Diagnostic potential for a serum miRNA neural network for detection of ovarian cancer
Vitonis, Allison F
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CitationElias, 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.
AbstractRecent 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.
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