The DOI matches its Crossref record; 44 of the 45 references it cites were found in a registry or an index.
Hypoxia-Inducible Factor-1α and the Glycolytic Phenotype in Tumors
Open accessAbstract
Metastatic tumors generally exhibit aerobic glycolysis (the Warburg effect). The advent of [18F]fluorodeoxyglucose positron emission tomography imaging, coupled with recent findings linking hypoxia-inducible factor (HIF-1alpha) overexpression to aggressive cancers, has rekindled an interest in this aspect of tumor metabolism. These studies explore the role of HIF-1alpha in human breast cancer lines and its relationship to glycolytic regulation. Here we demonstrate that, under normal oxygen conditions, nonmetastatic cells consume less glucose and express low HIF-1alpha, whereas metastatic cells constitutively express high glycolysis and HIF-1alpha, suggesting that dysregulation of HIF-1alpha may induce the Warburg effect. This hypothesis was tested by renormalizing HIF-1alpha levels in renal carcinoma cells, leading to inhibition of aerobic glycolysis.
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.
Integrity signals
Registry
Cites
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.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“It has been well-known since the early 2000s, that the HIF1α stabilization and the elevated HIF1α protein levels are characteristic for ∼50% of tumor cells under normoxia [35].”
From Hypoxia Signaling in Cancer: From Basics to Clinical Practice (2021) · cites this paper as [35]
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
A new mathematical model for relative quantification in real-time RT-PCR
Pfaffl, M. W. (2001) · Nucleic Acids Research · doi:10.1093/nar/29.9.e45 · Cited by 34,610
Warburg, O. (1956) · Science · doi:10.1126/science.123.3191.309 · Cited by 13,425
Tumor Angiogenesis and Metastasis — Correlation in Invasive Breast Carcinoma
Weidner, N., Semple, J. P., Welch, W. R., & Folkman, J. (1991) · New England Journal of Medicine · doi:10.1056/nejm199101033240101 · Cited by 5,598
Jaakkola, P., Mole, D. R., Tian, Y.-M., Wilson, M. I., Gielbert, J., Gaskell, S. J., Kriegsheim, A. V., Hebestreit, H. F., Mukherji, M., Schofield, C. J., et al. (2001) · Science · doi:10.1126/science.1059796 · Cited by 5,535
The tumour suppressor protein VHL targets hypoxia-inducible factors for oxygen-dependent proteolysis
Maxwell, P. H., Wiesener, M. S., Chang, G.-W., Clifford, S. C., Vaux, E. C., Cockman, M. E., Wykoff, C. C., Pugh, C. W., Maher, E. R., & Ratcliffe, P. J. (1999) · Nature · doi:10.1038/20459 · Cited by 5,180
Targeting Hypoxia-Inducible Factors for the Treatment of Anemia in Chronic Kidney Disease Patients
Locatelli, F., Fishbane, S. N., Block, G. A., & Macdougall, I. C. (2017) · American Journal of Nephrology · doi:10.1159/000455166 · Cited by 4,836
HIFα Targeted for VHL-Mediated Destruction by Proline Hydroxylation: Implications for O 2 Sensing
Ivan, M., Kondo, K., Yang, H., Kim, W., Valiando, J., Ohh, M., Salic, A., Asara, J. M., Lane, W. S., & Kaelin Jr., W. G. (2001) · Science · doi:10.1126/science.1059817 · Cited by 4,735 · cited 1×
“HIF-1a is constitutively expressed and destabilized in the presence of O2 by proline hydroxylation and is targeted for proteosomal degradation by the von Hippel-Lindau (vH-L) ubiquitin ligase [11–15].”
BackgroundActivation of vascular endothelial growth factor gene transcription by hypoxia-inducible factor 1
Forsythe, J. A., Jiang, B.-H., Iyer, N. V., Agani, F., Leung, S. W., Koos, R. D., & Semenza, G. L. (1996) · Molecular and Cellular Biology · doi:10.1128/mcb.16.9.4604 · Cited by 3,981
Semenza, G. L., & Wang, G. L. (1992) · Molecular and Cellular Biology · doi:10.1128/mcb.12.12.5447 · Cited by 2,617
Role of HIF-1α in hypoxia-mediated apoptosis, cell proliferation and tumour angiogenesis
Carmeliet, P., Dor, Y., Herbert, J.-M., Fukumura, D., Brusselmans, K., Dewerchin, M., Neeman, M., Bono, F., Abramovitch, R., Maxwell, P., et al. (1998) · Nature · doi:10.1038/28867 · Cited by 2,502
Flagged first, then by citations
1 of the 45 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Items 201–251 of 251
The hypoxia-induced chromatin reader ZMYND8 drives HIF-dependent metabolic rewiring in breast cancer
Nandi, S., Mondal, A., Sarkar, I., Hazari, M. W. A. D., Banerjee, I., Ghosh, S., Roy, H., Banerjee, A., Bandyopadhyay, A., Aich, S., et al. (2025) · Journal of Biological Chemistry · doi:10.1016/j.jbc.2025.110680 · Cited by 5
Fourie, C., du Plessis, M., Mills, J., & Engelbrecht, A. (2022) · Experimental Cell Research · doi:10.1016/j.yexcr.2022.113334 · Cited by 5
Optical Molecular Imaging and Spectroscopy of Oxygenation and Metabolism in Tumors
Im, J., & Rajaram, N. (2015) · IEEE Journal of Selected Topics in Quantum Electronics · doi:10.1109/jstqe.2015.2501379 · Cited by 5
Puvvada, N., Quick, C. M., & Rajaram, N. (2024) · Biophotonics discovery. · doi:10.1117/1.bios.1.1.015003 · Cited by 4
Song, D. H., Jo, J. Y., Kim, C. H., Kim, M. H., Cho, I. A., Shin, J. K., Choi, W. J., & Baek, J. C. (2024) · Diagnostics · doi:10.3390/diagnostics14161735 · Cited by 4
The Effects of Increased Glucose Level and Glycolysis on SARS CoV-2 Infection
Güzel, M., & Zurnacı, F. Ö. (2022) · Mini-Reviews in Medicinal Chemistry · doi:10.2174/1389557522666220318115350 · Cited by 4
Osteocyte Differentiation Requires Glycolysis, but Mature Osteocytes Display Metabolic FlexibilityPreprint
Prideaux, M., Palmier, M., Kitase, Y., Bonewald, L. F., & O’Connell, T. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.05.09.652291 · Cited by 4
Seagroves, T. N. (2009) · Springer eBooks · doi:10.1007/978-1-4419-0131-6_16 · Cited by 3
Breath- and blood-based molecular assessment for gastroesophageal cancer
Vanstraelen, S., Herpe, F. V., Dekervel, J., & Nafteux, P. R. (2025) · ESMO Gastrointestinal Oncology · doi:10.1016/j.esmogo.2025.100132 · Cited by 4
The role of ascorbate in the regulation of HIF-1 in cancer cells
Kuiper, C. (2013) · Otago University Research Archive (University of Otago) · Cited by 2
Histone deacetylases in cancer metabolic reprogramming
Jeon, Y. H., Seo, H., Youn, J. W., Yoo, J. H., Lee, S. W., Oh, S. Y., & Kwon, S. H. (2026) · Experimental & Molecular Medicine · doi:10.1038/s12276-026-01820-1 · Cited by 2
Yang, X., Feng, H., Kim, J., Ti, G., Wang, L., Wang, K., & Song, D. (2024) · Acta Biochimica et Biophysica Sinica · doi:10.3724/abbs.2024083 · Cited by 2
Reversing the Warburg Effect: Metabolic Modulation as a Novel Cancer Therapy
Sutendra, G., & Michelakis, E. D. (2008) · Springer eBooks · doi:10.1007/978-0-387-84835-8_13 · Cited by 2
A Review of the Past, Present, and Future Directions of Neoplasia
Rehemtulla, A., & Ross, B. D. (2005) · Neoplasia · doi:10.1593/neo.05793 · Cited by 2
El-Abd, E. A., Matta, C., Sheta, M., Elkerm, Y., Afifi, M., Benhassine, T., Meftahi, S., Laboratoire de Biologie Cellulaire et Moléculaire, F. D. S. B. (., Sakr, S., & Elsherbini, B. (2014) · Advances in Cancer Research & Treatment · doi:10.5171/2015.965854 · Cited by 2
Metabolic reprogramming and immunity in cancer
Chen, Y., & Li, Y. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-823397-9.00006-5 · Cited by 2
Ristow, M., & Schulz, T. J. (2008) · Springer eBooks · doi:10.1007/978-0-387-84835-8_2 · Cited by 2
Evolutionary dynamics in vascularised tumours under chemotherapyPreprint
Villa, C., Chaplain, M. A. J., & Lorenzi, T. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1912.06381 · Cited by 2
Targeting Mitochondria of Cancer Cells: Mechanisms and Compounds
Chen, G., Pelicano, H., Ogasawara, M. A., Wang, F., & Huang, P. (2014) · Springer eBooks · doi:10.1007/978-94-017-8984-4_8 · Cited by 1
Umeki, Y., Hala, D. N., & Petersen, L. H. (2024) · Biology Open · doi:10.1242/bio.060532 · Cited by 1
Diet-Modifiable Redox Alterations in Ageing and Cancer
Hine, C., Patel, A. K., & Ponti, A. K. (2024) · Sub-cellular biochemistry/Subcellular biochemistry · doi:10.1007/978-3-031-66768-8_7 · Cited by 1
Cellular Responses to Lactic Acidosis in Human Cancers
Chen, J. L.-Y. (2010) · DukeSpace (Duke University) · Cited by 1
Siano, N. (2025) · Library, Museums and Press - UDSpace (University of Delaware) · doi:10.58088/8brr-wk43 · Cited by 1
A Potent Target for Cancer Treatment – Tumor Hypoxia Necrosis: An Overview
Patil, P. B., & Tayade, R. (2014) · Pharmatutor
Metabolic targeting of cancer cells: Two molecular mechanisms involving glucose metabolism
Quiñones, Q. J. (2009) · DukeSpace (Duke University)
Wnt-5a signaling in breast cancer metastasis
Säfholm, A. (2007) · Lund University Publications (Lund University)
The relationship between HIF-1α and autophagy activity in the hypoxic environment of breast cancer
Mills, J. (2013) · SUNScholar (Stellenbosch University)
Neoplasia: Where We Have Been and Where We Are Going
Rehemtulla, A. (2006) · Neoplasia · doi:10.1593/neo.08edi
Neoplasia: An Anniversary of Progress
Rehemtulla, A. (2007) · Neoplasia · doi:10.1593/neo.07968
Metabolic Targeting of Cancer Cells: Two Molecular Mechanisms Involving Glucose
Quiñones, Q. J., & Pizzo, S. V. (2008)
Neurochirurgie, L. F., & Medizin, F. (2014)
Eddy, J. A. (2013) · Illinois Digital Environment for Access to Learning and Scholarship (University of Illinois at Urbana-Champaign)
The Effects Of Bone Marrow Adipocytes On Metabolic Regulation In Metastatic Prostate Cancer
Diedrich, J. D. (2017) · Human Biology
Why the tumor cell metabolism is not that abnormalPreprint
Jacquet, P., & Stéphanou, A. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/865048
Mechanisms of Blood Brain Barrier Dysfunction in Stroke: A Therapeutic Opportunity
Sami, A. (2021) · UA Campus Repository (The University of Arizona)
Tang, Y., Li, M., Hei, J., Ma, L., & Pan, J. (2021) · Research Square · doi:10.21203/rs.3.rs-1046059/v1
Chen, F., Ou, Z., Wang, K., Shen, W., Deng, G., Xu, Y., Wang, L., Zai, Z., Ling, Y., Zhang, T., et al. (2022) · Research Square · doi:10.21203/rs.3.rs-2016769/v1
Nutrient Transporters: New Molecular Targets for Triple Negative Breast Cancer in Type 2 Diabetics
Silva, C., & Martel, F. (2022) · Interdisciplinary cancer research · doi:10.1007/16833_2022_95
Modelling HIF-PHD Dynamics and Related Downstream Pathways
Ferrante, P., & Preziosi, L. (2023) · SEMA SIMAI Springer series · doi:10.1007/978-3-031-35715-2_4
In Silico Disease Models of Breast Cancer
Munshi, A., & Sharma, V. (2014) · Springer eBooks · doi:10.1007/978-81-322-0843-3_16
Vascularized Tumours and the Warburg Effect
Chaplain, M. A. J., & Preziosi, L. (2025) · Interdisciplinary applied mathematics · doi:10.1007/978-0-387-68564-9_17
Métabolisme oxydatif et mitochondrial des cellules cancéreuses de prostate
Matar, C. (2011) · theses.fr (ABES) · doi:10.70675/6abb943ez119bz424fzbe51z3467241fc235
Metabolic Mayhem in the Tumor Microenvironment: Can Mesenchymal Stromal Cells Restore Order?
Abou‐Shanab, A. M., Khairy, A., Hany, F., & El‐Badri, N. (2026) · Stem cell biology and regenerative medicine · doi:10.1007/978-3-032-12705-1_8
Immune Checkpoints in Peritoneal Carcinomatosis : HLA-G, PD-L1 & the Impact of Cancer Therapies
Ullah, M. (2019) · theses.fr (ABES) · doi:10.70675/6b7b16b5z18e9z4f6cz8371z93d206b34a18
He, X., Huang, T., WANG, Z., Bu, F., Cao, X., Xu, M., & Wu, C. (2026) · Oncogene · doi:10.1038/s41388-026-03768-y · Cited by 2
Gómez-Escudero, J., Maniati, E., Holdsworth, J., Beattie, G., Hijazi, M., Guelbert, M., Elorbany, S., Cutillas, P. R., Wang, J., Hodivala‐Dilke, K., et al. (2026) · Angiogenesis · doi:10.1007/s10456-026-10063-7 · Cited by 1
Glucose-responsive nanocarriers for biomedical application
Atta, A. Y. A., Mahmoud, S. A., Fahmy, H. M., & Goda, E. S. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-33641-6.00013-0
Mechanisms Underlying the Coordination of EMT and Glycolysis Mediated by Hypoxia and Glucose
Lu, W., Wang, H., Zhang, X., & Wang, W. (2026) · Current Issues in Molecular Biology · doi:10.3390/cimb48090907
Metadata
- Authors
- Robey, I. F., Lien, A. D., Welsh, S. J., Baggett, B. K., & Gillies, R. J.
- Affiliations
- University of Arizona Cancer Center (US)
- Corresponding author
- Gillies, Robert J.
- Venue
- Neoplasia
- Publisher
- Elsevier BV
- Published
- 2005-04
- Volume
- 7
- Issue
- 4
- Pages
- 324-330
- ISSN
- 1476-5586 (ISSN-L)
- PMC
- PMC1501147
- OpenAlex
- W2088715496
- Cited by
- 251
- References
- 45
- Citations by year
- 2026: 6 · 2025: 8 · 2024: 9 · 2023: 16 · 2022: 14 · 2021: 15 · 2020: 10 · 2019: 16 · 2018: 7 · 2017: 8 · 2016: 6 · 2015: 10 · 2014: 15 · 2013: 14 · 2012: 9
- FWCI
- 5.45
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Neoplasia (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- National Institutes of Health
- National Cancer Institute (R01-CA77575, R01CA077575, R01-CA77975-05)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Cancer, Hypoxia, and Metabolism
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Metabolism, Diabetes, and Cancer
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Cancer, Lipids, and Metabolism
- Keywords
- hypoxia-inducible factor-1α, aerobic glycolysis, Warburg effect, cancer metabolism, metastasis, breast cancer cell lines, renal carcinoma cells, glucose utilization, glycolytic regulation, HIF-1α overexpression
- MeSH terms
- Hypoxia, Glucose, Glycolysis, Humans, Lactates, Neoplasms, Phenotype, Time Factors, Transcription Factors, Blotting, Western, Gene Expression Regulation, Neoplastic, DNA Primers, Reverse Transcriptase Polymerase Chain Reaction, Cell Line, Tumor, Hypoxia-Inducible Factor 1, alpha Subunit
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
