The DOI matches its Crossref record; 36 of the 38 references it cites were found in a registry or an index.
An Iron Regulatory Gene Signature Predicts Outcome in Breast Cancer
Open accessPublished version of a preprint
Abstract
Changes in iron regulation characterize the malignant state. However, the pathways that effect these changes and their specific impact on prognosis remain poorly understood. We capitalized on publicly available microarray datasets comprising 674 breast cancer cases to systematically investigate how expression of genes related to iron metabolism is linked to breast cancer prognosis. Of 61 genes involved in iron regulation, 49% were statistically significantly associated with distant metastasis-free survival. Cases were divided into test and training cohorts, and the supervised principal component method was used to stratify cases into risk groups. Optimal risk stratification was achieved with a model comprising 16 genes, which we term the iron regulatory gene signature (IRGS). Multivariable analysis revealed that the IRGS contributes information not captured by conventional prognostic indicators (HR = 1.61; 95% confidence interval: 1.16-2.24; P = 0.004). The IRGS successfully stratified homogeneously treated patients, including ER+ patients treated with tamoxifen monotherapy, both with (P = 0.006) and without (P = 0.03) lymph node metastases. To test whether multiple pathways were embedded within the IRGS, we evaluated the performance of two gene dyads with known roles in iron biology in ER+ patients treated with tamoxifen monotherapy (n = 371). For both dyads, gene combinations that minimized intracellular iron content [anti-import: TFRC(Low)/HFE(High); or pro-export: SLC40A1 (ferroportin)(High)/HAMP(Low)] were associated with favorable prognosis (P < 0.005). Although the clinical utility of the IRGS will require further evaluation, its ability to both identify high-risk patients within traditionally low-risk groups and low-risk patients within high-risk groups has the potential to affect therapeutic decision making.
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“Intriguingly, a significant association between almost 50% of genes involved in iron metabolism and breast cancer prognosis has been observed (Miller et al., 2011).”
From The Role of Iron Regulation in Immunometabolism and Immune-Related Disease (2019) · cites this paper as Miller et al., 2011
In the citing paper
“Interestingly, large amounts of hepcidin are found both within the tumor microenvironment and systemically in cancer patients [73,74,75,76].”
From Iron as a central player and promising target in cancer progression (2019) · cites this paper as [74]
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Cites
Gene Ontology: tool for the unification of biology
Ashburner, M., Ball, C. A., Blake, J. A., Botstein, D., Butler, H., Cherry, J. M., Davis, A. P., Dolinski, K., Dwight, S. S., Eppig, J. T., et al. (2000) · Nature Genetics · doi:10.1038/75556 · Cited by 43,206
Sørlie, T., Perou, C. M., Tibshirani, R. J., Aas, T., Geisler, S. B., Johnsen, H., Hastie, T. J., 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 11,506
Repeated observation of breast tumor subtypes in independent gene expression data sets
Sørlie, T., Tibshirani, R. J., Parker, J. S., Hastie, T. J., Marron, J. S., Nobel, A. B., Deng, S., Johnsen, H., Pesich, R., Geisler, S. B., et al. (2003) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0932692100 · Cited by 5,508
Summaries of Affymetrix GeneChip probe level data
Irizarry, R. A. (2003) · Nucleic Acids Research · doi:10.1093/nar/gng015 · Cited by 4,837 · cited 1×
“For all cases, raw data (CEL files) were quantile-normalized within cohorts by computing Robust Multichip Average (RMA) expression values [Bioconductor "Affy Package (R)"] as previously described (12).”
Supervised Risk Predictor of Breast Cancer Based on Intrinsic Subtypes
Parker, J. S., Mullins, M., Cheang, M. C. U., Leung, S. C. Y., Voduc, D. K., Vickery, T. L., Davies, S. R., Fauron, C. M. ‐., He, X., Hu, Z., et al. (2009) · Journal of Clinical Oncology · doi:10.1200/jco.2008.18.1370 · Cited by 4,818 · cited 1×
“That the IRGS significantly risk-stratified LumA but not LumB cases, suggests that an integrated prognostic model combining aspects of the IRGS and luminal subtypes (or the subtype-derived Risk of Relapse (ROR) score) (34, 35) could provide additive prognostic power to outcome prediction.”
Hepcidin Regulates Cellular Iron Efflux by Binding to Ferroportin and Inducing Its Internalization
Nemeth, E., Tuttle, M. S., Powelson, J., Vaughn, M. B., Donovan, A., Ward, D. M., Ganz, T., & Kaplan, J. (2004) · Science · doi:10.1126/science.1104742 · Cited by 4,803 · cited 1×
“Ferroportin is an iron efflux pump and hepcidin is a peptide hormone that binds to ferroportin and triggers its degradation (11).”
The molecular portraits of breast tumors are conserved across microarray platforms
Hu, Z., Fan, C., Oh, D. S., Marron, J. S., He, X., Qaqish, B. F., Livasy, C., Carey, L., Reynolds, E. R., Dressler, L. G., et al. (2006) · BMC Genomics · doi:10.1186/1471-2164-7-96 · Cited by 1,587
Concordance among Gene-Expression–Based Predictors for Breast Cancer
Fan, C., Oh, D. S., Wessels, L. F. A., Weigelt, B., Nuyten, D. S. A., Nobel, A. B., Veer, L. J. V., & Perou, C. M. (2006) · New England Journal of Medicine · doi:10.1056/nejmoa052933 · Cited by 1,356 · cited 1×
“Intrinsic molecular subtypes of breast cancer were assigned to samples using the Single Sample Predictor (SSP) algorithm as described (13) and utilized by Fan and colleagues (14).”
Anders, C. K., Hsu, D. S., Broadwater, G., Acharya, C. R., Foekens, J. A., Zhang, Y., Wang, Y., Marcom, P. K., Marks, J. R., Febbo, P. G., et al. (2008) · Journal of Clinical Oncology · doi:10.1200/jco.2007.14.2471 · Cited by 925 · cited 1×
“It has been reported that breast cancer in younger women represents a unique biological entity, with increased expression of genes related to hypoxia and other signaling pathways (33).”
Feder, J. N., Penny, D. M., Irrinki, A., Lee, V. K., Lebrón, J. A., Watson, N., Tsuchihashi, Z., Sigal, E., Bjorkman, P. J., & Schatzman, R. C. (1998) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.95.4.1472 · Cited by 827 · cited 7×
“org Cancer Res; 71(21) November 1, 2011 OF3”
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Items 201–202 of 202
Li, Z., Zhang, W., Xu, Y., Zou, W., Nie, T., Wu, J., Chen, L., & Meng, Z. (2025) · Journal of Nanobiotechnology · doi:10.1186/s12951-025-03778-0
“Research has also highlighted the connection between tumor progression and disrupted iron homeostasis at both the microenvironmental and cellular levels [26, 27].”
Rôle de l'autophagie dans l'émergence des cellules souches cancéreuses : implication du métabolisme
El-Hout, M. (2019) · theses.fr (ABES) · doi:10.70675/881cf980z01caz4a56z8a81zdcebe1247536
Metadata
- Authors
- Miller, L. D., Coffman, L. G., Chou, J. W., Black, M. A., Bergh, J., D'Agostino, R., Torti, S. V., & Torti, F. M.
- Affiliations
- Karolinska Institutet (SE)
- Corresponding author
- Torti, Frank M.
- Venue
- Cancer Research
- Published
- 2011-10-30
- Volume
- 71
- Issue
- 21
- Pages
- 6728-6737
- ISSN
- 0008-5472 (ISSN-L)
- 1538-7445
- PMC
- PMC3206152
- OpenAlex
- W2101427112
- Cited by
- 202
- References
- 38
- Citations by year
- 2026: 2 · 2025: 8 · 2024: 3 · 2023: 14 · 2022: 15 · 2021: 26 · 2020: 19 · 2019: 19 · 2018: 9 · 2017: 20 · 2016: 17 · 2015: 8 · 2014: 16 · 2013: 15 · 2012: 11
- FWCI
- 11.45
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Cancer Research (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Cancer Institute (P30CA012197)
- National Institute of Diabetes and Digestive and Kidney Diseases (R01DK071892, R37 DK42412, R37DK042412)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Ferroptosis and cancer prognosis
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Breast Cancer Treatment Studies
- Health Sciences › Medicine › Hematology › Iron Metabolism and Disorders
- Keywords
- breast cancer, iron metabolism, prognostic gene signature, distant metastasis-free survival, risk stratification, estrogen receptor-positive breast cancer, ferroportin, SLC40A1, HFE gene, transferrin receptor, hepcidin, gene expression
- MeSH terms
- Antineoplastic Combined Chemotherapy Protocols, Breast Neoplasms, Estrogens, Female, Humans, Iron, Lymphatic Metastasis, Models, Genetic, Neoplasms, Hormone-Dependent, Prognosis, Tamoxifen, Survival Analysis, Treatment Outcome, Chemotherapy, Adjuvant, Disease-Free Survival, Antineoplastic Agents, Hormonal, Estrogen Receptor Modulators, Gene Expression Profiling
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