Next Article in Journal
Prognostic Factors in Primary Biliary Cholangitis: A Retrospective Study of Joint Slovak and Croatian Cohort of 249 Patients
Next Article in Special Issue
Identification of Therapeutic Targets for the Selective Killing of HBV-Positive Hepatocytes
Previous Article in Journal
Cannabinoids and Inflammations of the Gut-Lung-Skin Barrier
Previous Article in Special Issue
Differences among COVID-19, Bronchopneumonia and Atypical Pneumonia in Chest High Resolution Computed Tomography Assessed by Artificial Intelligence Technology
Article

Exploring the Metabolic Heterogeneity of Cancers: A Benchmark Study of Context-Specific Models

1
Hematology, Oncology and SCT Research Center, Tehran University of Medical Sciences, Tehran 14114, Iran
2
Department of Systems Biology and Bioinformatics, University of Rostock, 18051 Rostock, Germany
3
Wallenberg Research Centre, Stellenbosch Institute for Advanced Study (STIAS), Stellenbosch University, 10 Marais Street, Stellenbosch 7600, South Africa
4
Department of Cancer Physiology, Moffitt Cancer Center and Research Institute, Tampa, FL 33612, USA
5
Department of Oncologic Sciences, Morsani College of Medicine, University of South Florida, Tampa, FL 33612, USA
*
Authors to whom correspondence should be addressed.
These authors contributed equally to this work as first authors.
Academic Editor: José A. G. Agundez
J. Pers. Med. 2021, 11(6), 496; https://doi.org/10.3390/jpm11060496
Received: 14 March 2021 / Revised: 25 May 2021 / Accepted: 28 May 2021 / Published: 1 June 2021
(This article belongs to the Special Issue Systems Medicine and Bioinformatics)
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. View Full-Text
Keywords: genome-scale metabolic model; data integration; Warburg effect; metabolic pattern; FBA-based feature; cancer metabolism genome-scale metabolic model; data integration; Warburg effect; metabolic pattern; FBA-based feature; cancer metabolism
Show Figures

Figure 1

MDPI and ACS Style

Jalili, M.; Scharm, M.; Wolkenhauer, O.; Damaghi, M.; Salehzadeh-Yazdi, A. Exploring the Metabolic Heterogeneity of Cancers: A Benchmark Study of Context-Specific Models. J. Pers. Med. 2021, 11, 496. https://doi.org/10.3390/jpm11060496

AMA Style

Jalili M, Scharm M, Wolkenhauer O, Damaghi M, Salehzadeh-Yazdi A. Exploring the Metabolic Heterogeneity of Cancers: A Benchmark Study of Context-Specific Models. Journal of Personalized Medicine. 2021; 11(6):496. https://doi.org/10.3390/jpm11060496

Chicago/Turabian Style

Jalili, Mahdi, Martin Scharm, Olaf Wolkenhauer, Mehdi Damaghi, and Ali Salehzadeh-Yazdi. 2021. "Exploring the Metabolic Heterogeneity of Cancers: A Benchmark Study of Context-Specific Models" Journal of Personalized Medicine 11, no. 6: 496. https://doi.org/10.3390/jpm11060496

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop