DNA甲基化
DNA提取
结直肠癌
基因组DNA
DNA
甲基化
生物
色谱法
医学
化学
癌症
聚合酶链反应
内科学
生物化学
基因
基因表达
作者
Shengnan Jin,Ye Qian,Yanping Hong,Wenqing Dai,Chengliang Zhang,Weihao Liu,Ying Guo,Dewen Zhu,Zhengzheng Zhang,Shiliang Chen,Yourong Wang,Dandan Li,Wen Ma,Zhengquan Yang,Jinlei Li,Zhihai Zheng,Ju Luan,Xiaoli Wu,Feizhao Jiang,Chang Xu
标识
DOI:10.1515/cclm-2020-0300
摘要
Abstract Objectives Colorectal cancer (CRC) screening using stool samples is now in routine use where tumor DNA methylation analysis for leading markers such as NDRG4 and SDC2 is an integral part of the test. However, processing stool samples for reproducible and efficient extraction of human genomic DNA remains a bottleneck for further research into better biomarkers and assays. Methods We systematically evaluated several factors involved in the processing of stool samples and extraction of DNA. These factors include: stool processing (solid and homogenized samples), preparation of DNA from supernatant and pellets, and DNA extraction with column and magnetic beads-based methods. Furthermore, SDC2 and NDRG4 methylation levels were used to evaluate the clinical performance of the optimal protocol. Results The yield of total and human genomic DNA (hgDNA) was not reproducible when solid stool scraping is used, possibly due to sampling variations. More reproducible results were obtained from homogenized stool samples. Magnetic beads-based DNA extraction using the supernatant from the homogenized stool was chosen for further analysis due to better reproducibility, higher hgDNA yield, lower non-hgDNA background, and the potential for automation. With this protocol, a combination of SDC2 and NDRG4 methylation signals with a linear regression model achieved a sensitivity and specificity of 81.82 and 93.75%, respectively. Conclusions Through the systematic evaluation of different stool processing and DNA extraction methods, we established a reproducible protocol for analyzing tumor DNA methylation markers in stool samples for colorectal cancer screening.
科研通智能强力驱动
Strongly Powered by AbleSci AI