Purpose Pediatric sarcomas provide a unique diagnostic challenge. There is considerable morphologic overlap between entities, increasing the importance of molecular studies in the diagnosis, treatment, and identification of therapeutic targets. We developed and validated a genome-wide DNA methylation based classifier to differentiate between osteosarcoma, Ewing’s sarcoma, and synovial sarcoma. Materials and Methods DNA methylation status of 482,421 CpG sites in 10 Ewing’s sarcoma, 11 synovial sarcoma, and 15 osteosarcoma samples were determined using the Illumina Infinium HumanMethylation450 array. We developed a random forest classifier trained from the 400 most differentially methylated CpG sites within the training set of 36 sarcoma samples. This classifier was validated on data drawn from The Cancer Genome Atlas (TCGA) synovial sarcoma, TARGET Osteosarcoma, and a recently published series of Ewing’s sarcoma. Results Methylation profiling revealed three distinct patterns, each enriched with a single sarcoma subtype. Within the validation cohorts, all samples from TCGA were accurately classified as synovial sarcoma (10/10, sensitivity and specificity 100%), all but one sample from TARGET-OS were classified as osteosarcoma (85/86, sensitivity 98%, specificity 100%) and 14/15 Ewing’s sarcoma samples classified correctly (sensitivity 93%, specificity 100%). The single misclassified osteosarcoma sample demonstrated high EWSR1 and ETV1 expression on RNA-seq although no fusion was found on manual curation of the transcript sequence. Two additional clinical samples, that were difficult to classify by morphology and molecular methods, were classified as osteosarcoma when previously suspected to be a synovial sarcoma and Ewing’s sarcoma on initial diagnosis, respectively. Conclusion Osteosarcoma, synovial sarcoma, and Ewing’s sarcoma have distinct epigenetic profiles. Our validated methylation-based classifier can be used to provide diagnostic assistance when histological and standard techniques are inconclusive.

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Growing evidence showed an association between hepatitis B virus (HBV) infection and gastric cancer (GC). HBV DNA integration is one of the key mechanisms contributing to hepatocellular carcinoma (HCC) development. However, the status of HBV integration in GC has not been studied yet. In this study, HBV DNA was detected in 7/10 GC and 8/10 para-tumor tissues. By high-throughput viral integration detection and long-read sequencing, a total of 176 and 260 HBV integration breakpoints were identified from GC and para-tumor tissues, respectively. In the HBV genome, the breakpoints were more frequently occurred at X gene and C gene. In the host genome, these breakpoints distribution was correlated with CpG islands. Seven protein-coding genes and one non-coding RNA genes were inserted by HBV DNA for more than once in different samples. Combined with the bioinformatics analysis and functional experiments, we highlight SPRY3 and CHD6, as potential driver genes for GC. Besides, we also revealed the spatial relationship of HBV integration and its various structural variations. Taken together, our results first indicated that HBV DNA can integrate in GC. These findings provide insight into the HBV integration and its oncogenic progression in GC.