DNA modifications such as 5-methylcytosine (5mC) and 5-hydroxymethylcytosine (5hmC) are epigenetic marks known to affect global gene expression in mammals. Given their prevalence in the human genome, close correlation with gene expression and high chemical stability, these DNA epigenetic marks could serve as ideal biomarkers for cancer diagnosis. Taking advantage of a highly sensitive and selective chemical labeling technology, we report here the genome-wide profiling of 5hmC in circulating cell-free DNA (cfDNA) and in genomic DNA (gDNA) of paired tumor and adjacent tissues collected from a cohort of 260 patients recently diagnosed with colorectal, gastric, pancreatic, liver or thyroid cancer and normal tissues from 90 healthy individuals. 5hmC was mainly distributed in transcriptionally active regions coincident with open chromatin and permissive histone modifications. Robust cancer-associated 5hmC signatures were identified in cfDNA that were characteristic for specific cancer types. 5hmC-based biomarkers of circulating cfDNA were highly predictive of colorectal and gastric cancers and were superior to conventional biomarkers and comparable to 5hmC biomarkers from tissue biopsies. Thus, this new strategy could lead to the development of effective, minimally invasive methods for diagnosis and prognosis of cancer from the analyses of blood samples.

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BackgroundEfficient computational recognition and segmentation of target organ from medical images are foundational in diagnosis and treatment, especially about pancreas cancer. In practice, the diversity in appearance of pancreas and organs in abdomen, makes detailed texture information of objects important in segmentation algorithm. According to our observations, however, the structures of previous networks, such as the Richer Feature Convolutional Network (RCF), are too coarse to segment the object (pancreas) accurately, especially the edge.MethodIn this paper, we extend the RCF, proposed to the field of edge detection, for the challenging pancreas segmentation, and put forward a novel pancreas segmentation network. By employing multi-layer up-sampling structure replacing the simple up-sampling operation in all stages, the proposed network fully considers the multi-scale detailed contexture information of object (pancreas) to perform per-pixel segmentation. Additionally, using the CT scans, we supply and train our network, thus get an effective pipeline.ResultWorking with our pipeline with multi-layer up-sampling model, we achieve better performance than RCF in the task of single object (pancreas) segmentation. Besides, combining with multi scale input, we achieve the 76.36% DSC (Dice Similarity Coefficient) value in testing data.ConclusionThe results of our experiments show that our advanced model works better than previous networks in our dataset. On the other words, it has better ability in catching detailed contexture information. Therefore, our new single object segmentation model has practical meaning in computational automatic diagnosis.