NIBNA: a network-based node importance approach for identifying breast cancer drivers
| Autor: | Thuc Duy Le, Vu Viet Hoang Pham, Mandar S. Chaudhary |
|---|---|
| Přispěvatelé: | Chaudhary, Mandar S, Pham, Vu V H, Le, Thuc D |
| Rok vydání: | 2021 |
| Předmět: |
Statistics and Probability
Computer science Gene regulatory network Machine learning computer.software_genre Biochemistry coding cancer drivers 03 medical and health sciences 0302 clinical medicine Breast cancer medicine Molecular Biology 030304 developmental biology 0303 health sciences business.industry Node (networking) Cancer medicine.disease breast cancer drivers Computer Science Applications Computational Mathematics Computational Theory and Mathematics 030220 oncology & carcinogenesis Artificial intelligence Centrality business human activities computer Network analysis |
| Zdroj: | Bioinformatics. 37:2521-2528 |
| ISSN: | 1367-4811 1367-4803 |
| DOI: | 10.1093/bioinformatics/btab145 |
| Popis: | Motivation Identifying meaningful cancer driver genes in a cohort of tumors is a challenging task in cancer genomics. Although existing studies have identified known cancer drivers, most of them focus on detecting coding drivers with mutations. It is acknowledged that non-coding drivers can regulate driver mutations to promote cancer growth. In this work, we propose a novel node importance-based network analysis (NIBNA) framework to detect coding and non-coding cancer drivers. We hypothesize that cancer drivers are crucial to the formation of community structures in cancer network, and removing them from the network greatly perturbs the network structure thereby critically affecting the functioning of the network. NIBNA detects cancer drivers using a three-step process: first, a condition-specific network is built by incorporating gene expression data and gene networks; second, the community structures in the network are estimated; and third, a centrality-based metric is applied to compute node importance. Results We apply NIBNA to the BRCA dataset, and it outperforms existing state-of-art methods in detecting coding cancer drivers. NIBNA also predicts 265 miRNA drivers, and majority of these drivers have been validated in literature. Further we apply NIBNA to detect cancer subtype-specific drivers, and several predicted drivers have been validated to be associated with cancer subtypes. Lastly, we evaluate NIBNA’s performance in detecting epithelial–mesenchymal transition drivers, and we confirmed 8 coding and 13 miRNA drivers in the list of known genes. Availability and implementation The source code can be accessed at https://github.com/mandarsc/NIBNA. Supplementary information Supplementary data are available at Bioinformatics online. |
| Databáze: | OpenAIRE |
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