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Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization
Published on: October 3, 2025
Centrality-based pathway enrichment: a systematic approach for finding significant pathways dominated by key genes
Zuguang Gu1, Jialin Liu, Kunming Cao
1The State Key Laboratory of Pharmaceutical Biotechnology and Jiangsu Engineering Research Center for MicroRNA Biology and Biotechnology, School of Life Science, Nanjing University, Nanjing, 210093, China.
BMC Systems Biology
|June 8, 2012
Summary
This study introduces a novel pathway enrichment method that incorporates network topology and gene centrality. The approach enhances the identification of significant biological pathways from gene expression data.
Area of Science:
- Bioinformatics
- Systems Biology
- Computational Biology
Background:
- Biological pathways are crucial for understanding biological mechanisms.
- Identifying key pathways aids researchers in focusing on relevant gene sets.
- Current pathway enrichment tools often overlook network topology, limiting their effectiveness.
Purpose of the Study:
- To develop a systematic and extensible pathway enrichment method that accounts for network topology.
- To improve the identification of significant biological pathways by considering gene centrality and pathway structure.
- To offer a more comprehensive analysis of biological pathways compared to existing methods.
Main Methods:
- Proposed a pathway enrichment method weighting nodes by network centrality.
- Investigated the impact of pathway structure and centrality measures on pathway significance.
- Defined nodes, rather than genes, as the basic unit of pathways, allowing for complex gene-node relationships.
Main Results:
- Demonstrated how pathway structure, centrality measurement, and key genes influence pathway significance.
- Showcased the method's efficacy in identifying novel pathways using simulation and real-world data.
- Highlighted improvements over traditional enrichment methods by considering gene diversity and importance.
Conclusions:
- The developed method enhances systematic analysis of biological pathways.
- It facilitates the extraction of more meaningful insights from gene expression data.
- The algorithm is available as an R package (CePa) and a web-based tool.
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