ArticleNature communications2018
Biosynthetic energy cost for amino acids decreases in cancer evolution.
Article in Nature communications, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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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
27 citing papers in PubMed, 51 citations in OpenAlex.
- Trade-off between flux efficiency and metabolic control shapes enzyme allocation.Molecular systems biology · 2026Article
- Cancer Evolvability Determines Therapy Outcomes.bioRxiv : the preprint server for biology · 2026Article
- Modeling VEGF and GLUT1 Expression as Coadapted Foraging Strategies in Cancer.bioRxiv : the preprint server for biology · 2026Article
- Outsourcing of energetically costly amino acids at the origin of animals.Nature communications · 2026Article
- Amino acid metabolic regulatory network in tumor immune escape: mechanistic exploration and intervention directions.Frontiers in immunology · 2026Review
- The impact of three amino acids with distinct electrical properties on the synthesis of α-casein by MAC-T cells.Scientific reports · 2025Article
- The cellular economy while crafting regulatory processes relies on protein intrinsic disorder: how and why precision comes at a cost in protein structure.Cell communication and signaling : CCS · 2025Review
- Deuterium labeling enables proteome-wide turnover kinetics analysis in cell culture.Cell reports methods · 2025Article
- Bacterial Amino Acid Auxotrophies Enable Energetically Costlier Proteomes.International journal of molecular sciences · 2025Article
- Constructing a Novel Amino Acid Metabolism Signature: A New Perspective on Pheochromocytoma Diagnosis, Immune Landscape, and Immunotherapy.Biochemical genetics · 2025Article
- Does metabolic rate influence genome-wide amino acid composition in the course of animal evolution?Evolution letters · 2025Article
- Disentangling protein metabolic costs in human cells and tissues.PNAS nexus · 2025Article
- The effects of metabolism on the immune microenvironment in colorectal cancer.Cell death discovery · 2024Review
- Article
- Aspartate-glutamate carrier 2 (citrin): a role in glucose and amino acid metabolism in the liver.BMB reports · 2023Review
- Identification of Some Glutamic Acid Derivatives with Biological Potential by Computational Methods.Molecules (Basel, Switzerland) · 2023Article
- A novel prognostic related lncRNA signature associated with amino acid metabolism in glioma.Frontiers in immunology · 2023Article
- Article
- Amino acid variability, tradeoffs and optimality in human diet.Nature communications · 2022Article
- Protein lifetimes in aged brains reveal a proteostatic adaptation linking physiological aging to neurodegeneration.Science advances · 2022Article
Corrections and comments
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Authors and funding
8 authors at 3 institutions in 2 countries.
Funding
Abstract
Rapidly proliferating cancer cells have much higher demand for proteinogenic amino acids than normal cells. The use of amino acids in human proteomes is largely affected by their bioavailability, which is constrained by the biosynthetic energy cost in living organisms. Conceptually distinct from gene-based analyses, we introduce the energy cost per amino acid (ECPA) to quantitatively characterize the use of 20 amino acids during protein synthesis in human cells. By analyzing gene expression data from The Cancer Genome Atlas, we find that cancer cells evolve to utilize amino acids more economically by optimizing gene expression profile and ECPA shows robust prognostic power across many cancer types. We further validate this pattern in an experimental evolution of xenograft tumors. Our ECPA analysis reveals a common principle during cancer evolution.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.