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Discovery of circulating microRNAs associated with human prostate cancer using a mouse model of disease
Author(s) -
Selth Luke A.,
Townley Scott,
Gillis Joanna L.,
Ochnik Aleksandra M.,
Murti Krisna,
Macfarlane Robyn J.,
Chi Kim N.,
Marshall Villis R.,
Tilley Wayne D.,
Butler Lisa M.
Publication year - 2011
Publication title -
international journal of cancer
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.475
H-Index - 234
eISSN - 1097-0215
pISSN - 0020-7136
DOI - 10.1002/ijc.26405
Subject(s) - prostate cancer , tramp , microrna , prostate , disease , medicine , cancer , pca3 , cancer research , oncology , adenocarcinoma , pathology , biology , bioinformatics , gene , genetics
Circulating microRNAs (miRNAs) are emerging as useful non‐invasive markers of disease. The objective of this study was to use a mouse model of prostate cancer as a tool to discover serum miRNAs that could be assessed in a clinical setting. Global miRNA profiling identified 46 miRNAs at significantly altered levels ( p ≤ 0.05) in the serum of TRansgenic Adenocarcinoma of Mouse Prostate (TRAMP) mice with advanced prostate cancer compared to healthy controls. A subset of these miRNAs with known human homologues were validated in an independent cohort of mice and then measured in serum from men with metastatic castration‐resistant prostate cancer (mCRPC; n = 25) or healthy men ( n = 25). Four miRNAs altered in mice, mmu‐miR‐141, mmu‐miR‐298, mmu‐miR‐346 and mmu‐miR‐375, were also found to be at differential levels in the serum of men with mCRPC. Three of these (hsa‐miR‐141, hsa‐miR‐298 and hsa‐miR‐375) were upregulated in prostate tumors compared with normal prostate tissue, suggesting that they are released into the blood as disease progresses. Moreover, the intra‐tumoral expression of hsa‐miR‐141 and hsa‐miR‐375 were predictors of biochemical relapse after surgery. This study is the first to demonstrate that specific serum miRNAs are common between human prostate cancer and a mouse model of the disease, highlighting the potential of such models for the discovery of novel biomarkers.