Diagnostic potential for a serum miRNA neural network for detection of ovarian cancer
Menée à partir de données portant sur 179, 454, 325 puis 51 échantillons sériques humains, cette étude évalue la performance d'un système d'algorithmes, basé sur la présence de microARNs dans le sérum et élaboré selon la technologie des réseaux de neurones, pour diagnostiquer un cancer de l'ovaire
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.
eLife , résumé, 2017