Exploring the Metabolic Heterogeneity of Cancers: A Benchmark Study of Context-Specific Models
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 pa...
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| Published in: | Journal of personalized medicine Vol. 11; no. 6; p. 496 |
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| Main Authors: | , , , , |
| Format: | Journal Article |
| Language: | English |
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| ISSN: | 2075-4426, 2075-4426 |
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| 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. |
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| AbstractList | 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. 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.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. |
| Author | Jalili, Mahdi Scharm, Martin Damaghi, Mehdi Salehzadeh-Yazdi, Ali Wolkenhauer, Olaf |
| AuthorAffiliation | 4 Department of Cancer Physiology, Moffitt Cancer Center and Research Institute, Tampa, FL 33612, USA; mehdi.damaghi@moffitt.org 3 Wallenberg Research Centre, Stellenbosch Institute for Advanced Study (STIAS), Stellenbosch University, 10 Marais Street, Stellenbosch 7600, South Africa 5 Department of Oncologic Sciences, Morsani College of Medicine, University of South Florida, Tampa, FL 33612, USA 2 Department of Systems Biology and Bioinformatics, University of Rostock, 18051 Rostock, Germany; martin.scharm@uni-rostock.de (M.S.); olaf.wolkenhauer@uni-rostock.de (O.W.) 1 Hematology, Oncology and SCT Research Center, Tehran University of Medical Sciences, Tehran 14114, Iran |
| AuthorAffiliation_xml | – name: 4 Department of Cancer Physiology, Moffitt Cancer Center and Research Institute, Tampa, FL 33612, USA; mehdi.damaghi@moffitt.org – name: 2 Department of Systems Biology and Bioinformatics, University of Rostock, 18051 Rostock, Germany; martin.scharm@uni-rostock.de (M.S.); olaf.wolkenhauer@uni-rostock.de (O.W.) – name: 5 Department of Oncologic Sciences, Morsani College of Medicine, University of South Florida, Tampa, FL 33612, USA – name: 1 Hematology, Oncology and SCT Research Center, Tehran University of Medical Sciences, Tehran 14114, Iran – name: 3 Wallenberg Research Centre, Stellenbosch Institute for Advanced Study (STIAS), Stellenbosch University, 10 Marais Street, Stellenbosch 7600, South Africa |
| Author_xml | – sequence: 1 givenname: Mahdi orcidid: 0000-0002-5491-089X surname: Jalili fullname: Jalili, Mahdi – sequence: 2 givenname: Martin surname: Scharm fullname: Scharm, Martin – sequence: 3 givenname: Olaf surname: Wolkenhauer fullname: Wolkenhauer, Olaf – sequence: 4 givenname: Mehdi orcidid: 0000-0002-7744-6161 surname: Damaghi fullname: Damaghi, Mehdi – sequence: 5 givenname: Ali orcidid: 0000-0002-1678-0051 surname: Salehzadeh-Yazdi fullname: Salehzadeh-Yazdi, Ali |
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| SubjectTerms | Algorithms Biomarkers Cancer Consortia Gene expression Genomes Genomics Metabolism Metabolites Metastasis Phenotypes Precision medicine Proteins Proteomics |
| Title | Exploring the Metabolic Heterogeneity of Cancers: A Benchmark Study of Context-Specific Models |
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