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High GINS2 transcript level predicts poor prognosis and correlates with high histological grade and endocrine therapy resistance through mammary cancer stem cells in breast cancer patients

  • Epidemiology
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Abstract

GINS2, a subunit of the GINS complex, is overexpressed in lung adenocarcinoma and metastatic breast tumor; however, its prognostic power and possible molecular mechanisms in breast cancer (BC) remain unclear. In this study, we aimed to explore the function of GINS2 in BC. The association between GINS2 transcript level and the clinical outcome of BC patients were estimated using Kaplan–Meier plots, multivariate cox regression analysis, forest plots, and receiver operating characteristics curves. Gene set enrichment analysis (GSEA) was performed to explore the mechanisms underlying the effects of the GINS2 transcript. High GINS2 transcript level was correlated with poor relapse free survival (log-rank P ≤ 0.001 in six cohorts; forest plot: total n = 1,420, total RR = 1.72, 95 % CI 1.45–2.03; multivariate cox regression analysis: n = 906, HR 2.36, 95 % CI 1.88–2.97), and distant metastasis free survival (log-rank P < 0.01 in 3 cohorts; forest plot: total n = 691, total RR 1.91, 95 % CI 1.36–2.67; multivariate cox regression analysis: n = 442, HR 2.43, 95 % CI 1.70–3.47). BC patients with higher GINS2 transcript levels showed poorer tamoxifen efficacy in a dose-dependent manner. GINS2 expression was significantly downregulated under mutated p53-depleted condition in MDA-468 and MDA-MB-231 cells, upregulated in mammary cancer stem cells (MaCSCs) (P = 0.003), and correlated with upregulated genes in mammary stem cells (GSEA: P < 0.01). Our study, for the first time, demonstrates that GINS2 is an independent prognostic marker and is associated with lung metastasis, histological grade, and endocrine therapy resistance in BC patients, which may attribute to mutant p53 and MaCSCs.

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Abbreviations

BC:

Breast cancer

RFS:

Relapse free survival

DMFS:

Distant metastasis free survival

DSS:

Disease-specific survival

HR:

Hazard ratio

CI:

Confidence interval

ER:

Estrogen receptor

PgR:

Progesterone receptor

ESRRA:

Estrogen receptor 1

GEO:

Gene Expression Omnibus

GSEA:

Gene Set Enrichment Analysis

ROC:

Curve receiver operating characteristics curve

References

  1. Siegel R, Ma J, Zou Z, Jemal A (2014) Cancer statistics. CA Cancer J Clin 64(1):9–29. doi:10.3322/caac.21208

    Article  PubMed  Google Scholar 

  2. DeSantis C, Ma J, Bryan L, Jemal A (2013) Breast cancer statistics. CA Cancer J Clin 64(1):52–62. doi:10.3322/caac.21203

    Article  PubMed  Google Scholar 

  3. Scully OJ, Bay BH, Yip G, Yu Y (2012) Breast cancer metastasis. Cancer Genomics Proteomics 9(5):311–320

    CAS  PubMed  Google Scholar 

  4. MacNeill SA (2010) Structure and function of the GINS complex, a key component of the eukaryotic replisome. Biochem J 425(3):489–500. doi:10.1042/BJ20091531

    Article  CAS  PubMed  Google Scholar 

  5. Labib K, Gambus A (2007) A key role for the GINS complex at DNA replication forks. Trends Cell Biol 17(6):271–278. doi:10.1016/j.tcb.2007.04.002

    Article  CAS  PubMed  Google Scholar 

  6. Tumini E, Plevani P, Muzi-Falconi M, Marini F (2011) Physical and functional crosstalk between Fanconi anemia core components and the GINS replication complex. DNA Repair (Amst) 10(2):149–158. doi:10.1016/j.dnarep.2010.10.006

    Article  CAS  Google Scholar 

  7. Gao Y, Wang S, Liu B, Zhong L (2013) Roles of GINS2 in K562 human chronic myelogenous leukemia and NB4 acute promyelocytic leukemia cells. Int J Mol Med 31(6):1402–1410. doi:10.3892/ijmm.2013.1339

    CAS  PubMed  Google Scholar 

  8. Liu M, Pan H, Zhang F, Zhang YB, Zhang Y, Xia H, Zhu J, Fu WL, Zhang XL (2013) Screening of differentially expressed genes among various TNM stages of lung adenocarcinoma by genomewide gene expression profile analysis. Asian Pac J Cancer Prev 14(11):6281–6286

    Article  PubMed  Google Scholar 

  9. Thomassen M, Tan Q, Kruse TA (2009) Gene expression meta-analysis identifies chromosomal regions and candidate genes involved in breast cancer metastasis. Breast Cancer Res Treat 113(2):239–249. doi:10.1007/s10549-008-9927-2

    Article  PubMed  Google Scholar 

  10. Rantala JK, Edgren H, Lehtinen L, Wolf M, Kleivi K, Vollan HK, Aaltola AR, Laasola P, Kilpinen S, Saviranta P, Iljin K, Kallioniemi O (2010) Integrative functional genomics analysis of sustained polyploidy phenotypes in breast cancer cells identifies an oncogenic profile for GINS2. Neoplasia 12(11):877–888

    CAS  PubMed Central  PubMed  Google Scholar 

  11. Edgar R, Domrachev M, Lash AE (2002) Gene expression omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Res 30(1):207–210

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  12. Tan PK, Downey TJ, Spitznagel EJ, Xu P, Fu D, Dimitrov DS, Lempicki RA, Raaka BM, Cam MC (2003) Evaluation of gene expression measurements from commercial microarray platforms. Nucleic Acids Res 31(19):5676–5684

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  13. Irizarry RA, Hobbs B, Collin F, Beazer-Barclay YD, Antonellis KJ, Scherf U, Speed TP (2003) Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics 4(2):249–264. doi:10.1093/biostatistics/4.2.249

    Article  PubMed  Google Scholar 

  14. Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, Paulovich A, Pomeroy SL, Golub TR, Lander ES, Mesirov JP (2005) Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci USA 102(43):15545–15550. doi:10.1073/pnas.0506580102

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  15. Davis AP, Wiegers TC, Johnson RJ, Lay JM, Lennon-Hopkins K, Saraceni-Richards C, Sciaky D, Murphy CG, Mattingly CJ (2013) Text mining effectively scores and ranks the literature for improving chemical-gene-disease curation at the comparative toxicogenomics database. PLoS ONE 8(4):e58201. doi:10.1371/journal.pone.0058201

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  16. DerSimonian R, Laird N (1986) Meta-analysis in clinical trials. Control Clin Trials 7(3):177–188

    Article  CAS  PubMed  Google Scholar 

  17. Lim E, Wu D, Pal B, Bouras T, Asselin-Labat ML, Vaillant F, Yagita H, Lindeman GJ, Smyth GK, Visvader JE (2010) Transcriptome analyses of mouse and human mammary cell subpopulations reveal multiple conserved genes and pathways. Breast Cancer Res 12(2):R21. doi:10.1186/bcr2560

    Article  PubMed Central  PubMed  Google Scholar 

  18. Pece S, Tosoni D, Confalonieri S, Mazzarol G, Vecchi M, Ronzoni S, Bernard L, Viale G, Pelicci PG, Di Fiore PP (2010) Biological and molecular heterogeneity of breast cancers correlates with their cancer stem cell content. Cell 140(1):62–73. doi:10.1016/j.cell.2009.12.007

    Article  CAS  PubMed  Google Scholar 

  19. Johnston SR (2010) New strategies in estrogen receptor-positive breast cancer. Clin Cancer Res 16(7):1979–1987. doi:10.1158/1078-0432.CCR-09-1823

    Article  CAS  PubMed  Google Scholar 

  20. Weigelt B, Peterse JL, van, TVL (2005) Breast cancer metastasis: markers and models. Nat Rev Cancer 5(8):591–602. doi:10.1038/nrc1670

    Article  CAS  PubMed  Google Scholar 

  21. Criscitiello C, Andre F, Thompson AM, De Laurentiis M, Esposito A, Gelao L, Fumagalli L, Locatelli M, Minchella I, Orsi F, Goldhirsch A, Curigliano G (2014) Biopsy confirmation of metastatic sites in breast cancer patients: clinical impact and future perspectives. Breast Cancer Res 16(2):205

    Article  PubMed Central  PubMed  Google Scholar 

  22. Olivier M, Langerod A, Carrieri P, Bergh J, Klaar S, Eyfjord J, Theillet C, Rodriguez C, Lidereau R, Bieche I, Varley J, Bignon Y, Uhrhammer N, Winqvist R, Jukkola-Vuorinen A, Niederacher D, Kato S, Ishioka C, Hainaut P, Borresen-Dale AL (2006) The clinical value of somatic TP53 gene mutations in 1,794 patients with breast cancer. Clin Cancer Res 12(4):1157–1167. doi:10.1158/1078-0432.CCR-05-1029

    Article  CAS  PubMed  Google Scholar 

  23. Zhang X, Zhong L, Liu BZ, Gao YJ, Gao YM, Hu XX (2013) Effect of GINS2 on proliferation and apoptosis in leukemic cell line. Int J Med Sci 10(12):1795–1804. doi:10.7150/ijms.7025

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  24. Schwartz AM, Henson DE, Chen D, Rajamarthandan S (2014) Histologic grade remains a prognostic factor for breast cancer regardless of the number of positive lymph nodes and tumor size: a study of 161 708 cases of breast cancer from the SEER program. Arch Pathol Lab Med 138(8):1048–1052. doi:10.5858/arpa.2013-0435-OA

    Article  PubMed  Google Scholar 

  25. Monteiro J, Gaspar C, Richer W, Franken PF, Sacchetti A, Joosten R, Idali A, Brandao J, Decraene C, Fodde R (2014) Cancer stemness in Wnt-driven mammary tumorigenesis. Carcinogenesis 35(1):2–13. doi:10.1093/carcin/bgt279

    Article  CAS  PubMed  Google Scholar 

  26. Haldosen LA, Zhao C, Dahlman-Wright K (2014) Estrogen receptor beta in breast cancer. Mol Cell Endocrinol 382(1):665–672. doi:10.1016/j.mce.2013.08.005

    Article  CAS  PubMed  Google Scholar 

  27. Stanford JL, Szklo M, Brinton LA (1986) Estrogen receptors and breast cancer. Epidemiol Rev 8:42–59

    CAS  PubMed  Google Scholar 

  28. O’Brien CS, Farnie G, Howell SJ, Clarke RB (2011) Breast cancer stem cells and their role in resistance to endocrine therapy. Horm Cancer 2(2):91–103. doi:10.1007/s12672-011-0066-6

    Article  PubMed  Google Scholar 

  29. Ahmad A (2013) Pathways to breast cancer recurrence. ISRN Oncol 2013:290568. doi:10.1155/2013/290568

    PubMed Central  PubMed  Google Scholar 

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Acknowledgments

We thank Pan Luxiang, Guo Xu, and Prof. Xing Jinliang for their technical assistance in this project. This work was supported by NO. 81030058 from the National Natural Science Foundation of China and NO. 2015CB553704 from the National Basic Research Program.

Conflict of interest

We declare no potential competing financial interests. The experiments described in the manuscript comply with the current laws of the countries in which they were performed.

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Authors and Affiliations

  1. State Key Laboratory of Cancer Biology, Cell Engineering Research Center & Department of Cell Biology, The Fourth Military Medical University, Xi’an, People’s Republic of China

    Ming Zheng, Yinghui Zhou, Xiangmin Yang, Juan Tang, Ding Wei, Yang Zhang, Jian-Li Jiang & Zhi-nan Chen

  2. Department of Clinical Immunology, Xijing Hospital, The Fourth Military Medical University, Xi’an, People’s Republic of China

    Ming Zheng & Ping Zhu

Authors
  1. Ming Zheng
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  2. Yinghui Zhou
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  3. Xiangmin Yang
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  4. Juan Tang
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Correspondence to Zhi-nan Chen or Ping Zhu.

Additional information

Zheng Ming and Zhou Yinghui contributed equally to this work.

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Zheng, M., Zhou, Y., Yang, X. et al. High GINS2 transcript level predicts poor prognosis and correlates with high histological grade and endocrine therapy resistance through mammary cancer stem cells in breast cancer patients. Breast Cancer Res Treat 148, 423–436 (2014). https://doi.org/10.1007/s10549-014-3172-7

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