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Identification of genes associated with survival of breast cancer patients

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Abstract

Background

We aimed to investigate the potential of microRNA expression profiles to predict survival in breast cancer.

Methods

MicroRNA and mRNA expression data of breast cancer were downloaded from The Cancer Genome Atlas. LASSO regression was used to identify microRNAs signature predicting survival of breast cancer patients. Transfection experiment was conducted to explore the influence of microRNAs on their potential targets.

Results

We identified 56 differentially expressed microRNAs in breast cancer tissues compared to adjacent normal tissues. 10 microRNAs with non-zero coefficient were selected from the 56 microRNAs using LASSO Cox regression. After predicting the targets for the 10 microRNAs, we further obtained 155 targets that were associated with overall survival of breast cancer patients. Spearman’s correlation analysis found that the expression of SCUBE2, SCRN3, YTHDF3, ITFG1, ITPRIPL2, and JAK1 was an inversely correlated with their microRNAs. Transfection experiment showed that YTHDF3 was down-regulated in cells transfected with miR-106b-5p mimics compared with those transfected with negative control of mimics (fold change 4.21; P < 0.01).

Conclusions

In conclusion, we identified a 10-miRNA signature associated with prognosis of breast cancer patients. The expression of YTHDF3 was down-regulated by miR-106b-5p.

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References

  1. Siegel RL, Miller KD, Jemal A. Cancer statistics, 2017. CA Cancer J Clin. 2017; 67:7–30

    Article  PubMed  Google Scholar 

  2. Esbah O, Oksuzoglu B. Prognostic & predictive factors for planning adjuvant chemotherapy of early-stage breast cancer. Indian J Med Res. 2017;146:563–71.

    CAS  PubMed  PubMed Central  Google Scholar 

  3. Nicolini A, Ferrari P, Duffy MJ. Prognostic and predictive biomarkers in breast cancer: past, present and future. Semin Cancer Biol. 2018;52:56–73.

    Article  CAS  PubMed  Google Scholar 

  4. He L, Hannon GJ. MicroRNAs: small RNAs with a big role in gene regulation. Nat Rev Genet. 2004;5:522–31.

    Article  CAS  PubMed  Google Scholar 

  5. Mandujano-Tinoco EA, Garcia-Venzor A, Melendez-Zajgla J, Maldonado V. New emerging roles of microRNAs in breast cancer. Breast Cancer Res Treat. 2018;171:247–59

    Article  CAS  PubMed  Google Scholar 

  6. Teoh SL, Das S. The role of MicroRNAs in diagnosis, prognosis, metastasis and resistant cases in breast cancer. Curr Pharm Des. 2017;23:1845–59.

    Article  CAS  PubMed  Google Scholar 

  7. Tang Y, Zhou X, Ji J, Chen L, Cao J, Luo J, et al. High expression levels of miR-21 and miR-210 predict unfavorable survival in breast cancer: a systemic review and meta-analysis. Int J Biol Mark. 2015;30:e347-58.

    Google Scholar 

  8. Kim SY, Kawaguchi T, Yan L, Young J, Qi Q, Takabe K. Clinical relevance of microRNA expressions in breast cancer validated using The Cancer Genome Atlas (TCGA). Ann Surg Oncol. 2017;24:2943–9.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Tibshirani R. Regression shrinkage and selection via the lasso. J R Stat Soc Ser B (Methodol). 1996;58:267–88.

    Google Scholar 

  10. Tomczak K, Czerwinska P, Wiznerowicz M. The Cancer Genome Atlas (TCGA): an immeasurable source of knowledge. Contemp Oncol (Pozn). 2015;19:A68–77.

    Google Scholar 

  11. Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15:550.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Benjamini Y, Drai D, Elmer G, Kafkafi N, Golani I. Controlling the false discovery rate in behavior genetics research. Behav Brain Res. 2001;125:279–84.

    Article  CAS  PubMed  Google Scholar 

  13. Alencar AJ, Malumbres R, Kozloski GA, Advani R, Talreja N, Chinichian S, et al. MicroRNAs are independent predictors of outcome in diffuse large B-cell lymphoma patients treated with R-CHOP. Clin Cancer Res. 2011;17:4125–35.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Agarwal V, Bell GW, Nam JW, Bartel DP. Predicting effective microRNA target sites in mammalian mRNAs. Elife. 2015;4:e05005

    Article  PubMed Central  Google Scholar 

  15. Wong N, Wang X. miRDB: an online resource for microRNA target prediction and functional annotations. Nucleic Acids Res. 2015;43:D146-52.

    PubMed  Google Scholar 

  16. Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 2003;13:2498–504.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Zhong S, Wang J, Hou J, Zhang Q, Xu H, Hu J, et al. Circular RNA hsa_circ_0000993 inhibits metastasis of gastric cancer cells. Epigenomics. 2018;10:1301–13.

    Article  CAS  PubMed  Google Scholar 

  18. Zhang JX, Song W, Chen ZH, Wei JH, Liao YJ, Lei J, et al. Prognostic and predictive value of a microRNA signature in stage II colon cancer: a microRNA expression analysis. Lancet Oncol. 2013;14:1295–306.

    Article  CAS  PubMed  Google Scholar 

  19. Hayes J, Thygesen H, Tumilson C, Droop A, Boissinot M, Hughes TA, et al. Prediction of clinical outcome in glioblastoma using a biologically relevant nine-microRNA signature. Mol Oncol. 2015;9:704–14.

    Article  CAS  PubMed  Google Scholar 

  20. Feng YH, Tsao CJ. Emerging role of microRNA-21 in cancer. Biomed Rep. 2016;5:395–402.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Barbano R, Pasculli B, Rendina M, Fontana A, Fusilli C, Copetti M, et al. Stepwise analysis of MIR9 loci identifies miR-9-5p to be involved in Oestrogen regulated pathways in breast cancer patients. Sci Rep. 2017;7:45283.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Jiang CF, Shi ZM, Li DM, Qian YC, Ren Y, Bai XM, et al. Estrogen-induced miR-196a elevation promotes tumor growth and metastasis via targeting SPRED1 in breast cancer. Mol Cancer. 2018;17:83.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Yang Z, Han Y, Cheng K, Zhang G, Wang X. miR-99a directly targets the mTOR signalling pathway in breast cancer side population cells. Cell Prolif. 2014;47:587–95.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Hu Y, Zhu Q, Tang L. MiR-99a antitumor activity in human breast cancer cells through targeting of mTOR expression. PLoS One. 2014;9:e92099.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Ni X, Xia T, Zhao Y, Zhou W, Wu N, Liu X, et al. Downregulation of miR-106b induced breast cancer cell invasion and motility in association with overexpression of matrix metalloproteinase 2. Cancer Sci. 2014;105:18–25.

    Article  CAS  PubMed  Google Scholar 

  26. Lin YC, Chen CC, Cheng CJ, Yang RB. Domain and functional analysis of a novel breast tumor suppressor protein, SCUBE2. J Biol Chem. 2011;286:27039–47.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Lin YC, Lee YC, Li LH, Cheng CJ, Yang RB. Tumor suppressor SCUBE2 inhibits breast-cancer cell migration and invasion through the reversal of epithelial-mesenchymal transition. J Cell Sci. 2014;127:85–100.

    Article  PubMed  Google Scholar 

  28. Cheng CJ, Lin YC, Tsai MT, Chen CS, Hsieh MC, Chen CL, et al. SCUBE2 suppresses breast tumor cell proliferation and confers a favorable prognosis in invasive breast cancer. Cancer Res. 2009;69:3634–41.

    Article  CAS  PubMed  Google Scholar 

  29. Fan W, Xie J, Xia J, Zhang Y, Yang M, Wang H, et al. RUVBL1-ITFG1 interaction is required for collective invasion in breast cancer. Biochim Biophys Acta. 2017;1861:1788–800.

    Article  CAS  Google Scholar 

  30. Quintas-Cardama A, Verstovsek S. Molecular pathways: Jak/STAT pathway: mutations, inhibitors, and resistance. Clin Cancer Res. 2013;19:1933–40.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Lee HJ, Seo NJ, Jeong SJ, Park Y, Jung DB, Koh W, et al. Oral administration of penta-O-galloyl-beta-d-glucose suppresses triple-negative breast cancer xenograft growth and metastasis in strong association with JAK1-STAT3 inhibition. Carcinogenesis. 2011;32:804–11.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Kaushik N, Kim MJ, Kim RK, Kumar Kaushik N, Seong KM, Nam SY, et al. Low-dose radiation decreases tumor progression via the inhibition of the JAK1/STAT3 signaling axis in breast cancer cell lines. Sci Rep. 2017;7:43361.

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Funding

This study was funded by the National Natural Science Foundation of China (Grant number 81602551) and the young talents program of Jiangsu Cancer Hospital (2017YQL-10).

Author information

Author notes

  1. Min Liu and Siying Zhou are equal contributors.

Authors and Affiliations

  1. Department of Clinical Laboratory, Jiangsu Cancer Hospital & Jiangsu Institute of Cancer Research & The Affiliated Cancer Hospital of Nanjing Medical University, Nanjing, 210009, China

    Min Liu

  2. The First Clinical Medical College, Nanjing University of Chinese Medicine, Nanjing, 210023, China

    Siying Zhou

  3. Department of General Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029, China

    Jinyan Wang, Qian Zhang & Sujin Yang

  4. Department of Medical Oncology, Jiangsu Cancer Hospital & Jiangsu Institute of Cancer Research & The Affiliated Cancer Hospital of Nanjing Medical University, Nanjing, 210009, China

    Jifeng Feng

  5. Center of Clinical Laboratory Science, Jiangsu Cancer Hospital & Jiangsu Institute of Cancer Research & The Affiliated Cancer Hospital of Nanjing Medical University, Baiziting 42, Nanjing, 210009, China

    Bin Xu & Shanliang Zhong

Authors
  1. Min Liu
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  2. Siying Zhou
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  7. Bin Xu
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Corresponding author

Correspondence to Shanliang Zhong.

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Conflict of interest

The authors declare that they have no competing interest.

Electronic supplementary material

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12282_2018_926_MOESM1_ESM.tif

Figure S1 Overall survival analysis of breast cancer patients with high-expression genes and low-expression genes. The number following “=” is the median survival time for each group (509 patients for each group) (TIF 561 KB)

Figure S2 MiR-9-5p and miR-106b-5p binding sites in the six candidate target genes (TIF 323 KB)

Supplementary material 3 (DOCX 14 KB)

Supplementary material 4 (XLSX 14 KB)

Supplementary material 5 (XLSX 44 KB)

Supplementary material 6 (XLSX 22 KB)

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Liu, M., Zhou, S., Wang, J. et al. Identification of genes associated with survival of breast cancer patients. Breast Cancer 26, 317–325 (2019). https://doi.org/10.1007/s12282-018-0926-9

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  • DOI: https://doi.org/10.1007/s12282-018-0926-9

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