Identifying key genes in cancer networks using persistent homology
Abstract Identifying driver genes is crucial for understanding oncogenesis and developing targeted cancer therapies. Driver discovery methods using protein or pathway networks rely on traditional network science measures, focusing on nodes, edges, or community metrics. These methods can overlook the...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-87265-4 |
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| author | Rodrigo Henrique Ramos Yago Augusto Bardelotte Cynthia de Oliveira Lage Ferreira Adenilso Simao |
| author_facet | Rodrigo Henrique Ramos Yago Augusto Bardelotte Cynthia de Oliveira Lage Ferreira Adenilso Simao |
| author_sort | Rodrigo Henrique Ramos |
| collection | DOAJ |
| description | Abstract Identifying driver genes is crucial for understanding oncogenesis and developing targeted cancer therapies. Driver discovery methods using protein or pathway networks rely on traditional network science measures, focusing on nodes, edges, or community metrics. These methods can overlook the high-dimensional interactions that cancer genes have within cancer networks. This study presents a novel method using Persistent Homology to analyze the role of driver genes in higher-order structures within Cancer Consensus Networks derived from main cellular pathways. We integrate mutation data from six cancer types and three biological functions: DNA Repair, Chromatin Organization, and Programmed Cell Death. We systematically evaluated the impact of gene removal on topological voids ( $$\beta _2$$ structures) within the Cancer Consensus Networks. Our results reveal that only known driver genes and cancer-associated genes influence these structures, while passenger genes do not. Although centrality measures alone proved insufficient to fully characterize impact genes, combining higher-order topological analysis with traditional network metrics can improve the precision of distinguishing between drivers and passengers. This work shows that cancer genes play an important role in higher-order structures, going beyond pairwise measures, and provides an approach to distinguish drivers and cancer-associated genes from passenger genes. |
| format | Article |
| id | doaj-art-f739c9532ae945f7801a1f30e6ead2cc |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
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| series | Scientific Reports |
| spelling | doaj-art-f739c9532ae945f7801a1f30e6ead2cc2025-01-26T12:34:03ZengNature PortfolioScientific Reports2045-23222025-01-0115111310.1038/s41598-025-87265-4Identifying key genes in cancer networks using persistent homologyRodrigo Henrique Ramos0Yago Augusto Bardelotte1Cynthia de Oliveira Lage Ferreira2Adenilso Simao3University of São Paulo, ICMCUniversity of São Paulo, ICMCUniversity of São Paulo, ICMCUniversity of São Paulo, ICMCAbstract Identifying driver genes is crucial for understanding oncogenesis and developing targeted cancer therapies. Driver discovery methods using protein or pathway networks rely on traditional network science measures, focusing on nodes, edges, or community metrics. These methods can overlook the high-dimensional interactions that cancer genes have within cancer networks. This study presents a novel method using Persistent Homology to analyze the role of driver genes in higher-order structures within Cancer Consensus Networks derived from main cellular pathways. We integrate mutation data from six cancer types and three biological functions: DNA Repair, Chromatin Organization, and Programmed Cell Death. We systematically evaluated the impact of gene removal on topological voids ( $$\beta _2$$ structures) within the Cancer Consensus Networks. Our results reveal that only known driver genes and cancer-associated genes influence these structures, while passenger genes do not. Although centrality measures alone proved insufficient to fully characterize impact genes, combining higher-order topological analysis with traditional network metrics can improve the precision of distinguishing between drivers and passengers. This work shows that cancer genes play an important role in higher-order structures, going beyond pairwise measures, and provides an approach to distinguish drivers and cancer-associated genes from passenger genes.https://doi.org/10.1038/s41598-025-87265-4Topological data analysisPersistent homologyCancer genomicsDriver genesPathways networksProtein networks |
| spellingShingle | Rodrigo Henrique Ramos Yago Augusto Bardelotte Cynthia de Oliveira Lage Ferreira Adenilso Simao Identifying key genes in cancer networks using persistent homology Scientific Reports Topological data analysis Persistent homology Cancer genomics Driver genes Pathways networks Protein networks |
| title | Identifying key genes in cancer networks using persistent homology |
| title_full | Identifying key genes in cancer networks using persistent homology |
| title_fullStr | Identifying key genes in cancer networks using persistent homology |
| title_full_unstemmed | Identifying key genes in cancer networks using persistent homology |
| title_short | Identifying key genes in cancer networks using persistent homology |
| title_sort | identifying key genes in cancer networks using persistent homology |
| topic | Topological data analysis Persistent homology Cancer genomics Driver genes Pathways networks Protein networks |
| url | https://doi.org/10.1038/s41598-025-87265-4 |
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