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
- 744 Downloads
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.
KeywordsBreast cancer GINS2 Relapse Metastasis Endocrine therapy resistance Mammary cancer stem cell
Relapse free survival
Distant metastasis free survival
Estrogen receptor 1
Gene Expression Omnibus
Gene Set Enrichment Analysis
Curve receiver operating characteristics curve
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.
- 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):877–888PubMedCentralPubMedGoogle 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 PubMedCentralPubMedCrossRefGoogle 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 PubMedCentralPubMedCrossRefGoogle 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 PubMedCentralPubMedCrossRefGoogle 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 PubMedCrossRefGoogle 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):205PubMedCentralPubMedCrossRefGoogle 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 PubMedCrossRefGoogle 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 PubMedCrossRefGoogle Scholar