ArticleJournal of personalized medicine2021
Exploring the Metabolic Heterogeneity of Cancers: A Benchmark Study of Context-Specific Models.
Article in Journal of personalized medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
12 citing papers in PubMed.
- Rewiring tumor metabolism: heterogeneity, oncometabolites, and adaptive plasticity.Experimental hematology & oncology · 2026Review
- Tissue-Specific Multi-Omics Integration Demonstrates Molecular Signatures Connecting Obesity to Immune Vulnerability.Metabolites · 2026Article
- Modelling reliable metabolic phenotypes by analysing the context-specific transcriptomics data.NPJ systems biology and applications · 2025Article
- New insights into the roles of lactylation in cancer.Frontiers in pharmacology · 2024Review
- Fuzzy optimization for identifying anti-cancer targets with few side effects in constraint-based models of head and neck cancer.Royal Society open science · 2022Article
- A pipeline for the reconstruction and evaluation of context-specific human metabolic models at a large-scale.PLoS computational biology · 2022Article
- Machine learning-guided evaluation of extraction and simulation methods for cancer patient-specific metabolic models.Computational and structural biotechnology journal · 2022Article
- The role of metabolic ecosystem in cancer progression - metabolic plasticity and mTOR hyperactivity in tumor tissues.Cancer metastasis reviews · 2021Review
- Computer-Aided Design for Identifying Anticancer Targets in Genome-Scale Metabolic Models of Colon Cancer.Biology · 2021Article
- Thermodynamic Genome-Scale Metabolic Modeling of Metallodrug Resistance in Colorectal Cancer.Cancers · 2021Article
- Arginine Signaling and Cancer Metabolism.Cancers · 2021Review
- Advances in flux balance analysis by integrating machine learning and mechanism-based models.Computational and structural biotechnology journal · 2021Review
Corrections and comments
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Authors and funding
5 authors.
Funding
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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Registered trials
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