miRpower: a web-tool to validate survival-associated miRNAs utilizing expression data from 2178 breast cancer patients

Abstract

Purpose

The proper validation of prognostic biomarkers is an important clinical issue in breast cancer research. MicroRNAs (miRNAs) have emerged as a new class of promising breast cancer biomarkers. In the present work, we developed an integrated online bioinformatic tool to validate the prognostic relevance of miRNAs in breast cancer.

Methods

A database was set up by searching the GEO, EGA, TCGA, and PubMed repositories to identify datasets with published miRNA expression and clinical data. Kaplan–Meier survival analysis was performed to validate the prognostic value of a set of 41 previously published survival-associated miRNAs.

Results

All together 2178 samples from four independent datasets were integrated into the system including the expression of 1052 distinct human miRNAs. In addition, the web-tool allows for the selection of patients, which can be filtered by receptors status, lymph node involvement, histological grade, and treatments. The complete analysis tool can be accessed online at: www.kmplot.com/mirpower. We used this tool to analyze a large number of deregulated miRNAs associated with breast cancer features and outcome, and confirmed the prognostic value of 26 miRNAs. A significant correlation in three out of four datasets was validated only for miR-29c and miR-101.

Conclusions

In summary, we established an integrated platform capable to mine all available miRNA data to perform a survival analysis for the identification and validation of prognostic miRNA markers in breast cancer.

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References

  1. 1.

    Ferlay J, Soerjomataram I, Dikshit R, Eser S, Mathers C, Rebelo M, Parkin DM, Forman D, Bray F (2015) Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012. Int J Cancer 136:E359–E386. doi:10.1002/ijc.29210

    CAS  Article  PubMed  Google Scholar 

  2. 2.

    Goldhirsch A, Winer EP, Coates AS, Gelber RD, Piccart-Gebhart M, Thürlimann B, Senn HJ (2013) Personalizing the treatment of women with early breast cancer: highlights of the St Gallen International Expert Consensus on the Primary Therapy of Early Breast Cancer 2013. Ann Oncol 24:2206–2223. doi:10.1093/annonc/mdt303

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  3. 3.

    Dowsett M, Dunbier AK (2008) Emerging biomarkers and new understanding of traditional markers in personalized therapy for breast cancer. Clin Cancer Res 14:8019–8026. doi:10.1158/1078-0432.CCR-08-0974

    CAS  Article  PubMed  Google Scholar 

  4. 4.

    Iorio MV, Croce CM (2012) MicroRNA dysregulation in cancer: diagnostics, monitoring and therapeutics. A comprehensive review. EMBO Mol Med 4:143–159. doi:10.1002/emmm.201100209

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  5. 5.

    Blenkiron C, Goldstein LD, Thorne NP, Spiteri I, Chin SF, Dunning MJ, Barbosa-Morais NL, Teschendorff AE, Green AR, Ellis IO, Tavaré S, Caldas C, Miska EA (2007) MicroRNA expression profiling of human breast cancer identifies new markers of tumor subtype. Genome Biol 8:R214. doi:10.1186/gb-2007-8-10-r214

    Article  PubMed  PubMed Central  Google Scholar 

  6. 6.

    Iorio MV, Ferracin M, Liu CG, Veronese A, Spizzo R, Sabbioni S, Magri E, Pedriali M, Fabbri M, Campiglio M, Ménard S, Palazzo JP, Rosenberg A, Musiani P, Volinia S, Nenci I, Calin GA, Querzoli P, Negrini M, Croce CM (2005) MicroRNA gene expression deregulation in human breast cancer. Cancer Res 65:7065–7070. doi:10.1158/0008-5472.CAN-05-1783

    CAS  Article  PubMed  Google Scholar 

  7. 7.

    Buffa FM, Camps C, Winchester L, Snell CE, Gee HE, Sheldon H, Taylor M, Harris AL, Ragoussis J (2011) microRNA-associated progression pathways and potential therapeutic targets identified by integrated mRNA and microRNA expression profiling in breast cancer. Cancer Res 71:5635–5645. doi:10.1158/0008-5472.CAN-11-0489

    CAS  Article  PubMed  Google Scholar 

  8. 8.

    Volinia S, Croce CM (2013) Prognostic microRNA/mRNA signature from the integrated analysis of patients with invasive breast cancer. Proc Natl Acad Sci USA 110:7413–7417. doi:10.1073/pnas.1304977110

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  9. 9.

    van Schooneveld E, Wildiers H, Vergote I, Vermeulen PB, Dirix LY, Van Laere SJ (2015) Dysregulation of microRNAs in breast cancer and their potential role as prognostic and predictive biomarkers in patient management. Breast Cancer Res 17:21. doi:10.1186/s13058-015-0526-y

    Article  PubMed  PubMed Central  Google Scholar 

  10. 10.

    Bertoli G, Cava C, Castiglioni I (2015) MicroRNAs: new biomarkers for diagnosis, prognosis, therapy prediction and therapeutic tools for breast cancer. Theranostics 5:1122–1143. doi:10.7150/thno.11543

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  11. 11.

    Cancer Genome Atlas Network (2012) Comprehensive molecular portraits of human breast tumours. Nature 490:61–70. doi:10.1038/nature11412

    Article  Google Scholar 

  12. 12.

    Dvinge H, Git A, Gräf S, Salmon-Divon M, Curtis C, Sottoriva A, Zhao Y, Hirst M, Armisen J, Miska EA, Chin SF, Provenzano E, Turashvili G, Green A, Ellis I, Aparicio S, Caldas C (2013) The shaping and functional consequences of the microRNA landscape in breast cancer. Nature 497:378–382. doi:10.1038/nature12108

    CAS  Article  PubMed  Google Scholar 

  13. 13.

    de Rinaldis E, Gazinska P, Mera A, Modrusan Z, Fedorowicz GM, Burford B, Gillett C, Marra P, Grigoriadis A, Dornan D, Holmberg L, Pinder S, Tutt A (2013) Integrated genomic analysis of triple-negative breast cancers reveals novel microRNAs associated with clinical and molecular phenotypes and sheds light on the pathways they control. BMC Genom 23(14):643. doi:10.1186/1471-2164-14-643

    Article  Google Scholar 

  14. 14.

    Enerly E, Steinfeld I, Kleivi K, Leivonen SK, Aure MR, Russnes HG, Rønneberg JA, Johnsen H, Navon R, Rødland E, Mäkelä R, Naume B, Perälä M, Kallioniemi O, Kristensen VN, Yakhini Z, Børresen-Dale AL (2011) miRNA-mRNA integrated analysis reveals roles for miRNAs in primary breast tumors. PLoS One 6:e16915. doi:10.1371/journal.pone.0016915

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  15. 15.

    Santarpia L, Bottai G, Kelly CM, Győrffy B, Székely B, Pusztai L (2016) Deciphering and targeting oncogenic mutations and pathways in breast cancer. Oncologist 21:1063–1078. doi:10.1634/theoncologist.2015-0369

    Article  PubMed  Google Scholar 

  16. 16.

    Györffy B, Lanczky A, Eklund AC, Denkert C, Budczies J, Li Q, Szallasi Z (2010) An online survival analysis tool to rapidly assess the effect of 22,277 genes on breast cancer prognosis using microarray data of 1809 patients. Breast Cancer Res Treat 123:725–731. doi:10.1007/s10549-009-0674-9

    Article  PubMed  Google Scholar 

  17. 17.

    Gyorffy B, Lánczky A, Szállási Z (2012) Implementing an online tool for genome-wide validation of survival-associated biomarkers in ovarian-cancer using microarray data from 1287 patients. Endocr Relat Cancer 19:197–208. doi:10.1530/ERC-11-0329

    CAS  Article  PubMed  Google Scholar 

  18. 18.

    Győrffy B, Surowiak P, Budczies J, Lánczky A (2013) Online survival analysis software to assess the prognostic value of biomarkers using transcriptomic data in non-small-cell lung cancer. PLoS One 8:e82241. doi:10.1371/journal.pone.0082241

    Article  PubMed  PubMed Central  Google Scholar 

  19. 19.

    Okada Y, Muramatsu T, Suita N, Kanai M, Kawakami E, Iotchkova V, Soranzo N, Inazawa J, Tanaka T (2016) Significant impact of miRNA-target gene networks on genetics of human complex traits. Sci Rep 6:22223. doi:10.1038/srep22223

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  20. 20.

    Chen X, Yan CC, Zhang X, You ZH, Deng L, Liu Y, Zhang Y, Dai Q (2016) WBSMDA: within and between score for MiRNA-disease association prediction. Sci Rep 6:21106. doi:10.1038/srep21106

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  21. 21.

    Meng F, Wang J, Dai E, Yang F, Chen X, Wang S, Yu X, Liu D, Jiang W (2016) Psmir: a database of potential associations between small molecules and miRNAs. Sci Rep 6:19264. doi:10.1038/srep19264

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  22. 22.

    Kleivi Sahlberg K, Bottai G, Naume B, Burwinkel B, Calin GA, Børresen-Dale AL, Santarpia L (2015) A serum microRNA signature predicts tumor relapse and survival in triple-negative breast cancer patients. Clin Cancer Res 21:1207–1214. doi:10.1158/1078-0432.CCR-14-2011

    CAS  Article  PubMed  Google Scholar 

  23. 23.

    De Mattos-Arruda L, Bottai G, Nuciforo PG, Di Tommaso L, Giovannetti E, Peg V, Losurdo A, Pérez-Garcia J, Masci G, Corsi F, Cortés J, Seoane J, Calin GA, Santarpia L (2015) MicroRNA-21 links epithelial-to-mesenchymal transition and inflammatory signals to confer resistance to neoadjuvant trastuzumab and chemotherapy in HER2-positive breast cancer patients. Oncotarget 6:37269–37280. doi:10.18632/oncotarget.5495

    PubMed  PubMed Central  Google Scholar 

  24. 24.

    Parrella P, Barbano R, Pasculli B, Fontana A, Copetti M, Valori VM, Poeta ML, Perrone G, Righi D, Castelvetere M, Coco M, Balsamo T, Morritti M, Pellegrini F, Onetti-Muda A, Maiello E, Murgo R, Fazio VM (2014) Evaluation of microRNA-10b prognostic significance in a prospective cohort of breast cancer patients. Mol Cancer 13:142. doi:10.1186/1476-4598-13-142

    Article  PubMed  PubMed Central  Google Scholar 

  25. 25.

    Chen B, Tang H, Liu X, Liu P, Yang L, Xie X, Ye F, Song C, Xie X, Wei W (2015) miR-22 as a prognostic factor targets glucose transporter protein type 1 in breast cancer. Cancer Lett 356:410–417. doi:10.1016/j.canlet.2014.09.028

    CAS  Article  PubMed  Google Scholar 

  26. 26.

    Gee HE, Camps C, Buffa FM, Colella S, Sheldon H, Gleadle JM, Ragoussis J, Harris AL (2008) MicroRNA-10b and breast cancer metastasis. Nature 455:E8–E9. doi:10.1038/nature07362

    CAS  Article  PubMed  Google Scholar 

  27. 27.

    Pandey AK, Zhang Y, Zhang S, Li Y, Tucker-Kellogg G, Yang H, Jha S (2015) TIP60-miR-22 axis as a prognostic marker of breast cancer progression. Oncotarget 6:41290–41306. doi:10.18632/oncotarget.5636

    PubMed  PubMed Central  Google Scholar 

  28. 28.

    Song SJ, Poliseno L, Song MS, Ala U, Webster K, Ng C, Beringer G, Brikbak NJ, Yuan X, Cantley LC, Richardson AL, Pandolfi PP (2013) MicroRNA-antagonism regulates breast cancer stemness and metastasis via TET-family-dependent chromatin remodeling. Cell 154:311–324. doi:10.1016/j.cell.2013.06.026

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  29. 29.

    Gyorffy B, Gyorffy A, Tulassay Z (2005) The problem of multiple testing and solutions for genome-wide studies. Orv Hetil 146:559–563

    PubMed  Google Scholar 

  30. 30.

    Antonov AV, Knight RA, Melino G, Barlev NA, Tsvetkov PO (2013) MIRUMIR: an online tool to test microRNAs as biomarkers to predict survival in cancer using multiple clinical data sets. Cell Death Differ 20:367. doi:10.1038/cdd.2012.137

    CAS  Article  PubMed  Google Scholar 

  31. 31.

    Goswami CP, Nakshatri H (2012) PROGmiR: a tool for identifying prognostic miRNA biomarkers in multiple cancers using publicly available data. J Clin Bioinform 2:23. doi:10.1186/2043-9113-2-23

    CAS  Article  Google Scholar 

  32. 32.

    Aguirre-Gamboa R, Trevino V (2014) SurvMicro: assessment of miRNA-based prognostic signatures for cancer clinical outcomes by multivariate survival analysis. Bioinformatics 30:1630–1632. doi:10.1093/bioinformatics/btu087

    CAS  Article  PubMed  Google Scholar 

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Acknowledgments

This study was supported by the Hungarian Scientific Research Fund (OTKA) K 108655 Grant (to B.G.), Associazione Italiana Ricerca sul Cancro (Grant 6251 to L.S.), and Fondazione Italiana Ricerca sul Cancro (FIRC fellowship 18328 to G.B.). The authors are grateful to Laura Paladini for her cooperation in data collection.

Author Contributions

B.G. and L.S. conceived, designed, and supervised the study. B.G., A.L., A.N., and L.S. performed the analysis. G.B., B.G., G.M., and L.S. reviewed the literature. G.B., B.G., A.L., A.N., L.S., and A.S. participated in data interpretation. All authors were involved in writing and reviewing the manuscript, and approved the final manuscript.

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Correspondence to Libero Santarpia or Balázs Győrffy.

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Lánczky, A., Nagy, Á., Bottai, G. et al. miRpower: a web-tool to validate survival-associated miRNAs utilizing expression data from 2178 breast cancer patients. Breast Cancer Res Treat 160, 439–446 (2016). https://doi.org/10.1007/s10549-016-4013-7

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Keywords

  • Breast cancer
  • Biomarkers
  • MicroRNAs
  • Gene expression
  • Prognosis
  • Survival