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J Invest Dermatol. 2016 Jan;136(1):245-254. doi: 10.1038/JID.2015.355.

Identification, Review, and Systematic Cross-Validation of microRNA Prognostic Signatures in Metastatic Melanoma.

Author information

1
School of Mathematics and Statistics, The University of Sydney, Sydney, NSW, Australia.
2
The Westmead Millennium Institute for Medical Research, The University of Sydney, Sydney, NSW, Australia; Melanoma Institute Australia, Sydney, NSW, Australia. Electronic address: sarah-jane.schramm@sydney.edu.au.
3
The Westmead Millennium Institute for Medical Research, The University of Sydney, Sydney, NSW, Australia; Melanoma Institute Australia, Sydney, NSW, Australia.
4
Melanoma Institute Australia, Sydney, NSW, Australia; Discipline of Surgery, Sydney Medical School, The University of Sydney, Sydney, NSW, Australia.
5
Melanoma Institute Australia, Sydney, NSW, Australia; Discipline of Pathology, Sydney Medical School, The University of Sydney, Sydney, NSW, Australia; Tissue Pathology and Diagnostic Oncology, Royal Prince Alfred Hospital, Sydney, NSW, Australia.

Abstract

In metastatic melanoma, it is vital to identify and validate biomarkers of prognosis. Previous studies have systematically evaluated protein biomarkers or mRNA-based expression signatures. No such analyses have been applied to microRNA (miRNA)-based prognostic signatures. As a first step, we identified two prognostic miRNA signatures from publicly available data sets (Gene Expression Omnibus/The Cancer Genome Atlas) of global miRNA expression profiling information. A 12-miRNA signature predicted longer survival after surgery for resection of American Joint Committee on Cancer stage III disease (>4 years, no sign of relapse) and outperformed American Joint Committee on Cancer standard-of-care prognostic markers in leave-one-out cross-validation analysis (error rates 34% and 38%, respectively). A similar 15-miRNA biomarker derived from The Cancer Genome Atlas miRNA-seq data performed slightly worse (39%) than these current biomarkers. Both signatures were then assessed for replication in two independent data sets and subjected to systematic cross-validation together with the three other miRNA-based prognostic signatures proposed in the literature to date. Five miRNAs (miR-142-5p, miR-150-5p, miR-342-3p, miR-155-5p, and miR-146b-5p) were reproducibly associated with patient outcome and have the greatest potential for application in the clinic. Our extensive validation approach highlighted among multiple independent cohorts the translational potential and limitations of miRNA signatures, and pointed to future directions in the analysis of this emerging class of markers.

PMID:
26763444
DOI:
10.1038/JID.2015.355
[Indexed for MEDLINE]
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