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Elife. 2017 Oct 31;6. pii: e28932. doi: 10.7554/eLife.28932.

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

Author information

1
Division of Gynecologic Oncology, Department of Obstetrics and Gynecology, Brigham and Women's Hospital, Dana-Farber Cancer Institute, Boston, United States.
2
Harvard Medical School, Boston, United States.
3
Surgical ICU Translational Research Center, Brigham and Women's Hospital, Boston, United States.
4
Department of Biostatistics and Translational Medicine, Medical University of Lodz, Lodz, Poland.
5
Department of Radiation Oncology, Dana-Farber Cancer Institute, Boston, United States.
6
Obstetrics and Gynecology Epidemiology Center, Department of Obstetrics and Gynecology, Brigham and Women's Hospital, Boston, United States.
7
Department of Epidemiology, Harvard School of Public Health, Boston, United States.
8
Department of Anesthesiology, Perioperative and Pain Medicine, Brigham and Women's Hospital, Boston, United States.
9
Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, United States.
10
Division of Women's and Perinatal Pathology, Department of Pathology, Brigham and Women's Hospital, Boston, United States.
11
Department of Clinical Oncology, Medical University of Lodz, Lodz, Poland.

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.

KEYWORDS:

cancer biology; human; machine learning; miRNA; neural network; next generation sequencing; ovarian cancer; serum

PMID:
29087294
PMCID:
PMC5679755
DOI:
10.7554/eLife.28932
[Indexed for MEDLINE]
Free PMC Article

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