Diagnostic potential for a serum miRNA neural network for detection of ovarian cancer

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Diagnostic potential for a serum miRNA neural network for detection of ovarian cancer

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Title: Diagnostic potential for a serum miRNA neural network for detection of ovarian cancer
Author: Elias, Kevin M; Fendler, Wojciech; Stawiski, Konrad; Fiascone, Stephen J; Vitonis, Allison F; Berkowitz, Ross S; Frendl, Gyorgy; Konstantinopoulos, Panagiotis; Crum, Christopher P; Kedzierska, Magdalena; Cramer, Daniel W; Chowdhury, Dipanjan

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Citation: Elias, K. M., W. Fendler, K. Stawiski, S. J. Fiascone, A. F. Vitonis, R. S. Berkowitz, G. Frendl, et al. 2017. “Diagnostic potential for a serum miRNA neural network for detection of ovarian cancer.” eLife 6 (1): e28932. doi:10.7554/eLife.28932. http://dx.doi.org/10.7554/eLife.28932.
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Abstract: Recent studies posit a role for non-coding RNAs in epithelial ovarian cancer (EOC). Combining small RNA sequencing from 179 human serum samples with a neural network analysis produced a miRNA algorithm for diagnosis of EOC (AUC 0.90; 95% CI: 0.81–0.99). The model significantly outperformed CA125 and functioned well regardless of patient age, histology, or stage. Among 454 patients with various diagnoses, the miRNA neural network had 100% specificity for ovarian cancer. After using 325 samples to adapt the neural network to qPCR measurements, the model was validated using 51 independent clinical samples, with a positive predictive value of 91.3% (95% CI: 73.3–97.6%) and negative predictive value of 78.6% (95% CI: 64.2–88.2%). Finally, biologic relevance was tested using in situ hybridization on 30 pre-metastatic lesions, showing intratumoral concentration of relevant miRNAs. These data suggest circulating miRNAs have potential to develop a non-invasive diagnostic test for ovarian cancer.
Published Version: doi:10.7554/eLife.28932
Other Sources: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5679755/pdf/
Terms of Use: This article is made available under the terms and conditions applicable to Other Posted Material, as set forth at http://nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of-use#LAA
Citable link to this page: http://nrs.harvard.edu/urn-3:HUL.InstRepos:34493259
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