Research article

High-throughput miRNA profiling of human melanoma blood samples

Petra Leidinger^1†, Andreas Keller^2,3†, Anne Borries², Jörg Reichrath⁴, Knuth Rass⁴, Sven U Jager⁵, Hans-Peter Lenhof⁶ and Eckart Meese^1*

• * Corresponding author: Eckart Meese hgemee@uniklinik-saarland.de

• † Equal contributors

Author Affiliations

¹ Institute of Human Genetics, Medical School, Saarland University, Building 60, 66421 Homburg/Saar, Germany

² Febit biomed gmbh, Im Neuenheimer Feld 519, 69120 Heidelberg, Germany

³ Biomarker Discovery Center Heidelberg, 69120 Heidelberg, Germany

⁴ Clinic for Dermatology, Venerology and Allergology, Medical School, Saarland University, 66421 Homburg/Saar, Germany

⁵ Praxis für Dermatologie, 66280 Sulzbach/Saar, Germany

⁶ Center for Bioinformatics, Saarland University, Building E 1 1, 66041Saarbruecken, Germany

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BMC Cancer 2010, 10:262 doi:10.1186/1471-2407-10-262

Published: 7 June 2010

Abstract

Background

MicroRNA (miRNA) signatures are not only found in cancer tissue but also in blood of cancer patients. Specifically, miRNA detection in blood offers the prospect of a non-invasive analysis tool.

Methods

Using a microarray based approach we screened almost 900 human miRNAs to detect miRNAs that are deregulated in their expression in blood cells of melanoma patients. We analyzed 55 blood samples, including 20 samples of healthy individuals, 24 samples of melanoma patients as test set, and 11 samples of melanoma patients as independent validation set.

Results

A hypothesis test based approch detected 51 differentially regulated miRNAs, including 21 miRNAs that were downregulated in blood cells of melanoma patients and 30 miRNAs that were upregulated in blood cells of melanoma patients as compared to blood cells of healthy controls. The tets set and the independent validation set of the melanoma samples showed a high correlation of fold changes (0.81). Applying hierarchical clustering and principal component analysis we found that blood samples of melanoma patients and healthy individuals can be well differentiated from each other based on miRNA expression analysis. Using a subset of 16 significant deregulated miRNAs, we were able to reach a classification accuracy of 97.4%, a specificity of 95% and a sensitivity of 98.9% by supervised analysis. MiRNA microarray data were validated by qRT-PCR.

Conclusions

Our study provides strong evidence for miRNA expression signatures of blood cells as useful biomarkers for melanoma.