Framework for the Integration of Genomics, Epigenomics and Transcriptomics in Complex Diseases.

Area of Science:

• Multi-omics data integration in cancer research.
• Genomic, epigenomic, and transcriptomic analysis.

Background:

• Single -omics data provides limited insight into complex diseases like urothelial bladder cancer (UBC).
• Understanding molecular dysregulation in neoplastic diseases requires integrated data approaches.

Purpose of the Study:

• To propose a framework for integrating genomics, epigenomics, and transcriptomics data in UBC.
• To identify previously hidden genetic mechanisms underlying UBC through multi-omics integration.

Main Methods:

• Developed a multi-staged framework for pair-wise and three-way -omics data integration.
• Analyzed SNP genotypes, CpG methylation, and gene expression levels from 70 UBC tumor tissues.

Main Results:

• Identified two hypothesis-based scenarios for gene regulation: DNA methylation affecting gene expression, and genetic variants co-regulating both.
• Discovered three-way trans-association 'hotspots' at the molecular level in UBC.

Conclusions:

• The integrative framework successfully identified whole-genome level relationships in UBC.
• The study highlights the importance of integrating diverse -omics data for novel biological insights in complex diseases.