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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Bibliographic Details
Published in:Journal of personalized medicine Vol. 11; no. 6; p. 496
Main Authors: Jalili, Mahdi, Scharm, Martin, Wolkenhauer, Olaf, Damaghi, Mehdi, Salehzadeh-Yazdi, Ali
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.06.2021
MDPI
Subjects:
Algorithms
Biomarkers
Cancer
Consortia
Gene expression
Genomes
Genomics
Metabolism
Metabolites
Metastasis
Phenotypes
Precision medicine
Proteins
Proteomics
ISSN:2075-4426, 2075-4426
Online Access:Get full text
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Summary: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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These authors contributed equally to this work as first authors.
ISSN:2075-4426
2075-4426
DOI:10.3390/jpm11060496