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

eLife

Allison F Vitonis¹  Dipanjan Chowdhury²  Ross S Berkowitz³  Wojciech Fendler⁴  Christopher P Crum⁴  Daniel W Cramer⁵  Konrad Stawiski⁶  Gyorgy Frendl⁷  Kevin M Elias⁸  Panagiotis Konstantinopoulos⁸  Stephen J Fiascone⁹  Magdalena Kedzierska⁹ 

[1] Department of Biostatistics and Translational Medicine, Medical University of Lodz, Lodz, Poland;Department of Epidemiology, Harvard School of Public Health, Boston, United States;Department of Radiation Oncology, Dana-Farber Cancer Institute, Boston, United States;Harvard Medical School, Boston, United States;Obstetrics and Gynecology Epidemiology Center, Department of Obstetrics and Gynecology, Brigham and Women’s Hospital, Boston, United States;Surgical ICU Translational Research Center, Brigham and Women’s Hospital, Boston, United States;Department of Biostatistics and Translational Medicine, Medical University of Lodz, Lodz, Poland;Division of Gynecologic Oncology, Department of Obstetrics and Gynecology, Brigham and Women’s Hospital, Dana-Farber Cancer Institute, Boston, United States;Harvard Medical School, Boston, United States;

关键词: miRNA;    ovarian cancer;    serum;    next generation sequencing;    neural network;    machine learning;   

DOI  :  10.7554/eLife.28932

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown