Frères Pierre, Wenric Stéphane, Boukerroucha Meriem, Fasquelle Corinne, Thiry Jérôme, Bovy Nicolas, Struman Ingrid, Geurts Pierre, Collignon Joëlle, Schroeder Hélène, Kridelka Frédéric, Lifrange Eric, Jossa Véronique, Bours Vincent, Josse Claire, Jerusalem Guy
University Hospital (CHU), Department of Medical Oncology, Liège, Belgium.
University of Liège, GIGA-Research, Laboratory of Human Genetics, Liège, Belgium.
Oncotarget. 2016 Feb 2;7(5):5416-28. doi: 10.18632/oncotarget.6786.
Circulating microRNAs (miRNAs) are increasingly recognized as powerful biomarkers in several pathologies, including breast cancer. Here, their plasmatic levels were measured to be used as an alternative screening procedure to mammography for breast cancer diagnosis.A plasma miRNA profile was determined by RT-qPCR in a cohort of 378 women. A diagnostic model was designed based on the expression of 8 miRNAs measured first in a profiling cohort composed of 41 primary breast cancers and 45 controls, and further validated in diverse cohorts composed of 108 primary breast cancers, 88 controls, 35 breast cancers in remission, 31 metastatic breast cancers and 30 gynecologic tumors.A receiver operating characteristic curve derived from the 8-miRNA random forest based diagnostic tool exhibited an area under the curve of 0.81. The accuracy of the diagnostic tool remained unchanged considering age and tumor stage. The miRNA signature correctly identified patients with metastatic breast cancer. The use of the classification model on cohorts of patients with breast cancers in remission and with gynecologic cancers yielded prediction distributions similar to that of the control group.Using a multivariate supervised learning method and a set of 8 circulating miRNAs, we designed an accurate, minimally invasive screening tool for breast cancer.
循环微RNA（miRNA）在包括乳腺癌在内的多种疾病中日益被视为强大的生物标志物。在此，检测了它们的血浆水平，以用作乳腺癌诊断的乳腺X线摄影替代筛查程序。通过RT-qPCR在378名女性队列中确定了血浆miRNA谱。基于在由41例原发性乳腺癌和45例对照组成的分析队列中首先测量的8种miRNA的表达设计了一种诊断模型，并在由108例原发性乳腺癌、88例对照、35例缓解期乳腺癌、31例转移性乳腺癌和30例妇科肿瘤组成的不同队列中进一步验证。源自基于8-miRNA随机森林的诊断工具的受试者工作特征曲线显示曲线下面积为0.81。考虑年龄和肿瘤分期，诊断工具的准确性保持不变。miRNA特征正确识别了转移性乳腺癌患者。在缓解期乳腺癌患者和妇科癌症患者队列中使用分类模型产生的预测分布与对照组相似。使用多变量监督学习方法和一组8种循环miRNA，我们设计了一种准确、微创的乳腺癌筛查工具。
