Annals of Surgical Oncology

, Volume 26, Issue 11, pp 3765–3773 | Cite as

4-miRNA Score Predicts the Individual Metastatic Risk of Renal Cell Carcinoma Patients

  • Joana Heinzelmann
  • Madeleine Arndt
  • Ramona Pleyers
  • Tobias Fehlmann
  • Sebastian Hoelters
  • Philip Zeuschner
  • Alexander Vogt
  • Alexey Pryalukhin
  • Elke Schaeffeler
  • Rainer M. Bohle
  • Mieczyslaw Gajda
  • Martin Janssen
  • Michael Stoeckle
  • Kerstin JunkerEmail author
Urologic Oncology



In order to improve individual prognostication as well as stratification for adjuvant therapy in patients with clinically localized clear cell renal cell carcinoma (ccRCC), reliable prognostic biomarkers are urgently needed. In this study, microRNAs (miRNAs) have emerged as promising candidates. We investigated whether a combination of differently expressed miRNAs in primary tumors can predict the individual metastatic risk.


Using two prospectively collected biobanks of academic centers, 108 ccRCCs were selected, including 57 from patients with metastatic disease at diagnosis or during follow-up and 51 without evidence of metastases. Fourteen previously identified candidate miRNAs were tested in 20 representative formalin-fixed and paraffin embedded samples in order to select the best discriminators between metastatic and nonmetastatic ccRCC. These miRNAs were approved in 108 tumor samples. We evaluated the association of altered miRNA expression with the metastatic potential of tumors using quantitative polymerase chain reaction. A prognostic 4-miRNA model has been established using a random forest classifier. Cox regression analyses were performed for correlation of the miRNA model and clinicopathological parameters to metastasis-free and overall survival.


Nine miRNAs indicated significant expression alterations in the small cohort. These miRNAs were validated in the whole cohort. The established 4-miRNA score (miR-30a-3p/-30c-5p/-139-5p/-144-5p) has been identified as a superior predictor for metastasis-free survival (hazard ratio 12.402; p = 7.0E−05) and overall survival (p = 1.1E−04) compared with clinicopathological parameters, and likewise in the Leibovich score subgroups.


We identified a 4-miRNA model that was found to be superior to clinicopathological parameters in accurately predicting individual metastatic risk and can support patient selection for risk-stratified follow-up and adjuvant therapy studies.



The authors thank Prof. Dr. Carsten Ohlmann and Dr. Johannes Linxweiler for critically reading this paper.


This study was supported by a grant from the Wilhelm-Sander-Stiftung, Germany (Grant Number 2014.0007.1), and additionally supported by the Robert Bosch Foundation.

Conflict of interest

Joana Heinzelmann, Madeleine Arndt, Ramona Pleyers, Tobias Fehlmann, Sebastian Hoelters, Philip Zeuschner, Alexander Vogt, Alexey Pryalukhin, Elke Schaeffeler, Rainer M. Bohle, Mieczyslaw Gajda, Martin Janssen, Michael Stoeckle, and Kerstin Junker have no commercial interests in the subject matter of this study.

Supplementary material

10434_2019_7578_MOESM1_ESM.docx (16 kb)
Supplementary material 1 (DOCX 15 kb)
10434_2019_7578_MOESM2_ESM.doc (30 kb)
Supplementary material 2 (DOC 30 kb)
10434_2019_7578_MOESM3_ESM.jpg (784 kb)
Supplementary material 3 (JPEG 784 kb)


  1. 1.
    Cohen HT, McGovern FJ. Renal-cell carcinoma. N Engl J Med. 2005;353:2477–90.CrossRefPubMedGoogle Scholar
  2. 2.
    Dabestani S, Thorstenson A, Lindblad P, Harmenberg U, Ljungberg B, Lundstam S. Renal cell carcinoma recurrences and metastases in primary non-metastatic patients: a population-based study. World J Urol. 2016;34:1081–1086.CrossRefPubMedGoogle Scholar
  3. 3.
    Ljungberg B, Bensalah K, Canfield S, et al. EAU guidelines on renal cell carcinoma: 2014 update. Eur Urol. 2015;67:913–24.CrossRefPubMedGoogle Scholar
  4. 4.
    Leibovich BC, Blute ML, Cheville JC, et al. Prediction of progression after radical nephrectomy for patients with clear cell renal cell carcinoma: a stratification tool for prospective clinical trials. Cancer. 2003;97:1663–1671.CrossRefPubMedGoogle Scholar
  5. 5.
    Youssef RF, Cost NG, Darwish OM, Margulis V. Prognostic markers in renal cell carcinoma: a focus on the ‘mammalian target of rapamycin’ pathway. Arab J Urol. 2012;10:110–117.CrossRefPubMedPubMedCentralGoogle Scholar
  6. 6.
    van Vlodrop IJH, Joosten SC, De Meyer T, et al. A four-gene promoter methylation marker panel consisting of GREM1, NEURL, LAD1, and NEFH predicts survival of clear cell renal cell cancer patients. Clin Cancer Res. 2017;23:2006–2018.CrossRefPubMedGoogle Scholar
  7. 7.
    Buttner F, Winter S, Rausch S, et al. Survival prediction of clear cell renal cell carcinoma based on gene expression similarity to the proximal tubule of the nephron. Eur Urol. 2015;68:1016–20.CrossRefPubMedGoogle Scholar
  8. 8.
    Rini B, Goddard A, Knezevic D, et al. A 16-gene assay to predict recurrence after surgery in localised renal cell carcinoma: development and validation studies. Lancet Oncol. 2015;16:676–85.CrossRefPubMedGoogle Scholar
  9. 9.
    Brooks SA, Brannon AR, Parker JS, et al. ClearCode34: a prognostic risk predictor for localized clear cell renal cell carcinoma. Eur Urol. 2014;66:77–84.CrossRefPubMedPubMedCentralGoogle Scholar
  10. 10.
    Morgan TM, Mehra R, Tiemeny P, et al. A multigene signature based on cell cycle proliferation improves prediction of mortality within 5 yr of radical nephrectomy for renal cell carcinoma. Eur Urol. 2018;73:763–769.CrossRefPubMedGoogle Scholar
  11. 11.
    Sanjmyatav J, Hauke S, Gajda M, et al. Establishment of a multicolour fluorescence in situ hybridisation-based assay for subtyping of renal cell tumours. Eur Urol. 2013;64:689–91.CrossRefPubMedGoogle Scholar
  12. 12.
    Youssef YM, White NMA, Grigull J, et al. Accurate molecular classification of kidney cancer subtypes using microRNA signature. Eur Urol. 2011;59:721–730.CrossRefPubMedGoogle Scholar
  13. 13.
    Cho WC. MicroRNAs: potential biomarkers for cancer diagnosis, prognosis and targets for therapy. Int J Biochem Cell Biol. 2010;42:1273–81.CrossRefPubMedGoogle Scholar
  14. 14.
    Heinzelmann J, Henning B, Sanjmyatav J, et al. Specific miRNA signatures are associated with metastasis and poor prognosis in clear cell renal cell carcinoma. World J Urol. 2011;29:367–73.CrossRefPubMedGoogle Scholar
  15. 15.
    Heinzelmann J, Unrein A, Wickmann U, et al. MicroRNAs with prognostic potential for metastasis in clear cell renal cell carcinoma: a comparison of primary tumors and distant metastases. Ann Surg Oncol. 2014;21:1046–54.CrossRefPubMedGoogle Scholar
  16. 16.
    Fu Q, Liu Z, Pan D, et al. Tumor miR-125b predicts recurrence and survival of patients with clear-cell renal cell carcinoma after surgical resection. Cancer Sci. 2014;105:1427–34.CrossRefPubMedPubMedCentralGoogle Scholar
  17. 17.
    Slaby O, Redova M, Poprach A, et al. Identification of MicroRNAs associated with early relapse after nephrectomy in renal cell carcinoma patients. Genes Chromosomes Cancer. 2012;51:707–16.CrossRefPubMedGoogle Scholar
  18. 18.
    Khella HW, Scorilas A, Mozes R, et al. Low expression of miR-126 is a prognostic marker for metastatic clear cell renal cell carcinoma. Am J Pathol. 2015;185:693–703.CrossRefPubMedGoogle Scholar
  19. 19.
    Samaan S, Khella HW, Girgis A, et al. miR-210 is a prognostic marker in clear cell renal cell carcinoma. J Mol Diagn. 2015;17:136–44.CrossRefPubMedGoogle Scholar
  20. 20.
    Fritz HK, Lindgren D, Ljungberg B, Axelson H, Dahlback B. The miR(21/10b) ratio as a prognostic marker in clear cell renal cell carcinoma. Eur J Cancer. 2014;50:1758–65.CrossRefPubMedGoogle Scholar
  21. 21.
    Faragalla H, Youssef YM, Scorilas A, et al. The clinical utility of miR-21 as a diagnostic and prognostic marker for renal cell carcinoma. J Mol Diagn. 2012;14:385–92.CrossRefPubMedGoogle Scholar
  22. 22.
    Zaman MS, Shahryari V, Deng G, et al. Up-regulation of microRNA-21 correlates with lower kidney cancer survival. PLoS ONE. 2012;7:e31060.CrossRefPubMedPubMedCentralGoogle Scholar
  23. 23.
    Teixeira AL, Ferreira M, Silva J, et al. Higher circulating expression levels of miR-221 associated with poor overall survival in renal cell carcinoma patients. Tumour Biol. 2014;35:4057–66.CrossRefPubMedGoogle Scholar
  24. 24.
    Petillo D, Kort EJ, Anema J, Furge KA, Yang XJ, Teh BT. MicroRNA profiling of human kidney cancer subtypes. Int J Oncol. 2009;35:109–14.CrossRefPubMedGoogle Scholar
  25. 25.
    Keller A, Leidinger P, Vogel B, et al. miRNAs can be generally associated with human pathologies as exemplified for miR-144. BMC Med. 2014;12:224.CrossRefPubMedPubMedCentralGoogle Scholar
  26. 26.
    Haas NB, Manola J, Uzzo RG, et al. Adjuvant sunitinib or sorafenib for high-risk, non-metastatic renal-cell carcinoma (ECOG-ACRIN E2805): a double-blind, placebo-controlled, randomised, phase 3 trial. Lancet. 2016;387:2008–16.CrossRefPubMedPubMedCentralGoogle Scholar
  27. 27.
    Ravaud A, Motzer RJ, Pandha HS, et al. Adjuvant sunitinib in high-risk renal-cell carcinoma after nephrectomy. N Engl J Med. 2016;375:2246–2254.CrossRefPubMedGoogle Scholar
  28. 28.
    Motzer RJ, Haas NB, Donskov F, et al. Randomized phase III trial of adjuvant pazopanib versus placebo after nephrectomy in patients with locally advanced renal cell carcinoma (RCC) (PROTECT). J Clin Oncol. 2017;35:3916–3923.CrossRefPubMedPubMedCentralGoogle Scholar
  29. 29.
    Wu X, Weng L, Li X, et al. Identification of a 4-microRNA signature for clear cell renal cell carcinoma metastasis and prognosis. PLoS ONE. 2012;7:e35661.CrossRefPubMedPubMedCentralGoogle Scholar
  30. 30.
    Lokeshwar SD, Talukder A, Yates TJ, et al. Molecular characterization of renal cell carcinoma: a potential three-microRNA prognostic signature. Cancer Epidemiol Biomark. 2018;27:464–472.CrossRefGoogle Scholar
  31. 31.
    Kowalik CG, Palmer DA, Sullivan TB, et al. Profiling microRNA from nephrectomy and biopsy specimens: predictors of progression and survival in clear cell renal cell carcinoma. BJU Int. 2017;120:428–440.CrossRefPubMedGoogle Scholar
  32. 32.
    Hakimi AA, Voss MH. Genomic classifiers in renal cell carcinoma. Eur Urol. 2018;73:770–771.CrossRefPubMedGoogle Scholar
  33. 33.
    Gerlinger M, Rowan AJ, Horswell S, et al. Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. N Engl J Med. 2012;366:883–892.CrossRefPubMedPubMedCentralGoogle Scholar
  34. 34.
    Huang LL, Huang LW, Wang L, Tong BD, Wei Q, Ding XS. Potential role of miR-139-5p in cancer diagnosis, prognosis and therapy. Oncol Lett. 2017;14:1215–1222.CrossRefPubMedPubMedCentralGoogle Scholar
  35. 35.
    Yamada Y, Arai T, Kojima S, et al. Regulation of antitumor miR-144-5p targets oncogenes: direct regulation of syndecan-3 and its clinical significance. Cancer Sci. 2018;109:2919–2936.CrossRefPubMedPubMedCentralGoogle Scholar
  36. 36.
    Wang D, Zhu C, Zhang Y, et al. MicroRNA-30e-3p inhibits cell invasion and migration in clear cell renal cell carcinoma by targeting Snail1. Oncol Lett. 2017;13:2053–2058.CrossRefPubMedPubMedCentralGoogle Scholar
  37. 37.
    Mathew LK, Lee SS, Skuli N, et al. Restricted expression of miR-30c-2-3p and miR-30a-3p in clear cell renal cell carcinomas enhances HIF2alpha activity. Cancer Discov. 2014;4:53–60.CrossRefPubMedGoogle Scholar

Copyright information

© Society of Surgical Oncology 2019

Authors and Affiliations

  • Joana Heinzelmann
    • 1
    • 2
  • Madeleine Arndt
    • 1
  • Ramona Pleyers
    • 1
  • Tobias Fehlmann
    • 3
  • Sebastian Hoelters
    • 1
    • 9
  • Philip Zeuschner
    • 1
  • Alexander Vogt
    • 1
  • Alexey Pryalukhin
    • 4
    • 10
  • Elke Schaeffeler
    • 5
    • 6
  • Rainer M. Bohle
    • 4
  • Mieczyslaw Gajda
    • 7
  • Martin Janssen
    • 1
  • Michael Stoeckle
    • 1
  • Kerstin Junker
    • 1
    • 8
    Email author
  1. 1.Department of Urology and Pediatric UrologySaarland UniversityHomburgGermany
  2. 2.Department of Ophthalmology, Martin-Luther University Halle-WittenbergUniversity Hospital Halle (Saale)Halle (Saale)Germany
  3. 3.Department of Clinical BioinformaticsSaarland UniversitySaarbrueckenGermany
  4. 4.Institute of PathologySaarland UniversityHomburgGermany
  5. 5.Dr. Margarete Fischer-Bosch Institute of Clinical PharmacologyStuttgartGermany
  6. 6.University of TuebingenTuebingenGermany
  7. 7.Institute of PathologyJena University HospitalJenaGermany
  8. 8.Department of UrologyJena University HospitalJenaGermany
  9. 9.SERVA Electrophoresis GmbHHeidelbergGermany
  10. 10.Institute of PathologyBonn University Medical SchoolBonnGermany

Personalised recommendations