An ER-associated miRNA signature predicts prognosis in ER-positive breast cancer
- 2.1k Downloads
Breast cancer patients with positive estrogen receptor (ER) have a better prognosis. However, no prognostic miRNA signature was reported in the ER-positive breast cancer. The aim of the study was to identify and assess the prognostic significance of a miRNA signature in ER-positive breast cancer.
Two cohorts from The Cancer Genome Atlas (TCGA) dataset were used as training (n =596) and testing set (n =319). Differential expression profiling was identified in the training set. And the prognostic value of the miRNA signature was then assessed in the two cohorts.
A total of 14 miRNAs were observed to be associated with the status of ER by significance analysis of microarrays (SAM) in the training set. Patients were characterized as high score or low score group according to the calculated risk scores from each miRNA. And patients in high score group had worse overall survival compared with those in low score group both in the training and testing set.
Our study revealed a miRNA signature including 14 miRNAs associated with ER status which could act as a prognostic marker in ER-positive breast cancer.
KeywordsBreast cancer ER status miRNA signature prognosis
Breast cancer is a heterogeneous disease that comprises a range of subgroups with diverse clinical behaviors and responses to treatment . Many breast-cancer-related genes have been investigated to explore the molecular mechanism of carcinogenesis and diverse clinical outcome of the disease -. Among them, some specific genes such as estrogen receptor (ER) , progesterone receptor (PR)  and human epidermal growth factor receptor 2 (HER2)  have been used to identify different subgroups and indicate different prognostic results with different treatment modalities in the clinical. Patients with ER-positive status which account for almost 70% of breast cancer always had a better prognosis compared with those ER-negative types . However, ER-positive patients also have distinct outcomes and almost 20% might relapse within 10-years after surgery . Thus, there is an urgent need to identify biomarkers that could predict prognostic outcome in patients with ER-positive breast cancer.
MicroRNAs (miRNAs) are short (approximately 22 nucleotides), single-stranded and highly conserved non-coding RNAs which could regulate almost one-third human genome based on either mRNA degradation or translational repression through base pairing with the 3?-untranslated region of target mRNAs at post-transcriptional level ,. Reportedly, miRNAs play important roles in various biological processes, such as cellular development, differentiation, proliferation, angiogenesis and metabolism -. The prognostic value of miRNAs has been explored in several cancer types, such as colon cancer , nasopharyngeal carcinoma , hepatocellular carcinoma  and glioma . To date, no prognostic miRNA signature for ER-positive breast cancer has been reported. In the present study, we used data retrieved from The Cancer Genome Atlas (TCGA, http://cancergenome.nih.gov/) and identify a miRNA signature associated with the status of ER which could act as a prognostic predicator for ER-positive patients.
Methods and materials
The miRNA expression microarray data (Level 3) and corresponding clinical data for breast cancer patients were obtained from The Cancer Genome Atlas (TCGA) database (http://cancergenome.nih.gov) and Ref ,. The data from two independent platforms were classified into two cohorts. The cohort with 596 patients (456 ER-positive and 140 ER-negative) undergone IlluminaHiSeq_miRNASeq platform and the smaller dataset with 319 cases (251 ER-positive and 68 ER-negative) from IlluminaGA_miRNASeq platform were used as training and validation set, respectively. As the data were obtained from TCGA, further approval by an ethics committee was not required.
The differential expression profile between ER-positive and ER-negative cases in training set was assessed by using significance analysis of microarrays (SAM) on BRB array tools package which was developed by Richard Simon and the BRB-ArrayTools Development Team . And P value <0.001 with fold change (FC)?>?2.8 (log2 FC >1.5) was considered significant. Risk score analysis was performed to evaluate the association of ER associated miRNA signature and overall survival of ER-positive patients. ROC curves were used to identify the optimal cutoff value for each miRNA to discriminate ER-positive from negative cases. The score for each miRNA, denoted as S, was set as 1 if the expression level was greater than the cutoff value, otherwise was set as 0 . A risk score formula for predicting survival was developed based on a linear combination of the expression level multiplied regression coefficient derived from the univariate logistic regression model (B) fitted with the status of ER for each significant miRNA: Risk score=. In the equation above, S ij is the risk score for miRNA j on patient i, and Bj is the weight of the risk score of miRNA j. Patients in the training and test set were divided into high score and low score group according to the risk score. Overall survival curves for the two groups were estimated by the Kaplan-Meier methodology and compared using log-rank test.
Survival analyses were performed using SPSS version 16.0 for Windows (Statistical Package for Social Sciences, Chicago, IL). All p values were two-sided and statistical significance was defined as p?<?0.05.
Identification of ER associated miRNA signature
Prognostic value of ER associated miRNA signature in ER-positive patients
In the testing set, similar expression distribution of the miRNAs was found when the cutoff value for each miRNA, the same regression coefficient and cutoff value of risk score derived from the cases in the training phase was applied. And high score group is also prone to exhibiting a worse prognosis (Figure-3C). As shown in Figure-3D, prognosis of cases with high score was significantly worse than those with low score (P?=?0.018).
Breast cancer is the most common malignancy and the second leading cause of cancer death among women worldwide . Due to the distinct clinical, pathological and molecular features of the disease, the treatment, response to therapy and corresponding clinical outcome varies greatly . With the help of molecular profiling and the identification of intrinsic subtypes by specific genes, breast cancer patients could benefit from appropriate treatment . ER status is one of the strong factors in predicting patients- response to endocrine therapy and its determination has become a standard practice in the management of breast cancer . The level of ER was positively correlated with the sensitivity of the endocrine therapy and could predict tamoxifen resistance in breast cancer . However, ER-positive patients are less chemosensitive than ER-negative cases  so that adjuvant chemotherapy might not be beneficial to some ER-positive breast tumors . And ER-positive patients also have distinct behaviors and outcome due to different molecular features. Thus, a biomarker which could accurately predict clinical outcome in ER-positive patients with breast cancer is needed urgently.
In the present study, we used miRNA expression microarray data from TCGA and divided the data into two cohorts based on the cases from two sequencing platforms. Following the strategy of using the larger cohort as training set, and the smaller one as the validation set , we identified 14 miRNAs which were significantly associated with the status of ER both in training and validation set. The optimal cutoff value for each miRNA to discriminate different status of ER was determined by ROC curve. The risk score calculated from expression of each miRNA weighted by regression coefficient B fitted with status of ER might reflect the tendency from positive to negative status of ER. High score might be more likely related to negative status while low score to positive status. To assess the prognostic value of the miRNA signature, the ER-positive cases were divided into high and low score groups according to the risk score. Twelve miRNAs upregulated in ER-negative breast cancer patients exhibited high expression in high score group and two declined miRNAs showed high expression level in low score group. And the ER-positive patients in high score group suffered poorer survival compared with low score group both in the training stage and validation set.
Lowery et al.  found that a 6-miRNA signature could predict status of ER, of which only miR-135b was consistently included in the 14-miRNA signature in our study. To some extent, difference of target population and/or the entry criteria might be responsible for the phenomenon. However, it was also reported that elevated miR-18a , miR-505 , miR-9 and reduced miR-375  were correlated to oestrogen receptor negativity. The results were consistent with our findings. In addition, re-expression of miR-375 could reverse tamoxifen resistance and epithelial-mesenchymal transition-like properties in the established tamoxifen-resistant breast cancer cells . Moreover, high expression of miR-187 in breast cancer could lead to a more aggressive, invasive phenotype and may act as an independent predictor of outcome . Prognostic value of miR-210 has been explored in many cancer types. Breast cancer patients with elevated miR-210 might have a poor outcome ,. Huang et al.  found that miR-224 might act as an oncogene by directly suppressing the RKIP tumor suppressor resulting in promoting metastasis of breast cancer. A higher expression of miR-9 is associated with lymph node metastasis  and could act as a predictor for local recurrence of breast cancer . However, the other 5 miRNAs were not explored so widely in breast cancer and further researches are required to investigate their complex molecular mechanisms.
The specificity of biomarkers based on a single miRNA is generally poor . Thus, we developed a risk score of combination the 14 miRNAs associated with ER status and multiplied their corresponding weight to survival and found that the score could predict overall survival in ER-positive patients. Better insights into the mechanism of the 14-miRNA signature in breast cancer might contribute to an understanding of the genetic aberrations that are involved in tumor genesis, progression and response to treatment.
In conclusion, the ER associated miRNA signature identified in our study might support a potential predictor to indicate clinical outcome for ER-positive patients and serve as potential molecular targets for new therapeutic strategies, subsequently leading to improved outcomes.
ZX, WXP, HZB carried out the molecular genetic studies. ZX, XL, ZW participated in the sequence alignment. ZX, LP drafted the manuscript. All authors read and approved the final manuscript.
This study was supported by the National Natural Science Foundation of China (Grant number: 81171908) and Jiangsu Province Clinical science and technology projects (Clinical Research Center, BL2012008).
- 1.Parker JS, Mullins M, Cheang MC, Leung S, Voduc D, Vickery T, Davies S, Fauron C, He X, Hu Z, Quackenbush JF, Stijleman IJ, Palazzo J, Marron JS, Nobel AB, Mardis E, Nielsen TO, Ellis MJ, Perou CM, Bernard PS: Supervised risk predictor of breast cancer based on intrinsic subtypes. J Clin Oncol 2009, 27: 1160-1167. 10.1200/JCO.2008.18.1370PubMedCentralCrossRefPubMedGoogle Scholar
- 2.Sorlie T, Perou CM, Tibshirani R, Aas T, Geisler S, Johnsen H, Hastie T, Eisen MB, van de Rijn M, Jeffrey SS, Thorsen T, Quist H, Matese JC, Brown PO, Botstein D, Lonning PE, Borresen-Dale AL: Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications. Proc Natl Acad Sci U S A 2001, 98: 10869-10874. 10.1073/pnas.191367098PubMedCentralCrossRefPubMedGoogle Scholar
- 3.Potti A, Dressman HK, Bild A, Riedel RF, Chan G, Sayer R, Cragun J, Cottrill H, Kelley MJ, Petersen R, Harpole D, Marks J, Berchuck A, Ginsburg GS, Febbo P, Lancaster J, Nevins JR: Genomic signatures to guide the use of chemotherapeutics. Nat Med 2006, 12: 1294-1300. 10.1038/nm1491CrossRefPubMedGoogle Scholar
- 7.Bartlett JM, Brookes CL, Robson T, van de Velde CJ, Billingham LJ, Campbell FM, Grant M, Hasenburg A, Hille ET, Kay C, Kieback DG, Putter H, Markopoulos C, Kranenbarg EM, Mallon EA, Dirix L, Seynaeve C, Rea D: Estrogen receptor and progesterone receptor as predictive biomarkers of response to endocrine therapy: a prospectively powered pathology study in the Tamoxifen and Exemestane Adjuvant Multinational trial. J Clin Oncol 2011, 29: 1531-1538. 10.1200/JCO.2010.30.3677PubMedCentralCrossRefPubMedGoogle Scholar
- 9.Rimawi MF, Mayer IA, Forero A, Nanda R, Goetz MP, Rodriguez AA, Pavlick AC, Wang T, Hilsenbeck SG, Gutierrez C, Schiff R, Osborne CK, Chang JC: Multicenter phase II study of neoadjuvant lapatinib and trastuzumab with hormonal therapy and without chemotherapy in patients with human epidermal growth factor receptor 2-overexpressing breast cancer: TBCRC 006. J Clin Oncol 2013, 31: 1726-1731. 10.1200/JCO.2012.44.8027PubMedCentralCrossRefPubMedGoogle Scholar
- 10.Carey LA, Perou CM, Livasy CA, Dressler LG, Cowan D, Conway K, Karaca G, Troester MA, Tse CK, Edmiston S, Deming SL, Geradts J, Cheang MC, Nielsen TO, Moorman PG, Earp HS, Millikan RC: Race, breast cancer subtypes, and survival in the Carolina Breast Cancer Study. JAMA 2006, 295: 2492-2502. 10.1001/jama.295.21.2492CrossRefPubMedGoogle Scholar
- 11.Nishio M, Naoi Y, Tsunashima R, Nakauchi C, Kagara N, Shimoda M, Shimomura A, Maruyama N, Shimazu K, Kim SJ, Noguchi S: 72-Gene classifier for predicting prognosis of estrogen receptor-positive and node-negative breast cancer patients using formalin-fixed, paraffin-embedded tumor tissues. Clin Breast Cancer 2013, 14: e73-80. 10.1016/j.clbc.2013.11.006CrossRefPubMedGoogle Scholar
- 18.Zhang JX, Song W, Chen ZH, Wei JH, Liao YJ, Lei J, Hu M, Chen GZ, Liao B, Lu J, Zhao HW, Chen W, He YL, Wang HY, Xie D, Luo JH: Prognostic and predictive value of a microRNA signature in stage II colon cancer: a microRNA expression analysis. Lancet Oncol 2013, 14: 1295-1306. 10.1016/S1470-2045(13)70491-1CrossRefPubMedGoogle Scholar
- 19.Liu N, Chen NY, Cui RX, Li WF, Li Y, Wei RR, Zhang MY, Sun Y, Huang BJ, Chen M, He QM, Jiang N, Chen L, Cho WC, Yun JP, Zeng J, Liu LZ, Li L, Guo Y, Wang HY, Ma J: Prognostic value of a microRNA signature in nasopharyngeal carcinoma: a microRNA expression analysis. Lancet Oncol 2012, 13: 633-641. 10.1016/S1470-2045(12)70102-XCrossRefPubMedGoogle Scholar
- 22.Kandoth C, McLellan MD, Vandin F, Ye K, Niu B, Lu C, Xie M, Zhang Q, McMichael JF, Wyczalkowski MA, Leiserson MD, Miller CA, Welch JS, Walter MJ, Wendl MC, Ley TJ, Wilson RK, Raphael BJ, Ding L: Mutational landscape and significance across 12 major cancer types. Nature 2013, 502: 333-339. 10.1038/nature12634PubMedCentralCrossRefPubMedGoogle Scholar
- 25.Liu R, Zhang C, Hu Z, Li G, Wang C, Yang C, Huang D, Chen X, Zhang H, Zhuang R, Deng T, Liu H, Yin J, Wang S, Zen K, Ba Y, Zhang CY: A five-microRNA signature identified from genome-wide serum microRNA expression profiling serves as a fingerprint for gastric cancer diagnosis. Eur J Cancer 2011, 47: 784-791. 10.1016/j.ejca.2010.10.025CrossRefPubMedGoogle Scholar
- 27.Jorns JM, Healy P, Zhao L: Review of estrogen receptor, progesterone receptor, and HER-2/neu immunohistochemistry impacts on treatment for a small subset of breast cancer patients transferring care to another institution. Arch Pathol Lab Med 2013, 137: 1660-1663. 10.5858/arpa.2012-0670-OACrossRefPubMedGoogle Scholar
- 28.Berry DA, Cirrincione C, Henderson IC, Citron ML, Budman DR, Goldstein LJ, Martino S, Perez EA, Muss HB, Norton L, Hudis C, Winer EP: Estrogen-receptor status and outcomes of modern chemotherapy for patients with node-positive breast cancer. JAMA 2006, 295: 1658-1667. 10.1001/jama.295.14.1658PubMedCentralCrossRefPubMedGoogle Scholar
- 29.Esslimani-Sahla M, Simony-Lafontaine J, Kramar A, Lavaill R, Mollevi C, Warner M, Gustafsson JA, Rochefort H: Estrogen receptor beta (ER beta) level but not its ER beta cx variant helps to predict tamoxifen resistance in breast cancer. Clin Cancer Res 2004, 10: 5769-5776. 10.1158/1078-0432.CCR-04-0389CrossRefPubMedGoogle Scholar
- 30.Colleoni M, Viale G, Zahrieh D, Pruneri G, Gentilini O, Veronesi P, Gelber RD, Curigliano G, Torrisi R, Luini A, Intra M, Galimberti V, Renne G, Nole F, Peruzzotti G, Goldhirsch A: Chemotherapy is more effective in patients with breast cancer not expressing steroid hormone receptors: a study of preoperative treatment. Clin Cancer Res 2004, 10: 6622-6628. 10.1158/1078-0432.CCR-04-0380CrossRefPubMedGoogle Scholar
- 32.Lowery AJ, Miller N, Devaney A, McNeill RE, Davoren PA, Lemetre C, Benes V, Schmidt S, Blake J, Ball G, Kerin MJ: MicroRNA signatures predict oestrogen receptor, progesterone receptor and HER2/neu receptor status in breast cancer. Breast Cancer Res 2009, 11: R27. 10.1186/bcr2257PubMedCentralCrossRefPubMedGoogle Scholar
- 37.Mulrane L, Madden SF, Brennan DJ, Gremel G, McGee SF, McNally S, Martin F, Crown JP, Jirstrom K, Higgins DG, Gallagher WM, O Connor DP: miR-187 is an independent prognostic factor in breast cancer and confers increased invasive potential in vitro. Clin Cancer Res 2012, 18: 6702-6713. 10.1158/1078-0432.CCR-12-1420CrossRefPubMedGoogle Scholar
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.