Establish six-gene prognostic model for glioblastoma based on multi-omics data of TCGA database.

Glioblastoma (GBM) is the most common primary intracranial tumor with extremely high malignancy and poor prognosis. In order to identify the GBM prognostic biomarkers and establish a prognostic model, we analyzed the expression profile data of GBM in The Cancer Genome Atlas (TCGA) database as the experimental group. First, we identified the differentially expressed genes of different survival periods among the GBM patients.

EWASdb: epigenome-wide association study database.

DNA methylation, the most intensively studied epigenetic modification, plays an important role in understanding the molecular basis of diseases. Furthermore, epigenome-wide association study (EWAS) provides a systematic approach to identify epigenetic variants underlying common diseases/phenotypes. However, there is no comprehensive database to archive the results of EWASs.

Comparison of the general co-expression landscapes between human and mouse.

The murine model serves as an important experimental system in biomedical science because of its high degree of similarities at the sequence level with human. Recent studies have compared the transcriptional landscapes between human and mouse, but the general co-expression landscapes have not been characterized. Here, we calculated the general co-expression coefficients and constructed the general co-expression maps for human and mouse.

EWAS: epigenome-wide association studies software 1.0 - identifying the association between combinations of methylation levels and diseases.

Similar to the SNP (single nucleotide polymorphism) data, there is non-random association of the DNA methylation level (we call it methylation disequilibrium, MD) between neighboring methylation loci. For the case-control study of complex diseases, it is important to identify the association between methylation levels combination types (we call it methylecomtype) and diseases/phenotypes. We extended the classical framework of SNP haplotype-based association study in population genetics to DNA methylation level data, and developed a software EWAS to identify the disease-related methylecomtypes.

The framework for population epigenetic study.

At present, understanding of DNA methylation at the population level is still limited. Here, we first extended the classical framework of population genetics, such as single nucleotide polymorphism allele frequency, linkage disequilibrium (LD), LD block and haplotype, to epigenetics. Then, as an example, we compared the DNA methylation disequilibrium (MD) maps between HapMap CEU (Caucasian residents of European ancestry from Utah) population and YRI (Yoruba people from Ibadan) population (lymphoblastoid cell lines).