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


Journal

Annals of surgical oncology
ISSN: 1534-4681
Titre abrégé: Ann Surg Oncol
Pays: United States
ID NLM: 9420840

Informations de publication

Date de publication:
Oct 2019
Historique:
received: 10 01 2019
pubmed: 5 7 2019
medline: 13 2 2020
entrez: 5 7 2019
Statut: ppublish

Résumé

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.

Sections du résumé

BACKGROUND 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 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 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 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.

Identifiants

pubmed: 31270716
doi: 10.1245/s10434-019-07578-3
pii: 10.1245/s10434-019-07578-3
doi:

Substances chimiques

Biomarkers, Tumor 0
MicroRNAs 0

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

3765-3773

Auteurs

Joana Heinzelmann (J)

Department of Urology and Pediatric Urology, Saarland University, Homburg, Saar, Germany.
Department of Ophthalmology, Martin-Luther University Halle-Wittenberg, University Hospital Halle (Saale), Halle (Saale), Germany.

Madeleine Arndt (M)

Department of Urology and Pediatric Urology, Saarland University, Homburg, Saar, Germany.

Ramona Pleyers (R)

Department of Urology and Pediatric Urology, Saarland University, Homburg, Saar, Germany.

Tobias Fehlmann (T)

Department of Clinical Bioinformatics, Saarland University, Saarbruecken, Germany.

Sebastian Hoelters (S)

Department of Urology and Pediatric Urology, Saarland University, Homburg, Saar, Germany.
SERVA Electrophoresis GmbH, Heidelberg, Germany.

Philip Zeuschner (P)

Department of Urology and Pediatric Urology, Saarland University, Homburg, Saar, Germany.

Alexander Vogt (A)

Department of Urology and Pediatric Urology, Saarland University, Homburg, Saar, Germany.

Alexey Pryalukhin (A)

Institute of Pathology, Saarland University, Homburg, Saar, Germany.
Institute of Pathology, Bonn University Medical School, Bonn, Germany.

Elke Schaeffeler (E)

Dr. Margarete Fischer-Bosch Institute of Clinical Pharmacology, Stuttgart, Germany.
University of Tuebingen, Tuebingen, Germany.

Rainer M Bohle (RM)

Institute of Pathology, Saarland University, Homburg, Saar, Germany.

Mieczyslaw Gajda (M)

Institute of Pathology, Jena University Hospital, Jena, Germany.

Martin Janssen (M)

Department of Urology and Pediatric Urology, Saarland University, Homburg, Saar, Germany.

Michael Stoeckle (M)

Department of Urology and Pediatric Urology, Saarland University, Homburg, Saar, Germany.

Kerstin Junker (K)

Department of Urology and Pediatric Urology, Saarland University, Homburg, Saar, Germany. kerstin.junker@uks.eu.
Department of Urology, Jena University Hospital, Jena, Germany. kerstin.junker@uks.eu.

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Classifications MeSH