Details

Autor(en) / Beteiligte

• 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

Titel

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

Ist Teil von

• eLife, 2017-10, Vol.6

Ort / Verlag

England: eLife Sciences Publications Ltd

Erscheinungsjahr

2017

Quelle

MEDLINE

Beschreibungen/Notizen

• 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.