Showing 21 - 26 results of 26 for search '"CDKN2A"', query time: 0.04s Refine Results
  1. 21

    Protein arginine methyltransferase 7 linked to schizophrenia through regulation of neural progenitor cell proliferation and differentiation by Ting Shen, Jing Yu, Bin Xie, Cuiping Huang, Jingjie Cui, Kefu Liu, Chunyu Liu, Chao Chen

    Published 2025-02-01
    “…Mechanistically, PRMT7 regulates the expression of genes related to the cell cycle and neuronal functions, such as CDKN2A and SYP, via symmetrical di-methylation at arginine 3 of histone 4 (H4R3me2s) modification in their promoters. …”
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  2. 22

    Human placental extract improves liver cirrhosis in mice with regulation of macrophages and senescent cells by Natsuki Ishikawa, Yusuke Watanabe, Yuichirou Maeda, Tomoaki Yoshida, Naruhiro Kimura, Hiroyuki Abe, Akira Sakamaki, Hiroteru Kamimura, Takeshi Yokoo, Kenya Kamimura, Atsunori Tsuchiya, Shuji Terai

    Published 2025-03-01
    “…Furthermore, there was a decrease in the number of senescent cells in the liver and the mRNA levels of secrete senescence-associated secretory phenotype factors and Cdkn2a (p16). In vitro, HPE induced macrophage polarization to the anti-inflammatory M2 phenotype. …”
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  3. 23

    High p16INK4A expression in glioblastoma is associated with senescence phenotype and better prognosis by Soon Sang Park, Tae Hoon Roh, Yoshiaki Tanaka, Young Hwa Kim, So Hyun Park, Tae-Gyu Kim, So Yeong Eom, Tae Jun Park, In-Hyun Park, Se-Hyuk Kim, Jang-Hee Kim

    Published 2025-02-01
    “…Previous studies have identified a few prognostic markers for GBM, including the methylation status of O6-methylguanine-DNA methyltransferase (MGMT) promoter, TERT promoter mutation, EGFR amplification, and CDKN2A/2B deletion. However, the classification of GBM remains incomplete, necessitating a comprehensive analysis. …”
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  4. 24

    Glioma Image-Level and Slide-Level Gene Predictor (GLISP) for Molecular Diagnosis and Predicting Genetic Events of Adult Diffuse Glioma by Minh-Khang Le, Masataka Kawai, Kenta Masui, Takashi Komori, Takakazu Kawamata, Yoshihiro Muragaki, Tomohiro Inoue, Ippei Tahara, Kazunari Kasai, Tetsuo Kondo

    Published 2024-12-01
    “…Using the concept of multiple-instance learning, we developed an AI framework named GLioma Image-level and Slide-level gene Predictor (GLISP) to predict nine genetic abnormalities in hematoxylin and eosin sections: <i>IDH1/2</i>, <i>ATRX</i>, <i>TP53</i> mutations, <i>TERT</i> promoter mutations, <i>CDKN2A/B</i> homozygous deletion (CHD), <i>EGFR</i> amplification (<i>EGFR</i>amp), 7 gain/10 loss (7+/10−), 1p/19q co-deletion, and <i>MGMT</i> promoter methylation. …”
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  5. 25

    A Case Report and Genetic Characterization of a Massive Acinic Cell Carcinoma of the Parotid with Delayed Distant Metastases by Anthony C. Nichols, Michelle Chan-Seng-Yue, John Yoo, Sumit K. Agrawal, Maud H. W. Starmans, Daryl Waggott, Nicholas J. Harding, Samuel A. Dowthwaite, David A. Palma, Kevin Fung, Bret Wehrli, S. Danielle MacNeil, Philippe Lambin, Eric Winquist, James Koropatnick, Joe S. Mymryk, Paul C. Boutros, John W. Barrett

    Published 2013-01-01
    “…The primary tumor and blood underwent exome sequencing which revealed deletions in CDKN2A as well as PPP1R13B, which induces p53. A damaging nonsynonymous mutation was noted in EP300, a histone acetylase which plays a role in cellular proliferation. …”
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  6. 26

    An Optimized NGS Workflow Defines Genetically Based Prognostic Categories for Patients with Uveal Melanoma by Michele Massimino, Elena Tirrò, Stefania Stella, Cristina Tomarchio, Sebastiano Di Bella, Silvia Rita Vitale, Chiara Conti, Marialuisa Puglisi, Rosa Maria Di Crescenzo, Silvia Varricchio, Francesco Merolla, Giuseppe Broggi, Federica Martorana, Alice Turdo, Miriam Gaggianesi, Livia Manzella, Andrea Russo, Giorgio Stassi, Rosario Caltabiano, Stefania Staibano, Paolo Vigneri

    Published 2025-01-01
    “…Methods: Following the findings published by “The Cancer Genome Atlas–UM” (TCGA-UM) study, we developed an NGS-based gene panel (called the UMpanel) that classifies mutation sets in four categories: initiating alterations (<i>CYSLTR2</i>, <i>GNA11</i>, <i>GNAQ</i> and <i>PLCB4</i>), prognostic alterations (<i>BAP1</i>, <i>EIF1AX</i>, <i>SF3B1</i> and <i>SRSF2</i>), emergent biomarkers (<i>CDKN2A</i>, <i>CENPE</i>, <i>FOXO1</i>, <i>HIF1A</i>, <i>RPL5</i> and <i>TP53</i>) and chromosomal abnormalities (imbalances in chromosomes 1, 3 and 8). …”
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