ReviewNature reviews. Cancer2026
Decoding cancer across scales with metabolomics.
Review in Nature reviews. Cancer, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- 3D multi-omics tumour atlases: from technology to biology and clinical translation.Nature reviews. Cancer · 2026Review
- Accelerating discovery of cancer causes for prevention in the era of rising early-onset cancers.Cell · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
It is well established that malignant cells alter their metabolism to support proliferation, but the nutrients required to meet the anabolic demands of different cancers located at various anatomical sites throughout the body remain largely unknown. Moreover, the extent to which nutrients are supplied by neighbouring stromal cells or distant tissues, possibly due to metabolic reprogramming, is poorly understood. Metabolomics provides a unique biochemical approach to address these gaps in our knowledge, but cancer studies require careful consideration because it is challenging to identify appropriately matched control samples for comparison. Here, we detail a collection of metabolomics workflows designed to interrogate cancer across three discrete scales. First, we describe experiments to define the nutrient demands of cancer cells themselves. Second, we focus on identifying metabolic relationships between neighbouring cells in the tumour microenvironment. Finally, we highlight strategies to explore the metabolic crosstalk between cancer cells and distant tissues in the tumour macroenvironment. The approaches outlined span cells in culture, animal models and human specimens from patients with cancer. Special emphasis is dedicated to the application of emerging technologies and computational pipelines in the field of mass spectrometry that enable global profiling of metabolites and lipids.
Indexed as
Identifiers
41720944What Socratic holds
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 Socratic graph.