The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Large meta-analysis of multiple cancers reveals a common, compact and highly prognostic hypoxia metagene
Open accessAbstract
BACKGROUND: There is a need to develop robust and clinically applicable gene expression signatures. Hypoxia is a key factor promoting solid tumour progression and resistance to therapy; a hypoxia signature has the potential to be not only prognostic but also to predict benefit from particular interventions. METHODS: An approach for deriving signatures that combine knowledge of gene function and analysis of in vivo co-expression patterns was used to define a common hypoxia signature from three head and neck and five breast cancer studies. Previously validated hypoxia-regulated genes (seeds) were used to generate hypoxia co-expression cancer networks. RESULTS: A common hypoxia signature, or metagene, was derived by selecting genes that were consistently co-expressed with the hypoxia seeds in multiple cancers. This was highly enriched for hypoxia-regulated pathways, and prognostic in multivariate analyses. Genes with the highest connectivity were also the most prognostic, and a reduced metagene consisting of a small number of top-ranked genes, including VEGFA, SLC2A1 and PGAM1, outperformed both a larger signature and reported signatures in independent data sets of head and neck, breast and lung cancers. CONCLUSION: Combined knowledge of multiple genes' function from in vitro experiments together with meta-analysis of multiple cancers can deliver compact and robust signatures suitable for clinical application.
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.
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
Subramanian, A., Tamayo, P., Mootha, V. K., Mukherjee, S., Ebert, B. L., Gillette, M. A., Paulovich, A., Pomeroy, S. L., Golub, T. R., Lander, E. S., et al. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0506580102 · Cited by 53,625
The Elements of Statistical Learning
Hastie, T., Tibshirani, R., & Friedman, J. (2009) · Springer Series in Statistics · doi:10.1007/978-0-387-84858-7 · Cited by 25,970
The Elements of Statistical Learning
Hastie, T., Friedman, J., & Tibshirani, R. (2001) · Springer Series in Statistics · doi:10.1007/978-0-387-21606-5 · Cited by 24,162
Hypoxia — a key regulatory factor in tumour growth
Harris, A. L. (2002) · Nature Reviews Cancer · doi:10.1038/nrc704 · Cited by 5,177
Self-citationGene-expression profiles predict survival of patients with lung adenocarcinoma
Beer, D. G., Kardia, S. L. R., Huang, C.-C., Giordano, T. J., Levin, A. M., Misek, D. E., Lin, L., Chen, G., Gharib, T. G., Thomas, D. G., et al. (2002) · Nature Medicine · doi:10.1038/nm733 · Cited by 2,025
Elvidge, G. P., Glenny, L., Appelhoff, R. J., Ratcliffe, P. J., Ragoussis, J., & Gleadle, J. M. (2006) · Journal of Biological Chemistry · doi:10.1074/jbc.m511408200 · Cited by 464
Wolfe, C. J., Kohane, I. S., & Butte, A. J. (2005) · BMC Bioinformatics · doi:10.1186/1471-2105-6-227 · Cited by 422
Cited by
Phenotype molding of stromal cells in the lung tumor microenvironment
Lambrechts, D., Wauters, E., Boeckx, B., Aibar, S., Nittner, D., Burton, O., Bassez, A., Decaluwé, H., Pircher, A., Van den Eynde, K., et al. (2018) · Nature Medicine · doi:10.1038/s41591-018-0096-5 · Cited by 1,648
The updated landscape of tumor microenvironment and drug repurposing
Jin, M.-Z., & Jin, W.-L. (2020) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-020-00280-x · Cited by 1,262
Hypoxia and hypoxia-inducible factors: master regulators of metastasis
Lu, X., & Kang, Y. (2010) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-10-1360 · Cited by 723
Molecular landmarks of tumor hypoxia across cancer types
Bhandari, V., Hoey, C., Liu, L. Y., Lalonde, E., Ray, J., Livingstone, J., Lesurf, R., Shiah, Y.-J., Vujcic, T., Huang, X., et al. (2019) · Nature Genetics · doi:10.1038/s41588-018-0318-2 · Cited by 678
Tumour hypoxia causes DNA hypermethylation by reducing TET activity
Thienpont, B., Steinbacher, J., Zhao, H., D’Anna, F., Kuchnio, A., Ploumakis, A., Ghesquière, B., Van Dyck, L., Boeckx, B., Schoonjans, L., et al. (2016) · Nature · doi:10.1038/nature19081 · Cited by 635
Samanta, D., Gilkes, D. M., Chaturvedi, P., Xiang, L., & Semenza, G. L. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1421438111 · Cited by 460
RetractedOvergaard, J. (2011) · Radiotherapy and Oncology · doi:10.1016/j.radonc.2011.03.004 · Cited by 456
Lalonde, E., Ishkanian, A. S., Sykes, J., Fraser, M., Ross-Adams, H., Erho, N., Dunning, M. J., Halim, S., Lamb, A. D., Moon, N. C., et al. (2014) · The Lancet Oncology · doi:10.1016/s1470-2045(14)71021-6 · Cited by 342
Zhang, H., Wong, C. C. L., Wei, H., Gilkes, D. M., Korangath, P., Chaturvedi, P., Schito, L., Chen, J., Krishnamachary, B., Winnard, P. T., et al. (2012) · Oncogene · doi:10.1038/onc.2011.365 · Cited by 326
RetractedEustace, A., Mani, N., Span, P. N., Irlam, J. J., Taylor, J., Betts, G. N. J., Denley, H., Miller, C. J., Homer, J. J., Rojas, A. M., et al. (2013) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-13-0542 · Cited by 240
Self-citationHypoxia-induced switch in SNAT2/SLC38A2 regulation generates endocrine resistance in breast cancer
Morotti, M., Bridges, E., Valli, A., Choudhry, H., Sheldon, H., Wigfield, S., Gray, N., Zois, C. E., Grimm, F., Jones, D., et al. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1818521116 · Cited by 131
Self-citationA population-level digital histologic biomarker for enhanced prognosis of invasive breast cancer
Amgad, M., Hodge, J. M., Elsebaie, M. A. T., Bodelon, C., Puvanesarajah, S., Gutman, D. A., Siziopikou, K. P., Goldstein, J. A., Gaudet, M. M., Teras, L. R., et al. (2024) · Nature Medicine · doi:10.1038/s41591-023-02643-7 · Cited by 137
The hypoxic tumor microenvironment in vivo selects the cancer stem cell fate of breast cancer cells
Kim, H., Lin, Q., Glazer, P. M., & Yun, Z. (2018) · Breast Cancer Research · doi:10.1186/s13058-018-0944-8 · Cited by 122
Yang, L., Taylor, J., Eustace, A., Irlam, J. J., Denley, H., Hoskin, P. J., Alsner, J., Buffa, F. M., Harris, A. L., Choudhury, A., et al. (2017) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-17-0038 · Cited by 123
Self-citationHypoxia and the phenomenon of immune exclusion
Pietrobon, V., & Marincola, F. M. (2021) · Journal of Translational Medicine · doi:10.1186/s12967-020-02667-4 · Cited by 119
Hypoxia induces a lipogenic cancer cell phenotype via HIF1α-dependent and -independent pathways
Valli, A., Rodriguez, M., Moutsianas, L., Fischer, R., Fedele, V., Huang, H.-L., Van Stiphout, R., Jones, D., Mccarthy, M., Vinaxia, M., et al. (2015) · Oncotarget · doi:10.18632/oncotarget.3058 · Cited by 79
Self-citationMartinez, C.-A., Kerr, B., Jin, C., Cistulli, P. A., & Cook, K. M. (2019) · International Journal of Molecular Sciences · doi:10.3390/ijms20020445 · Cited by 55
Tutzauer, J., Sjöström, M., Holmberg, E., Karlsson, P., Killander, F., Leeb-Lundberg, L. M. F., Malmström, P., Niméus, E., Fernö, M., & Jögi, A. (2022) · British Journal of Cancer · doi:10.1038/s41416-021-01630-4 · Cited by 53
Hypoxic regulation of RIOK3 is a major mechanism for cancer cell invasion and metastasis
Singleton, D. C., Rouhi, P., Zois, C. E., Haider, S., Li, J.-L., Kessler, B. M., Cao, Y., & Harris, A. L. (2015) · Oncogene · doi:10.1038/onc.2014.396 · Cited by 52
Self-citationBIRC2 Expression Impairs Anti-Cancer Immunity and Immunotherapy Efficacy
Samanta, D., Huang, T. Y.-T., Shah, R., Yang, Y., Pan, F., & Semenza, G. L. (2020) · Cell Reports · doi:10.1016/j.celrep.2020.108073 · Cited by 49
Guidelines for using sigQC for systematic evaluation of gene signatures
Dhawan, A., Barberis, A., Cheng, W.-C., Domingo, E., West, C., Maughan, T., Scott, J. G., Harris, A. L., & Buffa, F. M. (2019) · Nature Protocols · doi:10.1038/s41596-019-0136-8 · Cited by 31
Self-citationExploiting convergent phenotypes to derive a pan-cancer cisplatin response gene expression signature
Scarborough, J. A., Eschrich, S. A., Torres-Roca, J., Dhawan, A., & Scott, J. G. (2023) · npj Precision Oncology · doi:10.1038/s41698-023-00375-y · Cited by 14
“This work employs a seed gene approach, inspired by Buffa et al., where previously identi fi ed hypoxia-regulated genes became seeds in a co-expression network, and highly connected genes formed a hypoxia metagene (gene signature) 20 .”
InfluentialExploiting metabolic vulnerabilities after anti-VEGF antibody therapy in ovarian cancer
Glassman, D., Kim, M. S., Spradlin, M., Badal, S., Taki, M., Bhattacharya, P., Dutta, P., Kingsley, C. V., Foster, K. I., Animasahun, O., et al. (2023) · iScience · doi:10.1016/j.isci.2023.106020 · Cited by 15
Metadata
- Authors
- Buffa, F. M., Harris, A. L., West, C. M., & Miller, C. J.
- Venue
- British Journal of Cancer
- Published
- 2010-01-19
- Volume
- 102
- Issue
- 2
- Pages
- 428-435
- ISSN
- 0007-0920 (ISSN-L)
- 1532-1827
- OpenAlex
- W2084116712
- Cited by
- 556
- References
- 7
- FWCI
- 0.87
- Type
- journal article
- Language
- en
- Open access
- hybrid
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.
