DiCE: differential centrality-ensemble analysis based on gene expression profiles and protein-protein interaction...

Area of Science:

• Genomics
• Bioinformatics
• Systems Biology

Background:

• Identifying disease-driving genes is vital for targeted therapies.
• Traditional methods miss subtle gene expression changes and require prior disease knowledge.
• Protein-protein interaction (PPI) network analysis is limited by existing disease annotations.

Purpose of the Study:

• To introduce DiCE (Differential Centrality-Ensemble analysis), a novel method for unbiased identification of disease-associated genes.
• To integrate differential gene expression with network centrality analysis.
• To advance biomarker discovery and therapeutic development.

Main Methods:

• DiCE combines differential expression analysis with network centrality.
• It refines candidate genes using an information gain filter.
• A condition-specific weighted PPI network is reconstructed and analyzed using centrality measures.

Main Results:

• DiCE identified genes significantly associated with disease-free survival in prostate cancer datasets.
• Identified genes were enriched in key cancer pathways and fitness genes.
• The method demonstrated effectiveness independent of prior disease annotations.

Conclusions:

• DiCE provides a comprehensive and unbiased approach to disease gene identification.
• The method enhances the discovery of potential biomarkers and therapeutic targets.
• DiCE overcomes limitations of traditional methods by integrating expression and network data.