Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 893

The DOI matches its Crossref record; 71 of the 71 references it cites were found in a registry or an index.

The Warburg effect: essential part of metabolic reprogramming and central contributor to cancer progression

Vaupel, Peter, Schmidberger, Heinz, Mayer, Arnulf·2019·International Journal of Radiation Biology

Abstract

In the early 1920s, Warburg published experimental data on the enhanced conversion of glucose to pyruvate (followed by lactate formation) even in the presence of abundant oxygen (aerobic glycolysis, Warburg effect). He attributed this metabolic trait to a respiratory injury and considered this a universal metabolic alteration in carcinogenesis. This interpretation of the data was questioned since the early 1950s. Realistic causative mechanisms and consequences of the Warburg effect were described only during the past 15 years and are summarized in this article. There is clear evidence that mitochondria are not defective in most cancers. Aerobic glycolysis, a key metabolic feature of the Warburg phenotype, is caused by active metabolic reprogramming required to support sustained cancer cell proliferation and malignant progression. This metabolic switch is directed by altered growth factor signaling, hypoxic or normoxic activation of HIF-1α- transcription, oncogene activation or loss-of-function of suppressor genes, and is implemented in the hostile tumor microenvironment. The 'selfish' reprogramming includes (a) overexpression of glucose transporters and of key glycolytic enzymes, and an accelerated glycolytic flux with subsequent accumulation and diversion of glycolytic intermediates for cancer biomass synthesis, (b) high-speed ATP production that meets the energy demand, and (c) accumulation of lactate which drives tumor progression and largely contributes to tumor acidosis, which in turn synergistically favors tumor progression and resistance to certain antitumor therapies, and compromises antitumor immunity. Altogether, the Warburg effect is the central contributor to the cancer progression machinery.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1080/09553002.2019.1589653

Cites

71 of 71 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Cited by

See all 893

Metadata

Authors
Vaupel, P., Schmidberger, H., & Mayer, A.
Venue
International Journal of Radiation Biology
Published
2019-03-22
Volume
95
Issue
7
Pages
912-919
ISSN
  • 0955-3002 (ISSN-L)
  • 1362-3095
OpenAlex
W2918298756
Cited by
893
References
71
FWCI
22.46
Type
journal article
Language
en
Open access
closed

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.