An integrative multi-omics network-based approach identifies key regulators for breast cancer

Area of Science:

• Genomics
• Systems Biology
• Bioinformatics

Background:

• Genome-wide association studies (GWASs) identify disease risk variants but struggle to elucidate biological functions.
• Understanding the molecular mechanisms of single nucleotide polymorphisms (SNPs) in complex diseases remains a significant challenge.

Purpose of the Study:

• To develop an integrative multi-omics network-based approach (IMNA) for identifying key genes in disease regulatory networks.
• To apply IMNA to breast cancer to prioritize regulatory genes and develop diagnostic/prognostic signatures.
• To explore the potential of IMNA for other complex diseases like ovarian cancer.

Main Methods:

• Integrated multiple biological data types: GWAS signals, gene expression, chromatin interactions, and protein interactions.
• Constructed a network-based framework (IMNA) to analyze these integrated data.
• Developed an abnormal gene expression score (AGES) based on top-ranked genes.

Main Results:

• Prioritized key genes within breast cancer regulatory networks using the IMNA framework.
• The AGES signature showed associations with genetic variants, tumor characteristics, and patient survival.
• Identified RNASEH2A as a novel candidate gene for breast cancer.

Conclusions:

• IMNA offers a genetic-driven framework for uncovering tissue-specific interactions across biological scales.
• The approach effectively reveals potential key regulatory genes, aiding in understanding complex diseases.
• IMNA has potential applications in identifying therapeutic targets for breast and ovarian cancers.