Found programs:
Authors:Ji Jie; Qi Jian; Hong Bo; Wang Shujie; Sun Ruifang; Cao Xueling; Sun Xiaojun; Nie Jinfu
Keywords:gastric cancer;liquid biopsy;cfDNA methylation;MeDIP-seq;machine learning
DOI:10.19405/j.cnki.issn1000-1492.2022.12.024
〔Abstract〕 Objective To construct a cell-free DNA(cfDNA) methylation model for early screening in male patients with gastric cancer by using novel cfDNA methylation detection technology. Methods Methylation information of the whole genome of gastric cancer patients were detected by cell-free methylated DNA immunoprecipitation and highthroughput sequencing(cfMeDIP-seq) technology and locate gastrogenic cfDNA. Then bioinformation methods were used to extract specific methylation labels which could distinguish GC patients and establish diagnosis model by random forest algorithm. Related validation clinical researches were also conducted. Results 63 most significant DMR were selected to construct the cfDNA methylation model based on GC samples and normal control samples, the goal sensitivity was above 85 percent while the goal specificity was above 95%. The sensitivity and specificity of the validation set were 98.7% and 99.0% while the area under curve(AUC) was 0.999. Conclusion The cfDNA methylation model constructed in this study has good performance in predicting GC.