alexa Cross-species identification of a plasma microRNA signature for detection, therapeutic monitoring, and prognosis in osteosarcoma.
Bioinformatics & Systems Biology

Bioinformatics & Systems Biology

Journal of Data Mining in Genomics & Proteomics

Author(s): AllenRhoades W, Kurenbekova L, Satterfield L, Parikh N, Fuja D,

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Abstract Osteosarcoma (OS) is the primary bone tumor in children and young adults. Currently, there are no reliable, noninvasive biologic markers to detect the presence or progression of disease, assess therapy response, or provide upfront prognostic insights. MicroRNAs (miRNAs) are evolutionarily conserved, stable, small noncoding RNA molecules that are key posttranscriptional regulators and are ideal candidates for circulating biomarker development due to their stability in plasma, ease of isolation, and the unique expressions associated with specific disease states. Using a qPCR-based platform that analyzes more than 750 miRNAs, we analyzed control and diseased-associated plasma from a genetically engineered mouse model of OS to identify a profile of four plasma miRNAs. Subsequent analysis of 40 human patient samples corroborated these results. We also identified disease-specific endogenous reference plasma miRNAs for mouse and human studies. Specifically, we observed plasma miR-205-5p was decreased 2.68-fold in mice with OS compared to control mice, whereas, miR-214, and miR-335-5p were increased 2.37- and 2.69-fold, respectively. In human samples, the same profile was seen with miR-205-5p decreased 1.75-fold in patients with OS, whereas miR-574-3p, miR-214, and miR-335-5p were increased 3.16-, 8.31- and 2.52-fold, respectively, compared to healthy controls. Furthermore, low plasma levels of miR-214 in metastatic patients at time of diagnosis conveyed a significantly better overall survival. This is the first study to identify plasma miRNAs that could be used to prospectively identify disease, potentially monitor therapeutic efficacy and have prognostic implications for OS patients. © 2015 The Authors. Cancer Medicine published by John Wiley & Sons Ltd.
This article was published in Cancer Med and referenced in Journal of Data Mining in Genomics & Proteomics

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