DNA Methylation Interactive Visualization Database (DNMIVD)

DNA Methylation Interactive Visualization Database (DNMIVD) provides integrated DNA methylation and gene expression data and analyses to characterize epigenetic regulation across human cancers.

Key Features:

• Data integration: Integrates high-throughput microarray DNA methylation data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) together with datasets from Pancan-meQTL and HACER.
• Diagnostic and Prognostic Models: Supplies DNA methylation-based diagnostic and prognostic models for multiple cancer types within the TCGA dataset.
• QTL analysis: Provides expression-methylation QTLs (emQTL) and pathway activity-methylation QTLs (pathway-meQTL) to link genetic variation and pathway activity with methylation changes.
• Functional Epigenetic Modules (FEM): Constructs FEMs using Protein-Protein Interaction (PPI) networks and Co-Occurrence and Mutual Exclusive (COME) networks by integrating DNA methylation and gene expression data.
• Differential analysis: Identifies differentially variable and differentially methylated CpGs and reports differentially methylated genes with related enhancer information.
• Correlation studies: Examines correlations between gene promoter methylation and corresponding gene expression levels.
• Survival analysis: Reports patient survival-associated CpGs and genes across multiple clinical endpoints.

Scientific Applications:

• Prognostic biomarker development: Enables development and evaluation of methylation-based prognostic models for patient stratification.
• Genetic-epigenetic association studies: Supports investigation of the genetic basis of epigenetic regulation via emQTL and pathway-meQTL analyses.
• Network-level functional analysis: Facilitates elucidation of functional networks affected by methylation changes through FEMs built on PPI and COME networks.
• Target discovery: Assists identification of potential therapeutic targets and regulatory elements via differential methylation and enhancer annotation.
• Survival-associated marker identification: Enables discovery of CpGs and genes associated with patient survival endpoints.

Methodology:

Integrates high-throughput microarray methylation data from TCGA and GEO with Pancan-meQTL and HACER; performs emQTL and pathway-meQTL analyses; constructs FEMs using PPI and COME networks by integrating DNA methylation and gene expression; identifies differentially variable and differentially methylated CpGs and genes with enhancer annotation; computes promoter methylation–expression correlations and survival associations for CpGs and genes.