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血清 miRNA 神经网络在卵巢癌检测中的诊断潜力。

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

机构信息

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.

出版信息

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

DOI:10.7554/eLife.28932
PMID:29087294
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5679755/
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.

摘要

最近的研究提出非编码 RNA 在卵巢上皮性癌（EOC）中的作用。将 179 个人类血清样本的小 RNA 测序与神经网络分析相结合，产生了用于诊断 EOC 的 miRNA 算法（AUC 0.90；95%CI：0.81-0.99）。该模型明显优于 CA125，并且无论患者的年龄、组织学或分期如何，都能很好地发挥作用。在 454 名具有不同诊断的患者中，miRNA 神经网络对卵巢癌具有 100%的特异性。在使用 325 个样本使神经网络适应 qPCR 测量后，使用 51 个独立的临床样本验证了该模型，阳性预测值为 91.3%（95%CI：73.3-97.6%），阴性预测值为 78.6%（95%CI：64.2-88.2%）。最后，通过对 30 个前转移病变进行原位杂交测试了生物学相关性，显示了相关 miRNA 的肿瘤内浓度。这些数据表明循环 miRNA 具有开发卵巢癌非侵入性诊断测试的潜力。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1df2/5679755/87f72e59f4ad/elife-28932-fig10.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1df2/5679755/6f08b8578ad4/elife-28932-fig9.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1df2/5679755/87f72e59f4ad/elife-28932-fig10.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1df2/5679755/c48a4931c0af/elife-28932-fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1df2/5679755/7df60328ff99/elife-28932-fig2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1df2/5679755/6064c5b671dd/elife-28932-fig3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1df2/5679755/9bad5ebe9bfa/elife-28932-fig4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1df2/5679755/95016d93c36c/elife-28932-fig4-figsupp1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1df2/5679755/07c65d7d0861/elife-28932-fig4-figsupp2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1df2/5679755/dcbe37fd696a/elife-28932-fig5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1df2/5679755/eb486d16073b/elife-28932-fig6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1df2/5679755/a42bfb809361/elife-28932-fig7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1df2/5679755/6b60015c9a23/elife-28932-fig8.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1df2/5679755/6f08b8578ad4/elife-28932-fig9.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1df2/5679755/87f72e59f4ad/elife-28932-fig10.jpg

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