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===Cancer Diagnosis Performance based on Methylation Haplotype Loading of circulating free DNA methylation === In this section, RRBS and Capseq dataset were taken as the discovery dataset and the BSPP dataset were used validate the performance of the biomarkers. For RRBS dataset, 68 cancer and 8 normal samples were included. 56046 regions were obtained by methylation haplotype loading algorithm (MHL) and 17263 regions were 100% un-methylated in 8 normal samples. 248 regions among 56046 were methylated in at least 50% cancer samples while 100% un-methylated in normal samples. The average length of the regions were 103bp (IQR=95bp, SD=126) and were located in the promoter region of 182 genes (2000bp up-stream of TSS). With the help of text mining, 21 genes of them were validated to be methylation relevant cancer related genes (Table). In order to evaluate the distinguish ability of DNA methylation from cancer to the normal samples, Random forest model were applied. In the procedure of RF model, the optimal number of variables tried at each split is131 (mtry) and number of trees (mtrees) makes no difference to the prediction accuracy from 250 to 7000. RF prediction model showed 1592 regions could provide positive ability to distinguish cancer samples from normal samples with sensitivity of 97.06%, specificity of 100% and accuracy of 97.37%. Furthermore, we found the top 206 most importance regions could take account of 80.0% contribution to the accurate prediction. And the random forest model based on top 206 regions with mtry of 14 and mtrees of 500 showed better performance with 100% sensitivity, 100% specificity and 100% accuracy. The result showed these 206 regions were high frequent hypermethylated in cancers (Mean=41.4%, SD=14.5%, IQR=22.4%). (Supplementary Table **) For CapSeq dataset, 31 cancer 24 normal samples were included. 86206 regions were obtained by methylation haplotype loading algorithm (MHL), however, only 2205 regions were 100% un-methylated in 24 normal samples. Parameters of Random forest model were tuned and the optimal number of variables tried at each split is 92 (mtry) and number of trees (mtree) makes no difference to the prediction accuracy from 250 to 7000. Random forest algorithm showed 516 regions could provide positive ability to separate cancer samples from normal samples with sensitivity of 87.1%, specificity of 75.0% and accuracy of 81.26%. We found that the top 21 most importance regions could take account of 80.0% contribution for the classification. Therefore, the second round prediction process of random forest model based on top 21 regions with mtry of 7 and mtrees of 750 showed perfect classification with 96.77% sensitivity, 100% specificity and 98.18% accuracy. The average hyper-methylation frequency in cancer samples for above 516 and 21 regions 90.43% (SD=0.08, IQR=0.096) and 96.7% (SD=0.041, IQR=0.056), respectively. (Supplementary Table ** and supplementary Table **) For BSPP dataset, 16 cancer plasma and 16 normal plasma samples were included. 36281 regions were obtained by methylation haplotype loading algorithm (MHL) and 4194 regions were 100% un-methylated in 16 normal samples. All the regions hypermethylated in cancer and 100% un-methylated in normal samples were collected from RRBS, SeqCap and BSPP dataset and 30 regions were found un-methylation in all the normal samples of the 3 dataset. These 30 regions could explain 93.75% cancers incidence and the specificity is 100%. The methylation frequency of these 30 regions ranged from 6.25% to 25% in free-cell circulating DNA while it is hypermethylated in 1.47%-44% RRBS cancer samples and 6.45%-77% SeqCap cancer samples. These 30 regions were located in the promoter or gene body of 24 genes, including FGF19, MEF2C-AS1, MEF2C, HABP2, AGO3, TAF15, ZWILCH, YTHDF3-AS1, ZNF213, C9orf114, C14orf28, ADAM11, CDKN2AIP, TMEM87B, CDKL3, MSH5, MSH5-SAPCD1, UBC, PCM1, PCK2, NR2F6, JADE2, KANK1 and MCM3. At least 12 genes have been demonstrated to be associated with human cancers with the annotation from NCBI (HABP2, FGF19, MEF2C, AGO3, CDKL3, MCM3, TAF15, PCK2, NR2F6, MSH5, ZWILCH and PCM1). For example, the FGF19-FGFR4 signaling axis has been implicated in the pathogenesis of several cancers in mice and potentially in humans (7) while HABP2 has been found to be hypermethylated in hepatocellular carcinomas cancer (8). AGO3 was found significant decreased in human liver cancer (9). KANK1 re-expression induced by 5-Aza-2'-deoxycytidine could suppresses nasopharyngeal carcinoma cell proliferation and promotes apoptosis (10). Whole-exome sequencing identifies mutated PCK2 associated with carcinoma cell proliferation in a hepatocellular carcinoma patient(11)
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