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

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

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.

Publication types

  • Validation Study

MeSH terms

  • Aged
  • Biomarkers, Tumor / blood*
  • Female
  • Humans
  • MicroRNAs / blood*
  • Middle Aged
  • Neural Networks, Computer
  • Ovarian Neoplasms / diagnosis*
  • Ovarian Neoplasms / pathology*
  • Predictive Value of Tests
  • Real-Time Polymerase Chain Reaction
  • Sensitivity and Specificity
  • Serum / chemistry*

Substances

  • Biomarkers, Tumor
  • MicroRNAs