We found a match
Your institution may have rights to this item. Sign in to continue.
- Title
Exploring the Metabolic Heterogeneity of Cancers: A Benchmark Study of Context-Specific Models.
- Authors
Jalili, Mahdi; Scharm, Martin; Wolkenhauer, Olaf; Damaghi, Mehdi; Salehzadeh-Yazdi, Ali
- Abstract
Metabolic heterogeneity is a hallmark of cancer and can distinguish a normal phenotype from a cancer phenotype. In the systems biology domain, context-specific models facilitate extracting physiologically relevant information from high-quality data. Here, to utilize the heterogeneity of metabolic patterns to discover biomarkers of all cancers, we benchmarked thousands of context-specific models using well-established algorithms for the integration of omics data into the generic human metabolic model Recon3D. By analyzing the active reactions capable of carrying flux and their magnitude through flux balance analysis, we proved that the metabolic pattern of each cancer is unique and could act as a cancer metabolic fingerprint. Subsequently, we searched for proper feature selection methods to cluster the flux states characterizing each cancer. We employed PCA-based dimensionality reduction and a random forest learning algorithm to reveal reactions containing the most relevant information in order to effectively identify the most influential fluxes. Conclusively, we discovered different pathways that are probably the main sources for metabolic heterogeneity in cancers. We designed the GEMbench website to interactively present the data, methods, and analysis results.
- Subjects
METABOLOMIC fingerprinting; HETEROGENEITY; METABOLIC models; SYSTEMS biology; WEB design
- Publication
Journal of Personalized Medicine, 2021, Vol 11, Issue 6, p496
- ISSN
2075-4426
- Publication type
Article
- DOI
10.3390/jpm11060496