The DOI matches its Crossref record; 64 of the 64 references it cites were found in a registry or an index.
MicroRNA signatures predict oestrogen receptor, progesterone receptor and HER2/neureceptor status in breast cancer
Open accessAbstract
Introduction: Breast cancer is a heterogeneous disease encompassing a number of phenotypically diverse tumours. Expression levels of the oestrogen, progesterone and HER2/neu receptors which characterize clinically distinct breast tumours have been shown to change during disease progression and in response to systemic therapies. Mi(cro)RNAs play critical roles in diverse biological processes and are aberrantly expressed in several human neoplasms including breast cancer, where they function as regulators of tumour behaviour and progression. The aims of this study were to identify miRNA signatures that accurately predict the oestrogen receptor (ER), progesterone receptor (PR) and HER2/neu receptor status of breast cancer patients to provide insight into the regulation of breast cancer phenotypes and progression. Methods: Expression profiling of 453 miRNAs was performed in 29 early-stage breast cancer specimens. miRNA signatures associated with ER, PR and HER2/neu status were generated using artificial neural networks (ANN), and expression of specific miRNAs was validated using RQ-PCR. Results: Stepwise ANN analysis identified predictive miRNA signatures corresponding with oestrogen (miR-342, miR-299, miR-217, miR-190, miR-135b, miR-218), progesterone (miR-520g, miR-377, miR-527-518a, miR-520f-520c) and HER2/neu (miR-520d, miR-181c, miR-302c, miR-376b, miR-30e) receptor status. MiR-342 and miR-520g expression was further analysed in 95 breast tumours. MiR-342 expression was highest in ER and HER2/neu-positive luminal B tumours and lowest in triple-negative tumours. MiR-520g expression was elevated in ER and PR-negative tumours. Conclusions: This study demonstrates that ANN analysis reliably identifies biologically relevant miRNAs associated with specific breast cancer phenotypes. The association of specific miRNAs with ER, PR and HER2/neu status indicates a role for these miRNAs in disease classification of breast cancer. Decreased expression of miR-342 in the therapeutically challenging triple-negative breast tumours, increased miR-342 expression in the luminal B tumours, and downregulated miR-520g in ER and PR-positive tumours indicates that not only is dysregulated miRNA expression a marker for poorer prognosis breast cancer, but that it could also present an attractive target for therapeutic intervention.
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
1 retracted
Genomic signatures to guide the use of chemotherapeutics
Potti, A., Dressman, H. K., Bild, A., Riedel, R. F., Chan, G., Sayer, R., Cragun, J., Cottrill, H., Kelley, M. J., Petersen, R., et al. (2006) · Nature Medicine · doi:10.1038/nm1491 · Cited by 593
RetractedAnalysis of Relative Gene Expression Data Using Real-Time Quantitative PCR and the 2−ΔΔCT Method
Livak, K. J., & Schmittgen, T. D. (2001) · Methods · doi:10.1006/meth.2001.1262 · Cited by 180,860
Bartel (2004) · doi:10.1016/s0092-8674(04)00045-5 · Cited by 34,714
Neural networks for pattern recognition
(1994) · Choice Reviews Online · doi:10.5860/choice.31-5500 · Cited by 18,538
Neural Networks for Pattern Recognition
Bishop, C. M. (1995) · doi:10.1093/oso/9780198538493.001.0001 · Cited by 12,287
Lewis, B. P., Burge, C. B., & Bartel, D. P. (2005) · Cell · doi:10.1016/j.cell.2004.12.035 · Cited by 11,855
Sørlie, T., Perou, C. M., Tibshirani, R., Aas, T., Geisler, S., Johnsen, H., Hastie, T., Eisen, M. B., van de Rijn, M., Jeffrey, S. S., et al. (2001) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.191367098 · Cited by 10,938
MicroRNA expression profiles classify human cancers
Lu, J., Getz, G., Miska, E. A., Alvarez-Saavedra, E., Lamb, J., Peck, D., Sweet-Cordero, A., Ebert, B. L., Mak, R. H., Ferrando, A. A., et al. (2005) · Nature · doi:10.1038/nature03702 · Cited by 9,539
A microRNA expression signature of human solid tumors defines cancer gene targets
Volinia, S., Calin, G. A., Liu, C.-G., Ambs, S., Cimmino, A., Petrocca, F., Visone, R., Iorio, M., Roldo, C., Ferracin, M., et al. (2006) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0510565103 · Cited by 5,697
miRBase: tools for microRNA genomics
Griffiths-Jones, S., Saini, H. K., van Dongen, S., & Enright, A. J. (2007) · Nucleic Acids Research · doi:10.1093/nar/gkm952 · Cited by 4,382
Cited by
MicroRNA dysregulation in cancer: diagnostics, monitoring and therapeutics. A comprehensive review
Iorio, M. V., & Croce, C. M. (2012) · EMBO Molecular Medicine · doi:10.1002/emmm.201100209 · Cited by 1,739
Tominaga, N., Kosaka, N., Ono, M., Katsuda, T., Yoshioka, Y., Tamura, K., Lötvall, J., Nakagama, H., & Ochiya, T. (2015) · Nature Communications · doi:10.1038/ncomms7716 · Cited by 724
microRNA Expression Patterns Reveal Differential Expression of Target Genes with Age
Noren Hooten, N., Abdelmohsen, K., Gorospe, M., Ejiogu, N., Zonderman, A. B., & Evans, M. K. (2010) · PLoS ONE · doi:10.1371/journal.pone.0010724 · Cited by 357
Metastatic breast cancer: the potential of miRNA for diagnosis and treatment monitoring
McGuire, A., Brown, J. A. L., & Kerin, M. J. (2015) · Cancer and Metastasis Reviews · doi:10.1007/s10555-015-9551-7 · Cited by 329
Self-citationmiRNA-mRNA Integrated Analysis Reveals Roles for miRNAs in Primary Breast Tumors
Enerly, E., Steinfeld, I., Kleivi, K., Leivonen, S.-K., Aure, M. R., Russnes, H. G., Rønneberg, J. A., Johnsen, H., Navon, R., Rødland, E., et al. (2011) · PLoS ONE · doi:10.1371/journal.pone.0016915 · Cited by 310
Metadata
- Authors
- Lowery, A. J., Miller, N., Devaney, A., McNeill, R. E., Davoren, P. A., Lemetre, C., Benes, V., Schmidt, S., Blake, J., Ball, G., & Kerin, M. J.
- Venue
- Breast Cancer Research
- Published
- 2009-05-11
- Volume
- 11
- Issue
- 3
- Pages
- R27-R27
- ISSN
- 1465-5411 (ISSN-L)
- 1465-542X
- OpenAlex
- W2081427413
- Cited by
- 450
- References
- 64
- FWCI
- 8.94
- Type
- journal article
- Language
- en
- Open access
- gold
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: MicroRNA signatures predict oestrogen receptor, progesterone receptor and HER2/neureceptor status in breast cancer. https://app.cochat.ai/papers/10.1186/bcr2257
- The paper
- Lowery, A. J., Miller, N., Devaney, A., McNeill, R. E., Davoren, P. A., Lemetre, C., Benes, V., Schmidt, S., Blake, J., Ball, G., & Kerin, M. J. (2009). MicroRNA signatures predict oestrogen receptor, progesterone receptor and HER2/neureceptor status in breast cancer. Breast Cancer Research. https://doi.org/10.1186/bcr2257
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.