Language
English
Publication Date
5-10-2025
Journal
npj Systems Biology and Applications
DOI
10.1038/s41540-025-00525-x
PMID
40348758
PMCID
PMC12065808
PubMedCentral® Posted Date
5-10-2025
PubMedCentral® Full Text Version
Post-print
Abstract
Abnormal metabolism is a hallmark of cancer, this was initially recognized nearly a century ago through the observation of aerobic glycolysis in cancer cells. Mitochondrial respiration can also drive tumor progression and metastasis. However, it remains largely unclear the mechanisms by which cancer cells mix and match different metabolic modalities (oxidative/reductive) and leverage various metabolic ingredients (glucose, fatty acids, glutamine) to meet their bioenergetic and biosynthetic needs. Here, we formulate a phenotypic model for cancer metabolism by coupling master gene regulators (AMPK, HIF-1, MYC) with key metabolic substrates (glucose, fatty acids, and glutamine). The model predicts that cancer cells can acquire four metabolic phenotypes: a catabolic phenotype characterized by vigorous oxidative processes-O, an anabolic phenotype characterized by pronounced reductive activities-W, and two complementary hybrid metabolic states-one exhibiting both high catabolic and high anabolic activity-W/O, and the other relying mainly on glutamine oxidation-Q. Using this framework, we quantified gene and metabolic pathway activity by developing scoring metrics based on gene expression. We validated the model-predicted gene-metabolic pathway association and the characterization of the four metabolic phenotypes by analyzing RNA-seq data of tumor samples from TCGA. Strikingly, carcinoma samples exhibiting hybrid metabolic phenotypes are often associated with the worst survival outcomes relative to other metabolic phenotypes. Our mathematical model and scoring metrics serve as a platform to quantify cancer metabolism and study how cancer cells adapt their metabolism upon perturbations, which ultimately could facilitate an effective treatment targeting cancer metabolic plasticity.
Keywords
Humans, Neoplasms, Glutamine, Computational Biology, Computer Simulation, Glucose, Metabolic Networks and Pathways, Models, Biological, Glycolysis, Gene Expression Regulation, Neoplastic, Fatty Acids, Phenotype, Regulatory networks, Computational biology and bioinformatics, Cancer
Published Open-Access
yes
Recommended Citation
Villela-Castrejon, Javier; Levine, Herbert; Kaipparettu, Benny A; et al., "Computational Modeling of Cancer Cell Metabolism Along the Catabolic-Anabolic Axes" (2025). Faculty, Staff and Students Publications. 8103.
https://digitalcommons.library.tmc.edu/baylor_docs/8103