Advertisement

Breast Cancer Research and Treatment

, Volume 160, Issue 3, pp 439–446 | Cite as

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

  • András Lánczky
  • Ádám Nagy
  • Giulia Bottai
  • Gyöngyi Munkácsy
  • András Szabó
  • Libero Santarpia
  • Balázs Győrffy
Preclinical study

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.

Keywords

Breast cancer Biomarkers MicroRNAs Gene expression Prognosis Survival 

Notes

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.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Supplementary material

10549_2016_4013_MOESM1_ESM.pdf (131 kb)
Online Resource 1 (PDF 132 kb)
10549_2016_4013_MOESM2_ESM.pdf (271 kb)
Online Resource 2 (PDF 271 kb)
10549_2016_4013_MOESM3_ESM.pdf (430 kb)
Online Resource 3 (PDF 430 kb)
10549_2016_4013_MOESM4_ESM.pdf (269 kb)
Online Resource 4 (PDF 270 kb)

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 CrossRefPubMedGoogle 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 CrossRefPubMedPubMedCentralGoogle 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 CrossRefPubMedGoogle 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 CrossRefPubMedPubMedCentralGoogle 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 CrossRefPubMedPubMedCentralGoogle 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 CrossRefPubMedGoogle 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 CrossRefPubMedGoogle 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 CrossRefPubMedPubMedCentralGoogle 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 CrossRefPubMedPubMedCentralGoogle 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 CrossRefPubMedPubMedCentralGoogle Scholar
  11. 11.
    Cancer Genome Atlas Network (2012) Comprehensive molecular portraits of human breast tumours. Nature 490:61–70. doi: 10.1038/nature11412 CrossRefGoogle 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 CrossRefPubMedGoogle 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 CrossRefGoogle 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 CrossRefPubMedPubMedCentralGoogle 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 CrossRefPubMedGoogle 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 CrossRefPubMedGoogle 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 CrossRefPubMedGoogle 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 CrossRefPubMedPubMedCentralGoogle 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 CrossRefPubMedPubMedCentralGoogle 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 CrossRefPubMedPubMedCentralGoogle 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 CrossRefPubMedPubMedCentralGoogle 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 CrossRefPubMedGoogle 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 PubMedPubMedCentralGoogle 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 CrossRefPubMedPubMedCentralGoogle 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 CrossRefPubMedGoogle 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 CrossRefPubMedGoogle 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 PubMedPubMedCentralGoogle 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 CrossRefPubMedPubMedCentralGoogle Scholar
  29. 29.
    Gyorffy B, Gyorffy A, Tulassay Z (2005) The problem of multiple testing and solutions for genome-wide studies. Orv Hetil 146:559–563PubMedGoogle 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 CrossRefPubMedGoogle 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 CrossRefGoogle 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 CrossRefPubMedGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • András Lánczky
    • 1
  • Ádám Nagy
    • 1
    • 2
  • Giulia Bottai
    • 3
  • Gyöngyi Munkácsy
    • 1
    • 4
  • András Szabó
    • 2
  • Libero Santarpia
    • 3
  • Balázs Győrffy
    • 1
    • 2
  1. 1.MTA TTK Lendület Cancer Biomarker Research GroupBudapestHungary
  2. 2.Department of PediatricsSemmelweis UniversityBudapestHungary
  3. 3.Oncology Experimental Therapeutics UnitHumanitas Clinical and Research InstituteRozzano-MilanItaly
  4. 4.MTA-SE Pediatrics and Nephrology Research GroupBudapestHungary

Personalised recommendations