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An eight-lncRNA signature predicts survival of breast cancer patients: a comprehensive study based on weighted gene co-expression network analysis and competing endogenous RNA network

  • Preclinical study
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Breast Cancer Research and Treatment Aims and scope Submit manuscript

Abstract

Purpose

To identify a lncRNA signature to predict survival of breast cancer (BRCA) patients.

Methods

A total of 1222 BRCA case and control datasets were downloaded from the TCGA database. The weighted gene co-expression network analysis of differentially expressed mRNAs was performed to generate the modules associated with BRCA overall survival status and further construct a hub on competing endogenous RNA (ceRNA) network. LncRNA signatures for predicting survival of BRCA patients were generated using univariate survival analyses and a multivariate Cox hazard model analysis and validated and characterized for prognostic performance measured using receiver operating characteristic (ROC) curves.

Results

A prognostic score model of eight lncRNAs signature was identified as Prognostic score = (0.121 × EXPAC007731.1) + (0.108 × EXPAL513123.1) + (0.105 × EXPC10orf126) + (0.065 × EXPWT1-AS) + (− 0.126 × EXPADAMTS9-AS1) + (− 0.130 × EXPSRGAP3-AS2) + (0.116 × EXPTLR8-AS1) + (0.060 × EXPHOTAIR) with median score 1.088. Higher scores predicted higher risk. The lncRNAs signature was an independent prognostic factor associated with overall survival. The area under the ROC curves (AUC) of the signature was 0.979, 0.844, 0.99 and 0.997 by logistic regression, support vector machine, decision tree and random forest models, respectively, and the AUCs in predicting 1- to 10-year survival were between 0.656 and 0.748 in the test dataset from TCGA database.

Conclusions

The eight-lncRNA signature could serve as an independent biomarker for prediction of overall survival of BRCA. The lncRNA-miRNA-mRNA ceRNA network is a good tool to identify lncRNAs that is correlated with overall survival of BRCA.

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Abbreviations

lncRNAs:

Long noncoding RNAs

ceRNA:

Competing endogenous RNA

BRCA:

Breast cancer

TCGA:

The cancer genome atlas

DEmRNAs:

Differentially expressed mRNAs

DEmiRNAs:

Differentially expressed miRNAs

DElncRNAs:

Differentially expressed lncRNAs

WGCNA:

Weighted gene co-expression network analysis

OS:

Overall survival

miRNA:

microRNAs

MREs:

microRNA response element

PCC:

Pearson’s correlation coefficient

PPI:

Protein–protein interaction network

GO:

Gene ontology

KEGG:

Kyoto encyclopedia of genes and genomes

AIC:

Akaike information criterion

ROC:

Receiver operating characteristic

AUC:

The area under the respective ROC curves

EXP:

Expression

ER:

Estrogen receptor

RCC:

Renal cell carcinoma

HR:

Hazard ratio

PCA:

Principal component analysis

References

  1. DeSantis CE, Ma J, Goding Sauer A, Newman LA, Jemal A (2017) Breast cancer statistics, 2017, racial disparity in mortality by state. CA 67(6):439–448

  2. Li G, Hu J, Hu G (2017) Biomarker studies in early detection and prognosis of breast cancer. Adv Exp Med Biol 1026:27–39

    Article  CAS  PubMed  Google Scholar 

  3. Kaklamani V (2006) A genetic signature can predict prognosis and response to therapy in breast cancer: oncotype DX. Expert Rev Mol Diagn 6(6):803–809

    Article  CAS  PubMed  Google Scholar 

  4. Mook S, Schmidt MK, Weigelt B, Kreike B, Eekhout I, van de Vijver MJ, Glas AM, Floore A, Rutgers EJ, van ‘t Veer LJ (2010) The 70-gene prognosis signature predicts early metastasis in breast cancer patients between 55 and 70 years of age. Ann Oncol 21(4):717–722

    Article  CAS  PubMed  Google Scholar 

  5. Bueno-de-Mesquita JM, Linn SC, Keijzer R, Wesseling J, Nuyten DS, van Krimpen C, Meijers C, de Graaf PW, Bos MM, Hart AA et al (2009) Validation of 70-gene prognosis signature in node-negative breast cancer. Breast Cancer Res Treat 117(3):483–495

    Article  CAS  PubMed  Google Scholar 

  6. Weigelt B, Hu Z, He X, Livasy C, Carey LA, Ewend MG, Glas AM, Perou CM, van ‘t Veer LJ (2005) Molecular portraits and 70-gene prognosis signature are preserved throughout the metastatic process of breast cancer. Cancer Res 65(20):9155–9158

    Article  CAS  PubMed  Google Scholar 

  7. Kondo M, Hoshi SL, Ishiguro H, Toi M (2012) Economic evaluation of the 70-gene prognosis-signature (MammaPrint(R)) in hormone receptor-positive, lymph node-negative, human epidermal growth factor receptor type 2-negative early stage breast cancer in Japan. Breast Cancer Res Treat 133(2):759–768

    Article  PubMed  Google Scholar 

  8. Hironaka-Mitsuhashi A, Matsuzaki J, Takahashi RU, Yoshida M, Nezu Y, Yamamoto Y, Shiino S, Kinoshita T, Ushijima T, Hiraoka N et al (2017) A tissue microRNA signature that predicts the prognosis of breast cancer in young women. PLoS ONE 12(11):e0187638

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  9. Zhou J, Liu M, Chen Y, Xu S, Guo Y, Zhao L (2019) Cucurbitacin B suppresses proliferation of pancreatic cancer cells by ceRNA: Effect of miR-146b-5p and lncRNA-AFAP1-AS1. J Cell Physiol 234(4):4655–4667

    Article  CAS  PubMed  Google Scholar 

  10. Dianat-Moghadam H, Heydarifard M, Jahanban-Esfahlan R, Panahi Y, Hamishehkar H, Pouremamali F, Rahbarghazi R, Nouri M (2018) Cancer stem cells-emanated therapy resistance: implications for liposomal drug delivery systems. J Control Release 288:62–83

    Article  CAS  PubMed  Google Scholar 

  11. Torres-Garcia W, Domenech M (2017) Hedgehog-mesenchyme gene signature identifies bi-modal prognosis in luminal and basal breast cancer sub-types. Mol Biosyst 13(12):2615–2624

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Salmena L, Poliseno L, Tay Y, Kats L, Pandolfi PP (2011) A ceRNA hypothesis: the Rosetta Stone of a hidden RNA language? Cell 146(3):353–358

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Cesana M, Daley GQ (2013) Deciphering the rules of ceRNA networks. Proc Natl Acad Sci USA 110(18):7112–7113

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. An Y, Furber KL, Ji S (2017) Pseudogenes regulate parental gene expression via ceRNA network. J Cell Mol Med 21(1):185–192

    Article  CAS  PubMed  Google Scholar 

  15. Tay Y, Rinn J, Pandolfi PP (2014) The multilayered complexity of ceRNA crosstalk and competition. Nature 505(7483):344–352

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Zhou S, Wang L, Yang Q, Liu H, Meng Q, Jiang L, Wang S, Jiang W (2018) Systematical analysis of lncRNA-mRNA competing endogenous RNA network in breast cancer subtypes. Breast Cancer Res Treat 169(2):267–275

    Article  CAS  PubMed  Google Scholar 

  17. Zheng L, Zhang Z, Zhang S, Guo Q, Zhang F, Gao L, Ni H, Guo X, Xiang C, Xi T (2018) RNA binding protein RNPC1 inhibits breast cancer cell metastasis via activating STARD13-correlated ceRNA network. Mol Pharm 15(6):2123–2132

    Article  CAS  PubMed  Google Scholar 

  18. Liu Y, Du Y, Hu X, Zhao L, Xia W (2018) Up-regulation of ceRNA TINCR by SP1 contributes to tumorigenesis in breast cancer. BMC Cancer 18(1):367

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  19. Yuan N, Zhang G, Bie F, Ma M, Ma Y, Jiang X, Wang Y, Hao X (2017) Integrative analysis of lncRNAs and miRNAs with coding RNAs associated with ceRNA crosstalk network in triple negative breast cancer. Onco Targets Ther 10:5883–5897

    Article  PubMed  PubMed Central  Google Scholar 

  20. Li C, Zheng L, Xin Y, Tan Z, Zhang Y, Meng X, Wang Z, Xi T (2017) The competing endogenous RNA network of CYP4Z1 and pseudogene CYP4Z2P exerts an anti-apoptotic function in breast cancer. FEBS Lett 591(7):991–1000

    Article  CAS  PubMed  Google Scholar 

  21. Wang Z, Jensen MA, Zenklusen JC (2016) A practical guide to the cancer genome atlas (TCGA). Methods Mol Biol 1418:111–141

    Article  PubMed  Google Scholar 

  22. Tomczak K, Czerwinska P, Wiznerowicz M (2015) The cancer genome atlas (TCGA): an immeasurable source of knowledge. Contemp Oncol (Pozn) 19(1A):A68–A77

    Google Scholar 

  23. Langfelder P, Horvath S (2008) WGCNA: an R package for weighted correlation network analysis. BMC Bioinform 9:559

    Article  CAS  Google Scholar 

  24. Zhang B, Horvath S (2005) A general framework for weighted gene co-expression network analysis. Stat Appl Genet Mol Biol 4:17

    Article  Google Scholar 

  25. Yu G, Wang LG, Han Y, He QY (2012) clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS 16(5):284–287

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Harris MA, Clark J, Ireland A, Lomax J, Ashburner M, Foulger R, Eilbeck K, Lewis S, Marshall B, Mungall C et al (2004) The gene ontology (GO) database and informatics resource. Nucleic Acids Res 32(Database issue):D258–D261

    CAS  PubMed  Google Scholar 

  27. Kanehisa M, Goto S (2000) KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res 28(1):27–30

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Szklarczyk D, Franceschini A, Wyder S, Forslund K, Heller D, Huerta-Cepas J, Simonovic M, Roth A, Santos A, Tsafou KP et al (2015) STRING v10: protein-protein interaction networks, integrated over the tree of life. Nucleic Acids Res 43:D447–D452

    Article  CAS  PubMed  Google Scholar 

  29. Cline MS, Smoot M, Cerami E, Kuchinsky A, Landys N, Workman C, Christmas R, Avila-Campilo I, Creech M, Gross B et al (2007) Integration of biological networks and gene expression data using cytoscape. Nat Protoc 2(10):2366–2382

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Chou CH, Shrestha S, Yang CD, Chang NW, Lin YL, Liao KW, Huang WC, Sun TH, Tu SJ, Lee WH et al (2018) miRTarBase update 2018: a resource for experimentally validated microRNA-target interactions. Nucleic Acids Res 46(D1):D296–D302

    Article  CAS  PubMed  Google Scholar 

  31. Ding K, Li W, Zou Z, Zou X, Wang C (2014) CCNB1 is a prognostic biomarker for ER+ breast cancer. Med Hypotheses 83(3):359–364

    Article  CAS  PubMed  Google Scholar 

  32. Feng W, Li HC, Xu K, Chen YF, Pan LY, Mei Y, Cai H, Jiang YM, Chen T, Feng DX (2016) SHCBP1 is over-expressed in breast cancer and is important in the proliferation and apoptosis of the human malignant breast cancer cell line. Gene 587(1):91–97

    Article  CAS  PubMed  Google Scholar 

  33. Alshareeda AT, Negm OH, Green AR, Nolan CC, Tighe P, Albarakati N, Sultana R, Madhusudan S, Ellis IO, Rakha EA (2015) KPNA2 is a nuclear export protein that contributes to aberrant localisation of key proteins and poor prognosis of breast cancer. Br J Cancer 112(12):1929–1937

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Tormo E, Adam-Artigues A, Ballester S, Pineda B, Zazo S, Gonzalez-Alonso P, Albanell J, Rovira A, Rojo F, Lluch A et al (2017) The role of miR-26a and miR-30b in HER2+ breast cancer trastuzumab resistance and regulation of the CCNE2 gene. Sci Rep 7:41309

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Pegoraro S, Ros G, Ciani Y, Sgarra R, Piazza S, Manfioletti G (2015) A novel HMGA1-CCNE2-YAP axis regulates breast cancer aggressiveness. Oncotarget 6(22):19087–19101

    Article  PubMed  PubMed Central  Google Scholar 

  36. Taghavi A, Akbari ME, Hashemi-Bahremani M, Nafissi N, Khalilnezhad A, Poorhosseini SM, Hashemi-Gorji F, Yassaee VR (2016) Gene expression profiling of the 8q22-24 position in human breast cancer: TSPYL5, MTDH, ATAD2 and CCNE2 genes are implicated in oncogenesis, while WISP1 and EXT1 genes may predict a risk of metastasis. Oncol Lett 12(5):3845–3855

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Klopocki E, Kristiansen G, Wild PJ, Klaman I, Castanos-Velez E, Singer G, Stohr R, Simon R, Sauter G, Leibiger H et al (2004) Loss of SFRP1 is associated with breast cancer progression and poor prognosis in early stage tumors. Int J Oncol 25(3):641–649

    CAS  PubMed  Google Scholar 

  38. Bernemann C, Hulsewig C, Ruckert C, Schafer S, Blumel L, Hempel G, Gotte M, Greve B, Barth PJ, Kiesel L et al (2014) Influence of secreted frizzled receptor protein 1 (SFRP1) on neoadjuvant chemotherapy in triple negative breast cancer does not rely on WNT signaling. Mol Cancer 13:174

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  39. Xiao C, Wu CH, Hu HZ (2016) LncRNA UCA1 promotes epithelial-mesenchymal transition (EMT) of breast cancer cells via enhancing Wnt/beta-catenin signaling pathway. Eur Rev Med Pharmacol Sci 20(13):2819–2824

    CAS  PubMed  Google Scholar 

  40. Deng J, Yang M, Jiang R, An N, Wang X, Liu B (2017) Long non-coding RNA HOTAIR regulates the proliferation, Self-renewal capacity, tumor formation and migration of the cancer stem-like cell (CSC) subpopulation enriched from breast cancer cells. PLoS ONE 12(1):e0170860

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  41. Xue X, Yang YA, Zhang A, Fong KW, Kim J, Song B, Li S, Zhao JC, Yu J (2016) LncRNA HOTAIR enhances ER signaling and confers tamoxifen resistance in breast cancer. Oncogene 35(21):2746–2755

    Article  CAS  PubMed  Google Scholar 

  42. Sorensen KP, Thomassen M, Tan Q, Bak M, Cold S, Burton M, Larsen MJ, Kruse TA (2013) Long non-coding RNA HOTAIR is an independent prognostic marker of metastasis in estrogen receptor-positive primary breast cancer. Breast Cancer Res Treat 142(3):529–536

    Article  PubMed  CAS  Google Scholar 

  43. Pan Y, Zhang J, Fu H, Shen L (2016) miR-144 functions as a tumor suppressor in breast cancer through inhibiting ZEB1/2-mediated epithelial mesenchymal transition process. Onco Targets Ther 9:6247–6255

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Yin Y, Cai J, Meng F, Sui C, Jiang Y (2018) MiR-144 suppresses proliferation, invasion, and migration of breast cancer cells through inhibiting CEP55. Cancer Biol Ther 19(4):306–315

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Ye ZB, Ma G, Zhao YH, Xiao Y, Zhan Y, Jing C, Gao K, Liu ZH, Yu SJ (2015) miR-429 inhibits migration and invasion of breast cancer cells in vitro. Int J Oncol 46(2):531–538

    Article  CAS  PubMed  Google Scholar 

  46. Li D, Wang H, Song H, Xu H, Zhao B, Wu C, Hu J, Wu T, Xie D, Zhao J et al (2017) The microRNAs miR-200b-3p and miR-429-5p target the LIMK1/CFL1 pathway to inhibit growth and motility of breast cancer cells. Oncotarget 8(49):85276–85289

    PubMed  PubMed Central  Google Scholar 

  47. Wang C, Ju H, Shen C, Tong Z (2015) miR-429 mediates delta-tocotrienol-induced apoptosis in triple-negative breast cancer cells by targeting XIAP. Int J Clin Exp Med 8(9):15648–15656

    CAS  PubMed  PubMed Central  Google Scholar 

  48. Olgun G, Sahin O, Tastan O (2018) Discovering lncRNA mediated sponge interactions in breast cancer molecular subtypes. BMC Genom 19(1):650

    Article  CAS  Google Scholar 

  49. Chen J, Xu J, Li Y, Zhang J, Chen H, Lu J, Wang Z, Zhao X, Xu K, Li X et al (2017) Competing endogenous RNA network analysis identifies critical genes among the different breast cancer subtypes. Oncotarget 8(6):10171–10184

    PubMed  Google Scholar 

  50. Xiao B, Zhang W, Chen L, Hang J, Wang L, Zhang R, Liao Y, Chen J, Ma Q, Sun Z et al (2018) Analysis of the miRNA-mRNA-lncRNA network in human estrogen receptor-positive and estrogen receptor-negative breast cancer based on TCGA data. Gene 658:28–35

    Article  CAS  PubMed  Google Scholar 

  51. Wu Q, Guo L, Jiang F, Li L, Li Z, Chen F (2015) Analysis of the miRNA-mRNA-lncRNA networks in ER + and ER- breast cancer cell lines. J Cell Mol Med 19(12):2874–2887

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Paci P, Colombo T, Farina L (2014) Computational analysis identifies a sponge interaction network between long non-coding RNAs and messenger RNAs in human breast cancer. BMC Syst Biol 8:83

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  53. Han L, Zhang HC, Li L, Li CX, Di X, Qu X (2018) Downregulation of long noncoding RNA HOTAIR and EZH2 Induces apoptosis and inhibits proliferation, invasion, and migration of human breast cancer cells. Cancer Biother Radiopharm 33(6):241–251

    Article  CAS  PubMed  Google Scholar 

  54. Zhao W, Geng D, Li S, Chen Z, Sun M (2018) LncRNA HOTAIR influences cell growth, migration, invasion, and apoptosis via the miR-20a-5p/HMGA2 axis in breast cancer. Cancer Med 7(3):842–855

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Ozes AR, Miller DF, Ozes ON, Fang F, Liu Y, Matei D, Huang T, Nephew KP (2016) NF-kappaB-HOTAIR axis links DNA damage response, chemoresistance and cellular senescence in ovarian cancer. Oncogene 35(41):5350–5361

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Battistelli C, Cicchini C, Santangelo L, Tramontano A, Grassi L, Gonzalez FJ, de Nonno V, Grassi G, Amicone L, Tripodi M (2017) The snail repressor recruits EZH2 to specific genomic sites through the enrollment of the lncRNA HOTAIR in epithelial-to-mesenchymal transition. Oncogene 36(7):942–955

    Article  CAS  PubMed  Google Scholar 

  57. Gupta RA, Shah N, Wang KC, Kim J, Horlings HM, Wong DJ, Tsai MC, Hung T, Argani P, Rinn JL et al (2010) Long non-coding RNA HOTAIR reprograms chromatin state to promote cancer metastasis. Nature 464(7291):1071–1076

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Kaneuchi M, Sasaki M, Tanaka Y, Shiina H, Yamada H, Yamamoto R, Sakuragi N, Enokida H, Verma M, Dahiya R (2005) WT1 and WT1-AS genes are inactivated by promoter methylation in ovarian clear cell adenocarcinoma. Cancer 104(9):1924–1930

    Article  CAS  PubMed  Google Scholar 

  59. Du T, Zhang B, Zhang S, Jiang X, Zheng P, Li J, Yan M, Zhu Z, Liu B (2016) Decreased expression of long non-coding RNA WT1-AS promotes cell proliferation and invasion in gastric cancer. Biochim Biophys Acta 1862(1):12–19

    Article  CAS  PubMed  Google Scholar 

  60. Lv L, Chen G, Zhou J, Li J, Gong J (2015) WT1-AS promotes cell apoptosis in hepatocellular carcinoma through down-regulating of WT1. J Exp Clin Cancer Res 34:119

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  61. Wang H, Fu Z, Dai C, Cao J, Liu X, Xu J, Lv M, Gu Y, Zhang J, Hua X et al (2016) LncRNAs expression profiling in normal ovary, benign ovarian cyst and malignant epithelial ovarian cancer. Sci Rep 6:38983

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Zhu N, Hou J, Wu Y, Liu J, Li G, Zhao W, Ma G, Chen B, Song Y (2018) Integrated analysis of a competing endogenous RNA network reveals key lncRNAs as potential prognostic biomarkers for human bladder cancer. Medicine (Baltimore) 97(35):e11887

    Article  CAS  Google Scholar 

  63. Xing Y, Zhao Z, Zhu Y, Zhao L, Zhu A, Piao D (2018) Comprehensive analysis of differential expression profiles of mRNAs and lncRNAs and identification of a 14-lncRNA prognostic signature for patients with colon adenocarcinoma. Oncol Rep 39(5):2365–2375

    CAS  PubMed  Google Scholar 

  64. Li Z, Yao Q, Zhao S, Wang Y, Li Y, Wang Z (2017) Comprehensive analysis of differential co-expression patterns reveal transcriptional dysregulation mechanism and identify novel prognostic lncRNAs in esophageal squamous cell carcinoma. Onco Targets Ther 10:3095–3105

    Article  PubMed  PubMed Central  Google Scholar 

  65. Yang Z, Li H, Wang Z, Yang Y, Niu J, Liu Y, Sun Z, Yin C (2018) Microarray expression profile of long non-coding RNAs in human lung adenocarcinoma. Thorac Cancer 9(10):1312–1322

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Yu F, Quan F, Xu J, Zhang Y, Xie Y, Zhang J, Lan Y, Yuan H, Zhang H, Cheng S et al (2018) Breast cancer prognosis signature: linking risk stratification to disease subtypes. Brief Bioinform

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Acknowledgements

The authors gratefully acknowledge The Cancer Genome Atlas pilot project (established by NCI and NHGRI), which made the genomic data and clinical data of BRCA available.

Funding

This research was supported by the Natural Science Foundation of Hubei Provincial Department of Education (Q20182105), Natural Science Foundation of Hubei Province of China (2016CFB530) and Faculty Development Foundation of Hubei University of Medicine (2014QDJZR01), Chen Xiao-ping Foundation for the development of science and technology of Hubei Provincial (CXPJJH11800001-2018333) and Innovation and entrepreneurship training program (201810929009, 201810929068 and 201813249010).

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Author notes

  1. Min Sun and Di Wu have contributed equally to this work.

Authors and Affiliations

  1. Department of General Surgery, Taihe Hospital, Hubei University of Medicine, Shiyan, 442000, China

    Min Sun, Ke Zhou, Heng Li & Dong Huang

  2. Department of Anesthesiology, Institute of Anesthesiology, Taihe Hospital, Hubei University of Medicine, Shiyan, 442000, China

    Min Sun, Di Wu, Xingrui Gong, Qiong Wei, Mengyu Du, Jin Zha & Hongrui Zhu

  3. The First Clinical School, Hubei University of Medicine, Shiyan, 442000, China

    Peijie Lei

  4. College of Basic Medical Sciences, Hubei University of Medicine, Shiyan, 442000, China

    Xinsheng Gu

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  1. Min Sun
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Contributions

Min Sun, Dong Huang and Xinsheng Gu participated in research design. Min Sun, Di Wu, Mengyu Du and Jin Zha performed data analysis. Min Sun, Mengyu Du and Xinsheng Gu wrote or contributed to the writing of the manuscript.

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Correspondence to Xinsheng Gu or Dong Huang.

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Sun, M., Wu, D., Zhou, K. et al. An eight-lncRNA signature predicts survival of breast cancer patients: a comprehensive study based on weighted gene co-expression network analysis and competing endogenous RNA network. Breast Cancer Res Treat 175, 59–75 (2019). https://doi.org/10.1007/s10549-019-05147-6

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