1. Diagnostic potential for a serum miRNA neural network for detection of ovarian cancer
- Author
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Kevin M Elias, Wojciech Fendler, Konrad Stawiski, Stephen J Fiascone, Allison F Vitonis, Ross S Berkowitz, Gyorgy Frendl, Panagiotis Konstantinopoulos, Christopher P Crum, Magdalena Kedzierska, Daniel W Cramer, and Dipanjan Chowdhury
- Subjects
miRNA ,ovarian cancer ,serum ,next generation sequencing ,neural network ,machine learning ,Medicine ,Science ,Biology (General) ,QH301-705.5 - 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
- 2017
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