A network-based pathway-extending approach using DNA methylation and gene expression data to identify altered...

Area of Science:

• Bioinformatics
• Computational Biology
• Cancer Genomics

Background:

• Pathway analysis is crucial for understanding cancer molecular mechanisms.
• High-throughput multi-omics data and biological networks aid in identifying cancer-related pathways.
• Integrating multidimensional data, pathway databases, and interaction networks is a promising strategy.

Purpose of the Study:

• To present a novel network-based approach for the integrative analysis of DNA methylation and gene expression data.
• To extend original pathways by incorporating multi-omics information.
• To identify cancer-related altered pathways using this extended pathway information.

Main Methods:

• Developed a novel network-based approach for integrative analysis of DNA methylation and gene expression data.
• Extended original pathways using gene interaction networks.
• Applied classical gene set analysis (ORA and FCS) to extended pathway gene lists.

Main Results:

• Pathway extension provides a basis for discovering new pathway components.
• The method facilitates understanding crosstalk between pathways in large biological networks.
• Identified altered pathways correlated with specific cancers across TCGA datasets (BRCA, LUAD, COAD).

Conclusions:

• The integration of DNA methylation and gene expression data via gene interaction networks is effective for identifying altered cancer pathways.
• This network-based approach enhances pathway analysis for cancer research.
• The extended pathways offer deeper insights into cancer mechanisms and inter-pathway communication.