The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Transcriptomic and proteomic profiling of KEAP1 disrupted and sulforaphane-treated human breast epithelial cells reveals common expression profiles
Open accessAbstract
Sulforaphane (SFN), an isothiocyanate found in cruciferous vegetables, is a potent inhibitor of experimental mammary carcinogenesis and may be an effective, safe chemopreventive agent for use in humans. SFN acts in part on the Keap1/Nrf2 pathway to regulate a battery of cytoprotective genes. In this study, transcriptomic and proteomic changes in the estrogen receptor negative, non-tumorigenic human breast epithelial MCF10A cell line were analyzed following SFN treatment or KEAP1 knockdown with siRNA using microarray and stable isotopic labeling with amino acids in culture (SILAC), respectively. Changes in selected transcripts and proteins were confirmed by PCR and Western blot in MCF10A and MCF12A cells. There was strong correlation between the transcriptomic and proteomic responses in both the SFN treatment (R = 0.679, P < 0.05) and KEAP1 knockdown (R = 0.853, P < 0.05) experiments. Common pathways for SFN treatment and KEAP1 knockdown were xenobiotic metabolism and antioxidants, glutathione metabolism, carbohydrate metabolism, and NADH/NADPH regeneration. Moreover, these pathways were most prominent in both the transcriptomic and the proteomic analyses. The aldo–keto reductase family members, AKR1B10, AKR1C1, AKR1C2 and AKR1C3, as well as NQO1 and ALDH3A1, were highly upregulated at both the transcriptomic and the proteomic levels. Collectively, these studies served to identify potential biomarkers that can be used in clinical trials to investigate the initial pharmacodynamic action of SFN in the breast.
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
Gene Expression Omnibus: NCBI gene expression and hybridization array data repository
Edgar, R. (2002) · Nucleic Acids Research · doi:10.1093/nar/30.1.207 · Cited by 13,288
Jemal, A., Siegel, R., Xu, J., & Ward, E. (2010) · CA: A Cancer Journal for Clinicians · doi:10.3322/caac.20073 · Cited by 12,596
Itoh, K., Chiba, T., Takahashi, S., Ishii, T., Igarashi, K., Katoh, Y., Oyake, T., Hayashi, N., Satoh, K., Hatayama, I., et al. (1997) · Biochemical and Biophysical Research Communications · doi:10.1006/bbrc.1997.6943 · Cited by 3,999
Jain, A., Lamark, T., Sjøttem, E., Bowitz Larsen, K., Atesoh Awuh, J., Øvervatn, A., McMahon, M., Hayes, J. D., & Johansen, T. (2010) · Journal of Biological Chemistry · doi:10.1074/jbc.m110.118976 · Cited by 1,472
PENNING, T. M., BURCZYNSKI, M. E., JEZ, J. M., HUNG, C.-F., LIN, H.-K., MA, H., MOORE, M., PALACKAL, N., & RATNAM, K. (2000) · Biochemical Journal · doi:10.1042/bj3510067 · Cited by 649
Cited by
NRF2 plays a critical role in mitigating lipid peroxidation and ferroptosis
Dodson, M., Castro-Portuguez, R., & Zhang, D. D. (2019) · Redox Biology · doi:10.1016/j.redox.2019.101107 · Cited by 2,435
Dixon, S. J., Patel, D. N., Welsch, M., Skouta, R., Lee, E. D., Hayano, M., Thomas, A. G., Gleason, C. E., Tatonetti, N. P., Slusher, B. S., et al. (2014) · eLife · doi:10.7554/elife.02523 · Cited by 2,088
Brigelius-Flohé, R., & Maiorino, M. (2013) · Biochimica et Biophysica Acta (BBA) - General Subjects · doi:10.1016/j.bbagen.2012.11.020 · Cited by 1,904
NRF2 and the Hallmarks of Cancer
Rojo de la Vega, M., Chapman, E., & Zhang, D. D. (2018) · Cancer Cell · doi:10.1016/j.ccell.2018.03.022 · Cited by 1,672
The Molecular Mechanisms Regulating the KEAP1-NRF2 Pathway
Baird, L., & Yamamoto, M. (2020) · Molecular and Cellular Biology · doi:10.1128/mcb.00099-20 · Cited by 1,542
Metadata
- Authors
- Agyeman, A. S., Chaerkady, R., Shaw, P. G., Davidson, N. E., Visvanathan, K., Pandey, A., & Kensler, T. W.
- Venue
- Breast Cancer Research and Treatment
- Published
- 2011-05-20
- Volume
- 132
- Issue
- 1
- Pages
- 175-187
- ISSN
- 0167-6806 (ISSN-L)
- 1573-7217
- OpenAlex
- W2030061880
- Cited by
- 251
- References
- 5
- FWCI
- 3.06
- Type
- journal article
- Language
- en
- Open access
- green
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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Transcriptomic and proteomic profiling of KEAP1 disrupted and sulforaphane-treated human breast epithelial cells reveals common expression profiles. https://app.cochat.ai/papers/10.1007/s10549-011-1536-9
- The paper
- Agyeman, A. S., Chaerkady, R., Shaw, P. G., Davidson, N. E., Visvanathan, K., Pandey, A., & Kensler, T. W. (2012). Transcriptomic and proteomic profiling of KEAP1 disrupted and sulforaphane-treated human breast epithelial cells reveals common expression profiles. Breast Cancer Research and Treatment. https://doi.org/10.1007/s10549-011-1536-9
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.