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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
  • 38 Downloads

Abstract

Background

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.

Methods

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.

Results

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.

Conclusions

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.

Notes

Acknowledgment

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

Funding

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)

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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

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