Aberrant DNA methylation plays an important role in cancer progression. However, no resource has been available that comprehensively provides DNA methylation-based diagnostic and prognostic models, expression–methylation quantitative trait loci (emQTL), pathway activity-methylation quantitative trait loci (pathway-meQTL), differentially variable and differentially methylated CpGs, and survival analysis, as well as functional epigenetic modules for different cancers. These provide valuable information for researchers to explore DNA methylation profiles from different aspects in cancer. To this end, we constructed a user-friendly database named DNA Methylation Interactive Visualization Database (DNMIVD), which comprehensively provides the following important resources: (i) diagnostic and prognostic models based on DNA methylation for multiple cancer types of The Cancer Genome Atlas (TCGA); (ii) meQTL, emQTL and pathway-meQTL for diverse cancers; (iii) Functional Epigenetic Modules (FEM) constructed from Protein-Protein Interactions (PPI) and Co-Occurrence and Mutual Exclusive (COME) network by integrating DNA methylation and gene expression data of TCGA cancers; (iv) differentially variable and differentially methylated CpGs and differentially methylated genes as well as related enhancer information; (v) correlations between methylation of gene promoter and corresponding gene expression and (vi) patient survival-associated CpGs and genes with different endpoints. DNMIVD is freely available at http://www.unimd.org/dnmivd/. We believe that DNMIVD can facilitate research of diverse cancers.
DNA methylation status is closely associated with diverse diseases, and is generally more stable than gene expression, thus abnormal DNA methylation could be important biomarkers for tumor diagnosis, treatment and prognosis. However, the signatures regarding DNA methylation changes for pan-cancer diagnosis and prognosis are less explored. Here we systematically analyzed the genome-wide DNA methylation patterns in diverse TCGA cancers with machine learning. We identified seven CpG sites that could effectively discriminate tumor samples from adjacent normal tissue samples for 12 main cancers of TCGA (1216 samples, AUC > 0.99). Those seven potential diagnostic biomarkers were further validated in the other 9 different TCGA cancers and 4 independent datasets (AUC > 0.92). Three out of the seven CpG sites were correlated with cell division, DNA replication and cell cycle. We also identified 12 CpG sites that can effectively distinguish 26 different cancers (7605 samples), and the result was repeatable in independent datasets as well as two disparate tumors with metastases (micro-average AUC > 0.89). Furthermore, a series of potential signatures that could significantly predict the prognosis of tumor patients for 7 different cancer were identified via survival analysis (p-value < 1e-4). Collectively, DNA methylation patterns vary greatly between tumor and adjacent normal tissues, as well as among different types of cancers. Our identified signatures may aid the decision of clinical diagnosis and prognosis for pan-cancer and the potential cancer-specific biomarkers could be used to predict the primary site of metastatic breast and prostate cancers.
SummaryDue to differences across species, the mechanisms of cell fate decisions determined in mice cannot be readily extrapolated to humans. In this study, we developed a feeder- and xeno-free culture protocol that efficiently induced human pluripotent stem cells (iPSCs) into PLZF+/GPR125+/CD90+ spermatogonium-like cells (SLCs). These SLCs were enriched with key genes in germ cell development such as MVH, DAZL, GFRα1, NANOS3, and DMRT1. In addition, a small fraction of SLCs went through meiosis in vitro to develop into haploid cells. We further demonstrated that this chemically defined induction protocol faithfully recapitulated the features of compromised germ cell development of PSCs with NANOS3 deficiency or iPSC lines established from patients with non-obstructive azoospermia. Taken together, we established a powerful experimental platform to investigate human germ cell development and pathology related to male infertility.
The arrival of the Infinium DNA methylation BeadChips for mice and other nonhuman mammalian species has outpaced the development of the informatics that supports their use for epigenetics study in model organisms. Here, we present informatics infrastructure and methods to allow easy DNA methylation analysis on multiple species, including domesticated animals and inbred laboratory mice (in SeSAMe version 1.16.0+). First, we developed a data-driven analysis pipeline covering species inference, genome-specific data preprocessing and regression modeling. We targeted genomes of 310 species and 37 inbred mouse strains and showed that genome-specific preprocessing prevents artifacts and yields more accurate measurements than generic pipelines. Second, we uncovered the dynamics of the epigenome evolution in different genomic territories and tissue types through comparative analysis. We identified a catalog of inbred mouse strain-specific methylation differences, some of which are linked to the strains’ immune, metabolic and neurological phenotypes. By streamlining DNA methylation array analysis for undesigned genomes, our methods extend epigenome research to broad species contexts.
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