Inferencing Bulk Tumor and Single-Cell Multi-Omics Regulatory Networks for Discovery of Biomarkers and Therapeutic...

Area of Science:

• Computational biology
• Cancer research
• Bioinformatics

Background:

• Current cancer treatment lacks sufficient accurate biomarkers and effective therapeutic targets.
• Multi-omics data and electronic medical records (EMRs) offer potential for molecular insights.
• Integrating these data sources is key to advancing cancer precision medicine.

Purpose of the Study:

• To review multi-omics data harmonization and network inference methods.
• To present Prediction Logic Boolean Implication Networks (PLBINs) for constructing genome-scale multi-omics networks.
• To establish a framework for discovering biomarkers and therapeutic targets by integrating multi-omics data with EMRs.

Main Methods:

• Harmonization of multi-omics data.
• Network inference using Prediction Logic Boolean Implication Networks (PLBINs).
• Integration of multi-omics profiles with large-scale EMRs (e.g., SEER-Medicare).
• Application of graph theory network centrality metrics for candidate prioritization.
• External validation for biomarker applicability.

Main Results:

• PLBINs demonstrate superior computational efficiency, scalability, and accuracy for multi-omics network construction.
• The integrated framework successfully identifies potential biomarkers applicable to large patient populations.
• Prioritization of candidate biomarkers and therapeutic targets is achieved using network centrality metrics.

Conclusions:

• The proposed framework offers an innovative approach to accelerate biomarker and therapeutic target discovery in cancer.
• Integration of multi-omics data and EMRs is a powerful strategy for advancing cancer research.
• This methodology has the potential to significantly improve cancer patient survival outcomes.