WeChat Mini Program
Old Version Features

Functional Polymorphisms in Circadian Positive Feedback Loop Genes Predict Postsurgical Prognosis of Gastric Cancer

Cancer Medicine(2019)

Zhengzhou Univ

Cited 15|Views16
Abstract
BackgroundCircadian positive feedback loop (CPFL) genes (CLOCK, BAML1, and NPAS2) have been implicated in cancer initiation and progression. The purpose of this study was to explore the effects of single-nucleotide polymorphisms (SNPs) in CPFL genes on prognosis of gastric cancer (GC) patients. MethodsNine functional SNPs from the three CPFL genes were genotyped in a cohort of 704 GC patients undergoing resection. Multivariate Cox regression model and Kaplan-Meier curve were used for prognosis analysis. ResultsAmong the nine SNPs, rs11133399 in CLOCK, rs1044432 and rs2279284 in BAML1 were significantly associated with GC overall survival and recurrence-free survival. The unfavorable genotypes of these SNPs showed a cumulative effect on GC prognosis. Multivariate assessment model indicated that these SNPs, in conjunction with clinical variables, enhanced the power to predict GC prognosis. In addition, survival tree analysis revealed the genotype of rs11133399 as a primary risk factor contributing to the prognosis of GC patients. Functional assays showed that the G allele in rs11133399 significantly enhanced luciferase reporter activity than A allele. Immunohistochemical analysis further demonstrated that the genotype of rs11133399 was significantly associated with the expression level of CLOCK in GC tissues, suggesting that this SNP might affect the prognosis of GC through its influence on the expression of CLOCK gene. ConclusionsOur data indicate that SNPs in CPFL genes might contribute to the clinical outcome of GC through their impact on gene expression. Further studies are needed to elucidate its underlying molecular mechanisms.
More
Translated text
Key words
circadian gene,gastric cancer,prognosis,single-nucleotide polymorphism
求助PDF
上传PDF
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