Objective: To explore the difference in H4 clustered histone 6(H4C6) methylation level and circulating cell-free DNA (cfDNA) concentration between 94 normal group and 122 tumor groups (65 patients with lung cancer，22 patients with gastric cancer，23 patients with colorectal cancer，and 12 patients with liver cancer) ，and the age of total 216 subjects were between 18 and 85 years old．To construct a cancer risk prediction model based on H4C6 methylation level and cfDNA concentration and evaluate the predictive performance of the model． Methods: cfDNA was extracted from blood samples using magnetic beads．Qubit 4. 0 fluorescence quantitative meter was used to detect the concentration of cfDNA. Real-time quantitative PCR( RT-qPCR) technology was used to detect the methylation level of H4C6 in cfDNA．Logistic regression algorithm was used to construct a cancer risk prediction model of H4C6 methylation level combined with cfDNA concentration．The accuracy of the model was assessed using receiver operating characteristic (ROC) curve and calibration curve．The clinical benefit of the model was as- sessed using decision curve analysis (DCA) . Results: The model was constructed by combining H4C6 methylation level and cfDNA concentration to distinguish lung cancer，liver cancer，colorectal cancer，gastric cancer，pancancer from healthy control group had the area under curve (AUC) of 0. 769，0. 988，0. 934，0. 922，0. 830，respectively．The mean absolute error of the calibration curve was less than 0. 05 ; the net benefit of the DCA curve was greater than 0． Conclusion The cancer risk prediction model based on H4C6 methylation level and cfDNA concentration has good predictive performance，which helps to provide reasonable and effective suggestions for preclinical decision-making，and ultimately may provide patients with targeted and personalized cancer detection and diagnosis program.

Objective : To construct a cell-free DNA ( cfDNA) methylation model for early screening in male pa- tients with gastric cancer by using novel cfDNA methylation detection technology． Methods : Methylation informa- tion 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 sig- nificant 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.