The DOI matches its Crossref record; 31 of the 31 references it cites were found in a registry or an index.
Fructose-1,6-bisphosphatase opposes renal carcinoma progression
Open accessAbstract
Clear cell renal cell carcinoma (ccRCC), the most common form of kidney cancer, is characterized by elevated glycogen levels and fat deposition. These consistent metabolic alterations are associated with normoxic stabilization of hypoxia-inducible factors (HIFs) secondary to von Hippel-Lindau (VHL) mutations that occur in over 90% of ccRCC tumours. However, kidney-specific VHL deletion in mice fails to elicit ccRCC-specific metabolic phenotypes and tumour formation, suggesting that additional mechanisms are essential. Recent large-scale sequencing analyses revealed the loss of several chromatin remodelling enzymes in a subset of ccRCC (these included polybromo-1, SET domain containing 2 and BRCA1-associated protein-1, among others), indicating that epigenetic perturbations are probably important contributors to the natural history of this disease. Here we used an integrative approach comprising pan-metabolomic profiling and metabolic gene set analysis and determined that the gluconeogenic enzyme fructose-1,6-bisphosphatase 1 (FBP1) is uniformly depleted in over six hundred ccRCC tumours examined. Notably, the human FBP1 locus resides on chromosome 9q22, the loss of which is associated with poor prognosis for ccRCC patients. Our data further indicate that FBP1 inhibits ccRCC progression through two distinct mechanisms. First, FBP1 antagonizes glycolytic flux in renal tubular epithelial cells, the presumptive ccRCC cell of origin, thereby inhibiting a potential Warburg effect. Second, in pVHL (the protein encoded by the VHL gene)-deficient ccRCC cells, FBP1 restrains cell proliferation, glycolysis and the pentose phosphate pathway in a catalytic-activity-independent manner, by inhibiting nuclear HIF function via direct interaction with the HIF inhibitory domain. This unique dual function of the FBP1 protein explains its ubiquitous loss in ccRCC, distinguishing FBP1 from previously identified tumour suppressors that are not consistently mutated in all tumours.
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
“Hypoxia can provide energy to tumor cells by activating transcriptional programs that induce glucose uptake and glycolysis.”
From Hypoxia signaling in cancer: Implications for therapeutic interventions (2023) · cites this paper as [47]
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
Understanding the Warburg Effect: The Metabolic Requirements of Cell Proliferation
Vander Heiden, M. G., Cantley, L. C., & Thompson, C. B. (2009) · Science · doi:10.1126/science.1160809 · Cited by 16,197
Intratumor Heterogeneity and Branched Evolution Revealed by Multiregion Sequencing
Gerlinger, M., Rowan, A. J., Horswell, S., Larkin, J., Endesfelder, D., Gronroos, E., Martinez, P., Matthews, N., Stewart, A., Tarpey, P., et al. (2012) · New England Journal of Medicine · doi:10.1056/nejmoa1113205 · Cited by 7,774
Comprehensive molecular characterization of clear cell renal cell carcinoma
The Cancer Genome Atlas Research Network (2013) · Nature · doi:10.1038/nature12222 · Cited by 3,485
Hypoxia-inducible factors and the response to hypoxic stress
Majmundar, A. J., Wong, W. J., & Simon, M. C. (2010) · Molecular Cell · doi:10.1016/j.molcel.2010.09.022 · Cited by 2,380
Self-citationReductive glutamine metabolism by IDH1 mediates lipogenesis under hypoxia
Metallo, C. M., Gameiro, P. A., Bell, E. L., Mattaini, K. R., Yang, J., Hiller, K., Jewell, C. M., Johnson, Z. R., Irvine, D. J., Guarente, L., et al. (2012) · Nature · doi:10.1038/nature10602 · Cited by 1,791
HIF1α and HIF2α: sibling rivalry in hypoxic tumour growth and progression
Keith, B., Johnson, R. S., & Simon, M. C. (2012) · Nature Reviews Cancer · doi:10.1038/nrc3183 · Cited by 1,693
Self-citationFunctional genomics reveal that the serine synthesis pathway is essential in breast cancer
Possemato, R., Marks, K. M., Shaul, Y. D., Pacold, M. E., Kim, D., Birsoy, K., Sethumadhavan, S., Woo, H.-K., Jang, H. G., Jha, A. K., et al. (2011) · Nature · doi:10.1038/nature10350 · Cited by 1,683
Rini, B. I., Campbell, S. C., & Escudier, B. J. (2009) · The Lancet · doi:10.1016/s0140-6736(09)60229-4 · Cited by 1,634
Cohen, H. T., & McGovern, F. J. (2005) · New England Journal of Medicine · doi:10.1056/nejmra043172 · Cited by 1,591
Exome sequencing identifies frequent mutation of the SWI/SNF complex gene PBRM1 in renal carcinoma
Varela, I., Tarpey, P. S., Raine, K. M., Huang, D., Ong, C. K., Stephens, P., Davies, H. R., Jones, D., Lin, M., Teague, J. W., et al. (2011) · Nature · doi:10.1038/nature09639 · Cited by 1,302
Cited by
Sorted by citations
Understanding the Intersections between Metabolism and Cancer Biology
Heiden, M. G. V., & DeBerardinis, R. J. (2017) · Cell · doi:10.1016/j.cell.2016.12.039 · Cited by 2,369
Cancer metabolism: looking forward
Martínez‐Reyes, I., & Chandel, N. S. (2021) · Nature reviews. Cancer · doi:10.1038/s41568-021-00378-6 · Cited by 1,877
The role of ROS in tumour development and progression
Cheung, E. C., & Vousden, K. H. (2022) · Nature reviews. Cancer · doi:10.1038/s41568-021-00435-0 · Cited by 1,663
Kang, H. M., Ahn, S. H., Choi, P., Ko, Y.-A., Han, S. H., Chinga, F., Park, A. S. D., Tao, J., Sharma, K., Pullman, J., et al. (2015) · Nature Medicine · doi:10.1038/nm.3762 · Cited by 1,555
Metabolic pathways promoting cancer cell survival and growth
Boroughs, L. K., & DeBerardinis, R. J. (2015) · Nature Cell Biology · doi:10.1038/ncb3124 · Cited by 1,499
The hallmarks of cancer metabolism: still emerging
Pavlova, N. N., Zhu, J., & Thompson, C. B. (2022) · Cell Metabolism · doi:10.1016/j.cmet.2022.01.007 · Cited by 1,418
Glutathione metabolism in cancer progression and treatment resistance
Bansal, A., & Simon, M. C. (2018) · The Journal of Cell Biology · doi:10.1083/jcb.201804161 · Cited by 1,253
Self-citationReprogramming glucose metabolism in cancer: can it be exploited for cancer therapy?
Hay, N. (2016) · Nature reviews. Cancer · doi:10.1038/nrc.2016.77 · Cited by 1,152
An Integrated Metabolic Atlas of Clear Cell Renal Cell Carcinoma
Hakimi, A. A., Reznik, E., Lee, C., Creighton, C. J., Brannon, A. R., Luna, A., Aksoy, B. A., Liu, E. M., Shen, R., Lee, W., et al. (2016) · Cancer Cell · doi:10.1016/j.ccell.2015.12.004 · Cited by 815
Targeting renal cell carcinoma with a HIF-2 antagonist
Chen, W., Hill, H., Christie, A., Kim, M. S., Holloman, E., Pavia-Jimenez, A., Homayoun, F., Ma, Y., Patel, N., Yell, P., et al. (2016) · Nature · doi:10.1038/nature19796 · Cited by 709
Regulated necrosis: disease relevance and therapeutic opportunities
Conrad, M., Angeli, J. P. F., Vandenabeele, P., & Stockwell, B. R. (2016) · Nature Reviews Drug Discovery · doi:10.1038/nrd.2015.6 · Cited by 630
Mechanisms and Implications of Metabolic Heterogeneity in Cancer
Kim, J., & DeBerardinis, R. J. (2019) · Cell Metabolism · doi:10.1016/j.cmet.2019.08.013 · Cited by 606
Metabolic reprogramming in clear cell renal cell carcinoma
Wettersten, H. I., Aboud, O. A., Lara, P. N., & Weiss, R. H. (2017) · Nature Reviews Nephrology · doi:10.1038/nrneph.2017.59 · Cited by 587
Regulation of chromatin and gene expression by metabolic enzymes and metabolites
Li, X., Egervári, G., Wang, Y., Berger, S. L., & Lu, Z. (2018) · Nature Reviews Molecular Cell Biology · doi:10.1038/s41580-018-0029-7 · Cited by 501
Qi, W., Keenan, H. A., Li, Q., Ishikado, A., Kannt, A., Sadowski, T., Yorek, M. A., Wu, I., Lockhart, S. M., Coppey, L. J., et al. (2017) · Nature Medicine · doi:10.1038/nm.4328 · Cited by 489
HIF drives lipid deposition and cancer in ccRCC via repression of fatty acid metabolism
Du, W., Zhang, L., Brett-Morris, A., Aguila, B., Kerner, J., Hoppel, C. L., Puchowicz, M., Serra, D., Herrero, L., Rini, B. I., et al. (2017) · Nature Communications · doi:10.1038/s41467-017-01965-8 · Cited by 468
The ever-expanding role of HIF in tumour and stromal biology
LaGory, E. L., & Giaccia, A. J. (2016) · Nature Cell Biology · doi:10.1038/ncb3330 · Cited by 445
Oxygen availability and metabolic adaptations
Nakazawa, M. S., Keith, B., & Simon, M. C. (2016) · Nature reviews. Cancer · doi:10.1038/nrc.2016.84 · Cited by 446
Self-citationHypoxia, Hypoxia-inducible Transcription Factors, and Renal Cancer
Schödel, J., Grampp, S., Maher, E. R., Moch, H., Ratcliffe, P. J., Russo, P., & Mole, D. R. (2015) · European Urology · doi:10.1016/j.eururo.2015.08.007 · Cited by 420
Qiu, B., Ackerman, D., Sanchez, D. J., Li, B., Ochocki, J. D., Grazioli, A., Bobrovnikova-Marjon, E., Diehl, J. A., Keith, B., & Simon, M. C. (2015) · Cancer Discovery · doi:10.1158/2159-8290.cd-14-1507 · Cited by 394
Self-citationCong, J., Wang, X., Zheng, X., Wang, D., Fu, B., Sun, R., Tian, Z., & Wei, H. (2018) · Cell Metabolism · doi:10.1016/j.cmet.2018.06.021 · Cited by 387
Four Key Steps Control Glycolytic Flux in Mammalian Cells
Tanner, L. B., Goglia, A. G., Wei, M. H., Sehgal, T., Parsons, L. R., Park, J. O., White, E., Toettcher, J. E., & Rabinowitz, J. D. (2018) · Cell Systems · doi:10.1016/j.cels.2018.06.003 · Cited by 383
Miess, H., Dankworth, B., Gouw, A. M., Rosenfeldt, M., Schmitz, W., Jiang, M., Saunders, B., Howell, M., Downward, J., Felsher, D. W., et al. (2018) · Oncogene · doi:10.1038/s41388-018-0315-z · Cited by 361
Finley, L. W. S. (2023) · Cell · doi:10.1016/j.cell.2023.01.038 · Cited by 343
Glis1 facilitates induction of pluripotency via an epigenome–metabolome–epigenome signalling cascade
Li, L., Chen, K., Wang, T., Wu, Y., Xing, G., Chen, M., Hao, Z., Zhang, C., Zhang, J., Ma, B., et al. (2020) · Nature Metabolism · doi:10.1038/s42255-020-0267-9 · Cited by 317
Gluconeogenesis in Cancer: Function and Regulation of PEPCK, FBPase, and G6Pase
Wang, Z., & Dong, C. (2018) · Trends in cancer · doi:10.1016/j.trecan.2018.11.003 · Cited by 300
Becker, L. M., O’Connell, J. T., Vo, A. P., Cain, M. P., Tampe, D., Bizarro, L., Sugimoto, H., McGow, A. K., Asara, J. M., Lovisa, S., et al. (2020) · Cell Reports · doi:10.1016/j.celrep.2020.107701 · Cited by 296
Courtney, K. D., Bezwada, D., Mashimo, T., Pichumani, K., Vemireddy, V., Funk, A. M., Wimberly, J., McNeil, S. S., Kapur, P., Lotan, Y., et al. (2018) · Cell Metabolism · doi:10.1016/j.cmet.2018.07.020 · Cited by 280
Liao, M., Yao, D., Wu, L., Luo, C., Wang, Z., Zhang, J., & Liu, B. (2023) · Acta Pharmaceutica Sinica B · doi:10.1016/j.apsb.2023.12.003 · Cited by 299
Metabolic Enzymes Moonlighting in the Nucleus: Metabolic Regulation of Gene Transcription
Boukouris, A. E., Zervopoulos, S. D., & Michelakis, E. D. (2016) · Trends in Biochemical Sciences · doi:10.1016/j.tibs.2016.05.013 · Cited by 289
Lim, S., Li, C., Xia, W., Lee, H., Chang, S.-S., Shen, J., Hsu, J. L., Raftery, D., Djukovic, D., Gu, H., et al. (2016) · Cancer Research · doi:10.1158/0008-5472.can-15-2478 · Cited by 270
Johnson, R. W., Finger, E. C., Olcina, M. M., Vilalta, M., Aguilera, T. A., Miao, Y. R., Merkel, A. R., Johnson, J., Sterling, J. A. R., Wu, J. Y., et al. (2016) · Nature Cell Biology · doi:10.1038/ncb3408 · Cited by 266
Shroff, E. H., Eberlin, L. S., Dang, V. M., Gouw, A. M., Gabay, M., Adam, S. J., Bellovin, D. I., Tran, P. T., Philbrick, W. M., Garcia-Ocana, A., et al. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1507228112 · Cited by 255
Li, L., & Li, W. (2015) · Pharmacology & Therapeutics · doi:10.1016/j.pharmthera.2015.01.004 · Cited by 257
Xu, D., Shao, F., Bian, X., Meng, Y., Liang, T., & Lu, Z. (2021) · Cell Metabolism · doi:10.1016/j.cmet.2020.12.015 · Cited by 257
Glutamine fuels proliferation but not migration of endothelial cells
Kim, B., Li, J., Jang, C., & Arany, Z. P. (2017) · The EMBO Journal · doi:10.15252/embj.201796436 · Cited by 257
Metabolic reprogramming in triple-negative breast cancer
Wang, Z., Jiang, Q., & Dong, C. (2020) · Cancer Biology and Medicine · doi:10.20892/j.issn.2095-3941.2019.0210 · Cited by 248
“Mechanistically, FBP1 suppresses HIF-1 α activity by directly binding to its inhibitory domain, thus acting as a transcriptional corepressor in the nucleus to repress the transcription of HIF-1 α target glycolysis-related genes, including GLUT1, LDHA, and PDK1 94 .”
BackgroundAdorno-Cruz, V., Kibria, G., Liu, X., Doherty, M. R., Junk, D. J., Guan, D., Hubert, C., Venere, M., Mulkearns-Hubert, E. E., Sinyuk, M., et al. (2015) · Cancer Research · doi:10.1158/0008-5472.can-14-3225 · Cited by 240
Gluconeogenesis in cancer cells – Repurposing of a starvation-induced metabolic pathway?
Bluemel, G., Smolle, E., Olschewski, H., & Leithner, K. (2019) · Biochimica et Biophysica Acta (BBA) - Reviews on Cancer · doi:10.1016/j.bbcan.2019.05.006 · Cited by 248
Metabolic Reprogramming in Breast Cancer and Its Therapeutic Implications
Gandhi, N. S., & Das, G. M. (2019) · Cells · doi:10.3390/cells8020089 · Cited by 237
A Clearer View of the Molecular Complexity of Clear Cell Renal Cell Carcinoma
Frew, I. J., & Moch, H. (2014) · Annual Review of Pathology Mechanisms of Disease · doi:10.1146/annurev-pathol-012414-040306 · Cited by 230
An N-end rule pathway that recognizes proline and destroys gluconeogenic enzymes
Chen, S., Wu, X., Wadas, B. C., Oh, J., & Varshavsky, A. J. (2017) · Science · doi:10.1126/science.aal3655 · Cited by 224
Conserved and Divergent Features of Human and Mouse Kidney Organogenesis
Lindström, N. O., McMahon, J. A., Guo, J., Tran, T., Guo, Q., Rutledge, E. A., Parvez, R. K., Saribekyan, G., Schuler, R. E., Liao, C., et al. (2018) · Journal of the American Society of Nephrology · doi:10.1681/asn.2017080887 · Cited by 220
Liu, M., Jin, L., Sun, S.-J., Liu, P., Feng, X., Cheng, Z., Liu, W., Guan, K., Shi, Y., Yuan, H., et al. (2018) · Oncogene · doi:10.1038/s41388-017-0070-6 · Cited by 217
Undercover: gene control by metabolites and metabolic enzymesPreprint
van der Knaap, J. A., & Verrijzer, C. P. (2016) · Genes & Development · doi:10.1101/gad.289140.116 · Cited by 222
Oncometabolites in renal cancer
Yong, C., Stewart, G. D., & Frezza, C. (2019) · Nature Reviews Nephrology · doi:10.1038/s41581-019-0210-z · Cited by 216
Li, F., Huangyang, P., Burrows, M., Guo, K., Riscal, R., Godfrey, J. T., Lee, K. E., Lin, N., Lee, P., Blair, I. A., et al. (2020) · Nature Cell Biology · doi:10.1038/s41556-020-0511-2 · Cited by 216
Self-citationRegulation of gene expression by glycolytic and gluconeogenic enzymes
Bian, X., Jiang, H., Meng, Y., Li, Y.-P., Fang, J., & Lu, Z. (2022) · Trends in Cell Biology · doi:10.1016/j.tcb.2022.02.003 · Cited by 211
Metabolic reprogramming in renal cancer: Events of a metabolic disease
Chakraborty, S., Balan, M., Sabarwal, A., Choueiri, T. K., & Pal, S. (2021) · Biochimica et Biophysica Acta (BBA) - Reviews on Cancer · doi:10.1016/j.bbcan.2021.188559 · Cited by 214
Fructose-1,6-bisphosphate couples glycolytic flux to activation of Ras
Peeters, K., Van Leemputte, F., Fischer, B., Bonini, B. M., Quezada, H., Tsytlonok, M., Haesen, D., Vanthienen, W., Bernardes, N., Gonzalez-Blas, C. B., et al. (2017) · Nature Communications · doi:10.1038/s41467-017-01019-z · Cited by 203
Oxygen availability and metabolic reprogramming in cancer
Xie, H. J., & Simon, M. C. (2017) · Journal of Biological Chemistry · doi:10.1074/jbc.r117.799973 · Cited by 207
Self-citationKim, N. H., Cha, Y. H., Lee, J., Lee, S.-H., Yang, J. H., Yun, J. S., Cho, E. S., Zhang, X., Nam, M., Kim, N., et al. (2017) · Nature Communications · doi:10.1038/ncomms14374 · Cited by 211
Moonlighting functions of metabolic enzymes and metabolites in cancer
Pan, C., Li, B., & Simon, M. C. (2021) · Molecular Cell · doi:10.1016/j.molcel.2021.08.031 · Cited by 198
Self-citationInosine Monophosphate Dehydrogenase Dependence in a Subset of Small Cell Lung Cancers
Huang, F., Ni, M., Chalishazar, M. D., Huffman, K. E., Kim, J., Cai, L., Shi, X., Cai, F., Zacharias, L. G., Ireland, A. S., et al. (2018) · Cell Metabolism · doi:10.1016/j.cmet.2018.06.005 · Cited by 188
Hou, Y., Wang, Q., Han, B., Chen, Y., Qiao, X., & Wang, L. (2021) · Cell Death and Disease · doi:10.1038/s41419-021-03813-6 · Cited by 185
The folate cycle enzyme MTHFD2 induces cancer immune evasion through PD-L1 up-regulation
Shang, M., Yang, H., Yang, R., Chen, T., Fu, Y., Li, Y., Fang, X.-L., Zhang, K., Jianju, Z., Li, H., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-22173-5 · Cited by 186
Applications of metabolomics to study cancer metabolism
Kaushik, A. K., & DeBerardinis, R. J. (2018) · Biochimica et Biophysica Acta (BBA) - Reviews on Cancer · doi:10.1016/j.bbcan.2018.04.009 · Cited by 179
The Pentose Phosphate Pathway as a Potential Target for Cancer Therapy
Cho, E. S., Cha, Y. H., Kim, H. S., Kim, N. H., & Yook, J. I. (2017) · Biomolecules & Therapeutics · doi:10.4062/biomolther.2017.179 · Cited by 183
Renal tubular epithelial cells response to injury in acute kidney injury
Li, Z., Li, X., Zhou, Y. N., Wang, B., Lv, L., & Liu, B. (2024) · EBioMedicine · doi:10.1016/j.ebiom.2024.105294 · Cited by 191
A landscape of metabolic variation across tumor types
Reznik, E., Luna, A., Aksoy, B. A., Liu, E. M., La, K., Ostrovnaya, I., Creighton, C. J., Hakimi, A. A., & Sander, C. (2018) · Cell Systems · doi:10.1016/j.cels.2017.12.014 · Cited by 172
DUB3 Promotes BET Inhibitor Resistance and Cancer Progression by Deubiquitinating BRD4
Jin, X., Yan, Y., Wang, D., Ding, D., Ma, T., Ye, Z., Jiménez, R. E., Wang, L., Wu, H., & Huang, H. (2018) · Molecular Cell · doi:10.1016/j.molcel.2018.06.036 · Cited by 169
Genetic and metabolic hallmarks of clear cell renal cell carcinoma
Sanchez, D. J., & Simon, M. C. (2018) · Biochimica et Biophysica Acta (BBA) - Reviews on Cancer · doi:10.1016/j.bbcan.2018.06.003 · Cited by 164
Self-citationKumar, D., New, J., Vishwakarma, V., Joshi, R., Enders, J. D., Lin, F., Dasari, S., Gutierrez, W. R., Leef, G. C., Ponnurangam, S., et al. (2018) · Cancer Research · doi:10.1158/0008-5472.can-17-1076 · Cited by 157
GCN2 inhibition sensitizes arginine-deprived hepatocellular carcinoma cells to senolytic treatment
Missiaen, R., Anderson, N. M., Kim, L. C., Nance, B., Burrows, M., Skuli, N., Carens, M., Riscal, R., Steensels, A., Li, F., et al. (2022) · Cell Metabolism · doi:10.1016/j.cmet.2022.06.010 · Cited by 154
Self-citationTargeting Glutathione Metabolism: Partner in Crime in Anticancer Therapy
Desideri, E., Ciccarone, F., & Ciriolo, M. R. (2019) · Nutrients · doi:10.3390/nu11081926 · Cited by 155
Renal Gluconeogenesis: An Underestimated Role of the Kidney in Systemic Glucose Metabolism
Legouis, D., Faivre, A., Cippà, P. E., & de Seigneux, S. (2022) · Nephrology Dialysis Transplantation · doi:10.1093/ndt/gfaa302 · Cited by 151
Hirata, H., Sugimachi, K., Komatsu, H., Ueda, M., Masuda, T., Uchi, R., Sakimura, S., Nambara, S., Saito, T., Shinden, Y., et al. (2016) · Cancer Research · doi:10.1158/0008-5472.can-15-2601 · Cited by 153
Malic Enzyme Couples Mitochondria with Aerobic Glycolysis in Osteoblasts
Lee, W., Ji, X., Nissim, I., & Long, F. (2020) · Cell Reports · doi:10.1016/j.celrep.2020.108108 · Cited by 143
Self-citationGuo, B., Huang, X., Lee, M. R., Lee, S. A., & Broxmeyer, H. E. (2018) · Nature Medicine · doi:10.1038/nm.4477 · Cited by 141
The tumour microenvironment and metabolism in renal cell carcinoma targeted or immune therapy
Lai, Y., Tang, F., Huang, Y., He, C., Chen, C., Zhao, J., Wu, W., & He, Z. (2020) · Journal of Cellular Physiology · doi:10.1002/jcp.29969 · Cited by 140
Bian, X., Chen, H., Yang, P., Li, Y.-P., Zhang, F.-N., Zhang, J.-Y., Wang, W.-J., Zhao, W., Zhang, S., Chen, Q.-T., et al. (2017) · Nature Communications · doi:10.1038/ncomms14420 · Cited by 137
Transcriptional Response to Hypoxia: The Role of HIF-1-Associated Co-Regulators
Yfantis, A., Mylonis, I., Chachami, G., Nikolaidis, M., Amoutzias, G. D., Paraskeva, E., & Simos, G. (2023) · Cells · doi:10.3390/cells12050798 · Cited by 144
Biological insights through nontargeted metabolomics
Sévin, D. C., Kuehne, A., Zamboni, N., & Sauer, U. (2014) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2014.10.001 · Cited by 141
Fructose 2,6-Bisphosphate in Cancer Cell Metabolism
Bartrons, R., Simon‐Molas, H., Rodríguez‐García, A., Castaño, E., Navarro‐Sabaté, À., Manzano, À., & Martinez‐Outschoorn, U. (2018) · Frontiers in Oncology · doi:10.3389/fonc.2018.00331 · Cited by 136
Jin, X., Pan, Y., Wang, L., Zhang, L., Ravichandran, R., Potts, P. R., Jiang, J., Wu, H., & Huang, H. (2017) · Oncogenesis · doi:10.1038/oncsis.2017.21 · Cited by 131
Bárcena‐Varela, M., Caruso, S., Llerena, S., Álvarez‐Sola, G., Uriarte, I., Latasa, M. U., Urtasun, R., Rebouissou, S., Alvarez, L., Jimenez, M., et al. (2019) · Hepatology · doi:10.1002/hep.30168 · Cited by 130
STAT3 as a mediator of oncogenic cellular metabolism: Pathogenic and therapeutic implications
Tošić, I., & Frank, D. A. (2021) · Neoplasia · doi:10.1016/j.neo.2021.10.003 · Cited by 131
Glycolysis, the sweet appetite of the tumor microenvironment
Kooshan, Z., Cárdenas-Piedra, L., Clements, J. A., & Batra, J. (2024) · Cancer Letters · doi:10.1016/j.canlet.2024.217156 · Cited by 135
Ochocki, J. D., Khare, S., Hess, M., Ackerman, D., Qiu, B., Daisak, J. I., Worth, A. J., Lin, N., Lee, P., Xie, H. J., et al. (2018) · Cell Metabolism · doi:10.1016/j.cmet.2018.04.009 · Cited by 128
Self-citationULK1/2 Constitute a Bifurcate Node Controlling Glucose Metabolic Fluxes in Addition to Autophagy
Li, T. Y., Sun, Y., Liang, Y., Liu, Q., Shi, Y., Zhang, C., Zhang, C., Song, L., Zhang, P., Zhang, X., et al. (2016) · Molecular Cell · doi:10.1016/j.molcel.2016.04.009 · Cited by 124
Cholesterol Auxotrophy as a Targetable Vulnerability in Clear Cell Renal Cell Carcinoma
Riscal, R., Bull, C. J., Mesaros, C., Finan, J. M., Carens, M., Ho, E. S., Xu, J. P., Godfrey, J., Brennan, P., Johansson, M., et al. (2021) · Cancer Discovery · doi:10.1158/2159-8290.cd-21-0211 · Cited by 122
Self-citationHypoxia signaling in cancer: Implications for therapeutic interventions
Zhuang, Y., Liu, K., He, Q., Gu, X., Jiang, C., & Wu, J. (2023) · MedComm · doi:10.1002/mco2.203 · Cited by 119
Wei, G., Sun, H., Dong, K., Hu, L., Wang, Q., Zhuang, Q., Zhu, Y., Zhang, X., Shao, Y., Tang, H., et al. (2021) · Cell Metabolism · doi:10.1016/j.cmet.2021.08.012 · Cited by 119
MTHFD2 links RNA methylation to metabolic reprogramming in renal cell carcinoma
Green, N. H., Galvan, D. L., Badal, S. S., Chang, B. H., LeBleu, V. S., Long, J., Jonasch, E., & Danesh, F. R. (2019) · Oncogene · doi:10.1038/s41388-019-0869-4 · Cited by 119
A compendium of proteins that interact with HIF-1α
Semenza, G. L. (2017) · Experimental Cell Research · doi:10.1016/j.yexcr.2017.03.041 · Cited by 118
Expanding uncapped translation and emerging function of circular RNA in carcinomas and noncarcinomas
Wang, Y., Wu, C., Du, Y., Li, Z., Li, M., Hou, P., Shen, Z., Chu, S., Zheng, J., & Bai, J. (2022) · Molecular Cancer · doi:10.1186/s12943-021-01484-7 · Cited by 112
Organ-Specific Cancer Metabolism and Its Potential for Therapy
Elia, I., Schmieder, R., Christen, S., & Fendt, S. (2015) · Handbook of experimental pharmacology · doi:10.1007/164_2015_10 · Cited by 112
Li, M., He, X., Guo, W., Yu, H., Zhang, S., Wang, N., Liu, G., Sa, R., Shen, X., Jiang, Y., et al. (2020) · Nature Cancer · doi:10.1038/s43018-020-0086-7 · Cited by 113
Li, L., Yang, L., Fan, Z., Xue, W., Shen, Z., Yuan, Y., Sun, X., Wang, D., Lian, J., Wang, L., et al. (2020) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-020-0152-8 · Cited by 111
Metabolic dysregulation in monogenic disorders and cancer — finding method in madness
Erez, A., & DeBerardinis, R. J. (2015) · Nature Reviews Cancer · doi:10.1038/nrc3949 · Cited by 105
Ribonucleotide Reductase Requires Subunit Switching in Hypoxia to Maintain DNA Replication
Foskolou, I. P., Jorgensen, C., Leszczyńska, K. B., Olcina, M. M., Tarhonskaya, H., Haisma, B., D’Angiolella, V., Myers, W. K., Domene, C., Flashman, E., et al. (2017) · Molecular Cell · doi:10.1016/j.molcel.2017.03.005 · Cited by 110
Mohlin, S., Hamidian, A., von Stedingk, K., Bridges, E., Wigerup, C., Bexell, D., & Påhlman, S. (2015) · Cancer Research · doi:10.1158/0008-5472.can-15-0708 · Cited by 104
Glutathione Metabolism in Renal Cell Carcinoma Progression and Implications for Therapies
Xiao, Y., & Meierhofer, D. (2019) · International Journal of Molecular Sciences · doi:10.3390/ijms20153672 · Cited by 104
Targeting glutamine metabolism slows soft tissue sarcoma growth
Lee, P., Malik, D. M., Perkons, N. R., Huangyang, P., Khare, S., Rhoades, S. D., Gong, Y.-Y., Burrows, M., Finan, J. M., Nissim, I., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-14374-1 · Cited by 105
Self-citationYang, H. J., Jin, X., Yan, Y., Shao, Y., Pan, Y., Roberts, L. R., Zhang, J., Huang, H., & Jiang, J. (2017) · Scientific Reports · doi:10.1038/srep43864 · Cited by 100
Zheng, Q., Li, P., Zhou, X., Qiang, Y., Fan, J., Lin, Y., Chen, Y., Guo, J., Wang, F., Xue, H., et al. (2021) · Theranostics · doi:10.7150/thno.60233 · Cited by 100
Flux balance analysis predicts essential genes in clear cell renal cell carcinoma metabolism
Gatto, F., Miess, H., Schulze, A., & Nielsen, J. (2015) · Scientific Reports · doi:10.1038/srep10738 · Cited by 99
Jin, X., Pan, Y., Wang, L., Ma, T., Zhang, L., Tang, A., Billadeau, D. D., Wu, H., & Huang, H. (2017) · Cancer Research · doi:10.1158/0008-5472.can-16-3143 · Cited by 98
Kondaveeti, Y., Reed, I. K. G., & White, B. A. (2015) · Cancer Letters · doi:10.1016/j.canlet.2015.04.025 · Cited by 101
Metabolic alterations in renal cell carcinoma
Massari, F., Ciccarese, C., Santoni, M., Brunelli, M., Piva, F., Modena, A., Bimbatti, D., Fantinel, E., Santini, D., Liang Cheng, C., et al. (2015) · Cancer Treatment Reviews · doi:10.1016/j.ctrv.2015.07.002 · Cited by 99
Metadata
- Authors
- Li, B., Qiu, B., Lee, D. S. M., Walton, Z. E., Ochocki, J. D., Mathew, L. K., Mancuso, A., Gade, T. P. F., Keith, B., Nissim, I., & Simon, M. C.
- Affiliations
- Abramson Cancer Center (US)
- Howard Hughes Medical Institute (US)
- University of Pennsylvania (US)
- Children's Hospital of Philadelphia (US)
- Corresponding author
- Simon, M. Celeste
- ORCID
- Venue
- Nature
- Published
- 2014-07-20
- Volume
- 513
- Issue
- 7517
- Pages
- 251-255
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- PMC
- PMC4162811
- OpenAlex
- W2069663698
- Cited by
- 535
- References
- 31
- Citations by year
- 2026: 24 · 2025: 38 · 2024: 44 · 2023: 44 · 2022: 48 · 2021: 64 · 2020: 54 · 2019: 42 · 2018: 54 · 2017: 42 · 2016: 40 · 2015: 33 · 2014: 6
- FWCI
- 12.92
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Nature (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Funders
- Howard Hughes Medical Institute
- National Institutes of Health
- National Cancer Institute (F30CA177106, P01CA104838, CA104838, F32CA192758, P30CA016520)
- National Institute of Diabetes and Digestive and Kidney Diseases (R01DK053761, DK-053761, R56DK053761)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Cancer, Hypoxia, and Metabolism
- Health Sciences › Medicine › Oncology › Renal cell carcinoma treatment
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Metabolism, Diabetes, and Cancer
- Keywords
- FBP1, clear cell renal cell carcinoma, von Hippel-Lindau protein, hypoxia-inducible factor, glycolysis, pentose phosphate pathway, Warburg effect, renal tubular epithelial cells, tumor progression, chromatin remodeling, cancer metabolism, epigenetic regulation, gluconeogenesis
- MeSH terms
- Animals, Carcinoma, Renal Cell, Cell Line, Epithelial Cells, Glycolysis, Fructose-Bisphosphatase, Humans, Kidney Neoplasms, Models, Molecular, NADP, Swine, Protein Structure, Tertiary, Disease Progression, Cell Line, Tumor, Cell Proliferation, Basic Helix-Loop-Helix Proteins
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