Functional impact of multi-omic interactions in breast cancer subtypes

Area of Science:

• Computational biology
• Cancer research
• Systems biology

Background:

• Multi-omic approaches offer a comprehensive view of cancer molecular mechanisms.
• Existing methods struggle to fully elucidate the mechanistic explanations behind complex biological functions in cancer.

Purpose of the Study:

• To develop a theoretical and computational framework for semi-automatic generation of multi-omic regulatory network models.
• To identify functions and associated molecular features enriched within different breast cancer subtypes.
• To explore the overlap and intersection of omics-associated functions across subtypes.

Main Methods:

• Developed a computational framework for analyzing multi-omic data.
• Constructed network models representing regulatory constraints on biological functions.
• Performed functional enrichment analysis for four breast cancer molecular subtypes.
• Identified subtype-specific and intersecting molecular features associated with biological functions.

Main Results:

• Identified significantly enriched functions and associated molecular features for each breast cancer subtype.
• Observed distinct functional profiles across subtypes, such as invasion-related processes in basal subtype and DNA modification in normal tissue.
• Found limited overlap in omics-associated functions between subtypes but identified notable feature intersections within subtype-specific functions.
• Highlighted novel, potentially regulatory features with strong biological rationale.

Conclusions:

• Multi-omic regulatory networks provide reliable models for understanding omics interconnections.
• The proposed framework enables the systematic generation of mechanistic hypotheses in cancer research.
• This approach advances the mechanistic explanation of biological functions influenced by multi-omic data in cancer subtypes.