H. Pylori Infection Alters Repair of DNA Double-Strand Breaks Via SNHG17.
The Journal of Clinical Investigation(2020)SCI 1区
Chinese Acad Med Sci & Peking Union Med Coll | Harbin Med Univ | Inst Basic Med Sci
Abstract
Chronic infections can lead to carcinogenesis through inflammation-related mechanisms. Chronic infection of the human gastric mucosa with Helicobacter pylori is a well-known risk factor for gastric cancer. However, the mechanisms underlying H. pylori-induced gastric carcinogenesis are incompletely defined. We aimed to screen and clarify the functions of long noncoding RNAs (lncRNAs) that are differentially expressed in H. pylori-related gastric cancer. We found that lncRNA SNHG17 was upregulated by H. pylori infection and markedly increased the levels of double-strand breaks (DSBs). SNHG17 overexpression correlated with poor overall survival in patients with gastric cancer. The recruitment of NONO by overabundant nuclear SNHG17, along with the role of cytoplasmic SNHG17 as a decoy for miR-3909, which regulates Rad51 expression, shifted the DSB repair balance from homologous recombination toward nonhomologous end joining. Notably, during chronic H. pylori infection, SNHG17 knockdown inhibited chromosomal aberrations. Our findings suggest that spatially independent deregulation of the SNHG17/NONO and SNHG17/miR-3909/RING1/Rad51 pathways upon H. pylori infection promotes tumorigenesis in gastric cancer by altering the DNA repair system, which is critical for the maintenance of genomic stability. Upregulation of SNHG17 by H. pylori infection might be an undefined link between cancer and inflammation.
MoreTranslated text
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
Related Papers
1999
被引用234 | 浏览
2008
被引用727 | 浏览
2009
被引用77 | 浏览
2010
被引用300 | 浏览
2009
被引用68 | 浏览
2011
被引用7 | 浏览
2014
被引用76 | 浏览
2015
被引用82 | 浏览
2018
被引用29 | 浏览
2018
被引用37 | 浏览
2018
被引用94480 | 浏览
2018
被引用14 | 浏览
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