shRNA干扰RACK1表达增强口腔鳞癌细胞放射敏感性的研究
Chinese Journal of Radiation Oncology（2021）
Abstract
目的:探讨下调RACK1基因表达对口腔鳞癌细胞生长及放射敏感性的影响。方法:构建RACK1基因的shRNA载体，通过脂质体转染技术将其转入口腔鳞癌HSC-3细胞，G418筛选得到稳定转染细胞。荧光定量RT-PCR和Western blot分别检测细胞中RACK1mRNA和RACK1蛋白表达水平；CCK8实验检测细胞生长能力；流式细胞仪检测细胞凋亡；细胞体外侵袭实验检测细胞侵袭能力；克隆形成实验检测下调RACK1表达联合X射线照射对细胞增殖能力的影响。构建裸鼠移植瘤模型，观察下调RACK1基因表达联合X射线照射对口腔鳞癌的生长抑制作用。结果:RT-PCR和Western blot结果显示转染后HSC-3细胞RACK1mRNA相对表达量明显降低( P<0.05)，RACK1蛋白表达水平明显降低( P<0.05)。CCK8实验结果显示下调RACK1表达可抑制HSC-3细胞生长( P<0.05)，联合X射线照射使细胞凋亡率明显升高( P<0.05)，外侵袭穿透细胞数显著减少( P<0.05)。克隆形成实验结果显示shRACK1组的存活分数低于对照组，放射增敏比为1.37(D 0值比)。裸鼠移植瘤实验显示shRACK1组照射后瘤体生长缓慢，瘤体体积减小幅度低于对照组( P<0.05)，瘤体质量低于对照组( P<0.05)。 结论:下调RACK1表达可以增强口腔鳞癌细胞的放射敏感性，为提高口腔鳞癌放射敏感性研究提供新思路。
MoreTranslated text
Key words
RACK1 gene,Radiosensitivity,HSC-3 cell,Nude mouse with xenograft tumor
求助PDF
上传PDF
View via Publisher
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