KIF2C antibody - N-terminal region (ARP33915_P050)
- Known as:
- KIF2C (anti-) - N-terminal region (ARP33915_P050)
- Catalog number:
- arp33915_p050
- Product Quantity:
- USD
- Category:
- -
- Supplier:
- Aviva Systems Biology
- Gene target:
- KIF2C antibody - N-terminal region (ARP33915_P050)
Related genes to: KIF2C antibody - N-terminal region (ARP33915_P050)
- Gene:
- KIF2C NIH gene
- Name:
- kinesin family member 2C
- Previous symbol:
- KNSL6
- Synonyms:
- MCAK, CT139
- Chromosome:
- 1p34.1
- Locus Type:
- gene with protein product
- Date approved:
- 1999-09-07
- Date modifiied:
- 2014-11-19
Related products to: KIF2C antibody - N-terminal region (ARP33915_P050)
Related articles to: KIF2C antibody - N-terminal region (ARP33915_P050)
- MCAK/Kif2C is a microtubule-depolymerizing kinesin implicated in the correction of chromosome attachment errors. When eliminated from kinetochores, cells exhibit delayed congression and a modest increase in chromosome missegregation. Curiously, MCAK/Kif2C overexpression (OE) promotes these same defects. Both depletion and excess levels of centromeric MCAK/Kif2C increase acetylated tubulin levels in the spindle, suggesting an increase in k-fiber stability. We conclude that this is the likely mechanism for the increase in chromosome segregation errors observed in both of these antagonistic conditions. Reduced MCAK/Kif2C increased the tubulin ratio on the two faces of the kinetochore, suggesting a greater likelihood of erroneous lateral MT interactions. In contrast, excess MCAK/Kif2C reduced the tubulin ratio at the kinetochore, stabilizing end-on MT interactions that increase the IKD and ultimately culminate in excessive stabilization of K-fiber microtubules. Both of these conditions promote chromosome segregation errors. - Source: PubMed
Publication date: 2026/05/26
Wagenbach MikeVicente Juan JesusWordeman Linda - Hepatocellular carcinoma (HCC) remains a leading cause of cancer-related mortality worldwide, underscoring the need for robust molecular biomarkers to improve prognosis and therapeutic strategies. Although advances have been made in imaging, surgery, as well as systemic therapies, the prognosis of HCC remains poor due to late detection, high recurrence, and molecular heterogeneity, underscoring the significance of identifying robust prognostic biomarkers and therapeutic targets. mRNA-sequencing data from the TCGA-HCC cohort were examined to recognize differentially expressed genes (DEGs) between tumor and normal tissues. Weighted gene co-expression network analysis (WGCNA) was applied to uncover key gene modules and hub genes. Protein-protein interaction network (PPIN) construction and modular analysis further refined candidate genes. Univariate overall survival (OS) analysis identified five genes (, , , , and ) whose elevated expression significantly correlated with poor patient survival. Pathway enrichment analysis exhibited a strong association with mitotic checkpoint and kinetochore signaling pathways. Mutational profiling demonstrated frequent genomic alterations, particularly in , whereas immune infiltration analysis demonstrated significant correlations between expression and multiple immune cell populations. In this study, we employed an integrative transcriptomic and systems biology approach to recognize prognostically relevant hub genes in HCC. Collectively, this finding highlights the critical genes that may serve as prognostic biomarkers and potential therapeutic targets in HCC. - Source: PubMed
Publication date: 2026/05/07
Rahmani Arshad HusainBeg AnamSarwar TariqueKhan Amjad Ali - This study aimed to identify tumor-associated autoantibodies (TAAbs) with diagnostic potential for the early detection of oral squamous cell carcinoma (OSCC). Bioinformatics analyses were used to screen candidate genes. The candidate tumor-associated antigens (TAAs) were selected from the proteins encoded by the candidate genes. Serum levels of corresponding TAAbs were measured by enzyme-linked immunosorbent assay (ELISA) in 496 participants. Eight machine learning algorithms were employed to develop diagnostic models, and Shapley Additive exPlanations (SHAP) were applied to interpret the optimal model. Twelve candidate genes were identified, among which eight encoded proteins were confirmed to be overexpressed in OSCC. Based on mRNA expression evidence, all 12 encoded proteins were included as candidate TAAs. Of the corresponding autoantibodies, five TAAbs (anti-BLM, anti-BUB1, anti-KIF18A, anti-KIF2C, and anti-TPX2) demonstrated potential diagnostic performance in both the training and validation sets. Among the eight models constructed, the Naive Bayes (NB) model performed best, achieving an area under the receiver operating characteristic curve (AUC) of 0.75 (95% CI 0.70–0.80) in the training set and 0.66 (95% CI 0.57–0.75) in the validation set. SHAP analysis indicated anti-KIF2C contributed most to predictive performance. Five TAAbs were identified with diagnostic potential for OSCC. The NB model constructed based on these TAAbs demonstrated potential diagnostic performance. - Source: PubMed
Publication date: 2026/04/15
Xu LijuanXie WeihongZou YuanlinYang QianZheng ZhongZhang XiaoyueHou YiheLiu YuqiLi MengYe HuaWang Peng - The high morbidity and mortality rates of lung cancer associated with smoking underscore the need for a deeper understanding of prognosis-related kinesin family-microRNA-long non-coding RNA-competitive endogenous RNA (KIFs-miRNA-lncRNA-ceRNA) networks. - Source: PubMed
Publication date: 2026/02/27
Kousik Sakshi PriyaSingh JagritiVats PrernaBaweja BhavikaSaini ChainseeNema Rajeev - There is a strong correlation between lactylation, programmed cell death, and the progression of cancer. This study aims to identify prognostic genes associated with lactylation and programmed cell death in pancreatic ductal adenocarcinoma (PDAC), providing new insights for risk stratification and therapeutic strategies. - Source: PubMed
Publication date: 2026/03/12
Zhang BoxingSun GenHuang LuyingWei WenjunYuan YuzhangWang ZehuaPeng XingzhouSong LiangGörgülü KıvançAi Jiaoyu