WeChat Mini Program
Old Version Features

    • BRAFV600E突变对甲状腺乳头状癌HMGB1表达的影响

    管晓蕾,王萍,于清,李鹏,迟静薇,王斐

    Chinese Journal of General Practice（2017）

    Cited 0|Views9
    Abstract
    目的 近年来甲状腺乳头状癌(PTC)的发病率逐渐升高,发生复发和转移的患者也在增多.本文意在探索在PTC中BRAFV600E突变对高迁移率族蛋白(HMGB1)表达的影响,寻求BRAF基因影响PTC发展及预后的机制,指导临床进行精准治疗.方法 收集2015年9-12月青岛大学附属医院收治的44例PTC患者的术前血清及术后新鲜病理组织,组织提取DNA进行基因测序.根据有无BRAFV600E突变将患者分为BRAF突变阳性组和BRAF突变阴性组,运用免疫组化和Western blot检测组织中HMGB1蛋白的分布和含量;荧光定量PCR检测组织中HMGB1 mRNA的水平;应用ELISA法检测血清中HMGB1蛋白的水平.Western blot数据应用Image J软件计算灰度值,采用相对定量法计算荧光定量PCR数据,用2-ΔCt进行分析,应用ELISA Calc回归/拟合计算程序计算血清中的HMGB1蛋白浓度.所得数据均采用SPSS 20.0进行统计分析.Western blot、荧光定量PCR和ELISA数据分别用x2检验、Mann-Whitney-Wilcoxon test和独立样本￡检验的方法进行统计学处理.淋巴结转移和腺体外浸润与BRAFV600E突变发生的关系采用x2检验进行统计分析.结果 在PTC组织中,HMGB1蛋白主要定位于胞浆,BRAFV600E突变阳性组HMGB1的转录水平及蛋白水平均低于BRAFV600E突变阴性组(Z =2.117,P＜0.01;x2=19.989,P＜0.05),而这种变化在外周血中并未呈现(t=1.135,P＞0.05).BRAFv600E突变增加淋巴结转移和腺体外浸润的风险(x2 =6.117,P ＜0.05;x2=5.587,P ＜0.05).发生淋巴结转移PTC中的HMGB1 mRNA和蛋白的表达量均低于无淋巴结转移PTC(Z=-2.216,P＜0.05;t=-2.217,P＜0.05),发生腺体外浸润的PTC也呈现此种趋势(Z=-2.267,P＜0.05;t=-3.885,P＜0.01).结论 在PTC中,BRAFV600E突变可能通过下调HMGB1的表达加速肿瘤的恶性发展.
    More
    Translated text
    求助PDF
    上传PDF
    View via Publisher
    Bibtex
    AI Read Science
    AI Summary
    AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
    Example
    Background
    Key content
    Introduction
    Methods
    Results
    Related work
    Fund
    Key content
    • Pretraining has recently greatly promoted the development of natural language processing (NLP)
    • We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
    • We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
    • The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
    • Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
    Upload PDF to Generate Summary
    Must-Reading Tree
    Example
    Generate MRT to find the research sequence of this paper
    Data Disclaimer
    The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
    Chat Paper
    Summary is being generated by the instructions you defined