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DiCE: differential centrality-ensemble analysis based on gene expression profiles and protein-protein interaction
Elnaz Pashaei1, Sheng Liu1, Kailing Li2
1Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN 46202, United States.
Abstract:
Uncovering key genes that drive diseases and cancers is crucial for advancing understanding and developing targeted therapies. Traditional differential expression analysis often relies on arbitrary cutoffs, missing critical genes with subtle expression changes. Some methods incorporate protein-protein interactions (PPIs) but depend on prior disease knowledge. To address these challenges, we developed DiCE (Differential Centrality-Ensemble analysis), a novel approach that combines differential expression with network centrality analysis, independent of prior disease annotations. DiCE identifies candidate genes, refines them with an information gain filter, and reconstructs a condition-specific weighted PPI network. Using centrality measures, DiCE ranks genes based on expression shifts and network influence. Validated on prostate cancer datasets, DiCE identified genes overrepresented in key pathways and cancer fitness genes, significantly correlating with disease-free survival (DFS), despite DFS not being used in selection. DiCE offers a comprehensive, unbiased approach to identifying disease-associated genes, advancing biomarker discovery and therapeutic development.
Insights
We developed DiCE, a new method to find key genes driving diseases and cancers by analyzing gene expression and network interactions. This approach improves disease gene discovery and aids in developing targeted therapies.
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.
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