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

作者：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^*

来源：eLife, 2017, 6: e28932.

DOI：10.7554/eLife.28932.001

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.

• 出版日期2017-10-31