Evidence mapPaperPMID 41720944Full record

ReviewNature reviews. Cancer2026

Decoding cancer across scales with metabolomics.

Joe L Rowles, Gary J Patti

Abstract readReview
PubMed Publisher
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Review
  2. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Joe L RowlesDepartment of Chemistry, Washington University in St. Louis, St. Louis, MO, USA.ORCID http://orcid.org/0000-0002-0716-4759
Gary J PattiDepartment of Chemistry, Washington University in St. Louis, St. Louis, MO, USA. gjpattij@wustl.edu.ORCID http://orcid.org/0000-0002-3748-6193

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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

MetabolomicsNeoplasmsAnimalsHumansMass SpectrometryMetabolic ReprogrammingMetabolomeTumor Microenvironment

Identifiers

What Socratic holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.