iEDGE

iEDGE integrates epigenomic data (methylation, somatic copy number alterations, mutations, microRNA) with paired gene expression profiles to identify candidate cancer driver genes by modeling cis and trans regulatory effects and applying statistical mediation tests, implemented as an R package.

Key Features:

• Integrative Analysis: Integrates epigenomic data (methylation, somatic copy number alterations, mutations, microRNA) with paired gene expression profiles.
• Identification of Driver Genes: Identifies candidate cancer driver genes by modeling cis (within genomic boundaries) and trans (outside genomic boundaries) effects of alterations on gene expression.
• Statistical Mediation Tests: Applies statistical mediation tests to determine which cis genes predict trans gene expression changes.
• Pathway Enrichment Analysis: Annotates identified cis and trans effects through pathway enrichment analysis to associate alterations with regulatory pathways.
• Broad Applicability: Applied to analysis of somatic copy number alterations (SCNAs) and gene expression across 19 cancer types from The Cancer Genome Atlas (TCGA).
• Implementation: Provided as an R package for computational analysis of multi-omics datasets.

Scientific Applications:

• Cancer Driver Gene Identification: Enables identification of known and putative cancer driver genes, including oncogenes and tumor suppressors.
• Prognostic and Therapeutic Insights: Identifies functionally relevant, amplification-driven dependencies that can inform prognostic assessment and highlight potential therapeutic targets.

Methodology:

Three explicit modules: identification of cis and trans gene expression signatures associated with specific epigenetic alterations; application of statistical mediation tests to link cis genes to trans expression changes; and annotation of results via pathway enrichment analysis.