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Identifying key genes in cancer networks using persistent homology
Rodrigo Henrique Ramos1,2, Yago Augusto Bardelotte3, Cynthia de Oliveira Lage Ferreira3
1University of São Paulo, ICMC, São Carlos, 13566-590, Brazil. ramos@ifsp.edu.br.
Scientific Reports
|January 21, 2025
Summary
Identifying driver genes is key for cancer therapy. This study uses persistent homology to find that cancer genes, not passenger genes, impact higher-order network structures, improving driver gene discovery.
Area of Science:
- Computational Biology
- Network Science
- Cancer Genomics
Background:
- Identifying driver genes is critical for understanding cancer and developing targeted therapies.
- Traditional network science methods may miss complex, high-dimensional gene interactions in cancer networks.
Purpose of the Study:
- To present a novel method using Persistent Homology to analyze driver gene roles in higher-order structures within Cancer Consensus Networks.
- To distinguish driver genes from passenger genes by examining their influence on topological voids.
Main Methods:
- Applied Persistent Homology to Cancer Consensus Networks derived from key cellular pathways (DNA Repair, Chromatin Organization, Programmed Cell Death).
- Integrated mutation data from six cancer types.
- Evaluated the impact of gene removal on topological voids ([Formula: see text] structures).
Main Results:
- Only known driver genes and cancer-associated genes significantly influenced topological voids.
- Passenger genes did not impact these higher-order structures.
- Combining topological analysis with centrality measures enhanced the precision of driver gene identification.
Conclusions:
- Cancer genes play a significant role in higher-order network structures, beyond pairwise interactions.
- This higher-order topological analysis offers a novel approach to differentiate driver and cancer-associated genes from passenger genes.
- The method improves the precision of driver gene discovery for targeted cancer therapies.
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