. 265 common DEGs were identified from the datasets including 69 up-regulated and 196 down-regulated DEGs. Using the STRING database and a strong combine score > 0.70, a PPI network is generated including 92 nodes and 226 interactions. Using 3 different hub DEGs seeking algorithm, we identified 9 top hub DEGs. The hub genes are Kinesin Family Member 23 (KIF23), Aurora Kinase A (AURKA), Centromere Protein F (CENPF), Cell Division Cycle 20 (CDC20), Discs Large Associated Protein 5 (DLGAP5), Centrosomal Protein 55 (CEP55), Anillin Actin Binding Protein (ANLN), Non-SMC Condensin I Complex Subunit G (NCAPG), and Kinesin Family Member 14 (KIF14). 3 significant clusters also identified from the PPI network. Previous study shows KIF23 takes part in raising Cell Proliferation in Hepatocellular carcinoma cells and AURKA shows notable overexpression in cancer tissues, which indicates that KIF23 and AURKA showed promising character to become possible biomarkers for OSCC. Further analysis needed to justify the statement. - Source: PubMed
Publication date: 2026/03/10
Alam Mohammad KhursheedIslam Md RakibulHaque TahsinulGanji Kiran KumarBabkair Hamzah AliRashid Mohammed EnamurAhmed KawsarBui Francis M - Urinary proteins are promising non-invasive biomarkers, but their low abundance and wide dynamic range make detection challenging. This study presents UriPred, a computational tool that integrates machine learning (ML), BLAST, and Motif-EmeRging and Classes-Identification (MERCI) to predict urinary proteins and facilitate the identification of liver cancer (LC) biomarkers. A dataset of 10588 urinary and non-urinary proteins was curated, from which two feature types were generated: 10074 compositional and 20 evolutionary features. Seven feature selection methods were applied to compositional features, and 11 ML algorithms were trained on different feature sets. Evolutionary features achieved the highest training performance (AUC 0.79, accuracy 71.99 %), whereas amino acid composition (AAC) with 20 features achieved identical validation AUC (0.74) and comparable accuracy while being computationally less expensive and consistently selected. The ML-AAC model was therefore chosen as the final model. This optimal model was integrated with BLAST and MERCI to create UriPred, which reduced false positives from 34.59 % (ML) to 3.12 % (hybrid) on the validation dataset and from 5.8 % (ML) to zero (hybrid) on an external dataset. Using UriPred, 53 LC differentially expressed protein-coding genes were predicted as urinary proteins. Protein-protein interaction analysis, AUROC evaluation (AUC > 0.80), survival analysis, and cross-verification of urine detectability with the Human Protein Atlas and Human Urine PeptideAtlas databases identified five proteins (KIF23, COL15A1, CTHRC1, MMP9, and SPP1) as potential LC biomarkers. UriPred efficiently predicts urinary proteins using AAC features and enables biomarker discovery for LC. The tool is publicly available at https://github.com/Dahrii-Paul/UriPred. - Source: PubMed
Publication date: 2026/02/08
Paul DahriiSinnarasan Vigneshwar Suriya PrakashDas RajeshSheikh Md Mujibur RahmanManickannan SanthoshVenkatesan Amouda