Loke Sau Yeen, Munusamy Prabhakaran, Koh Geok Ling, Chan Claire Hian Tzer, Madhukumar Preetha, Thung Jee Liang, Tan Kiat Tee Benita, Ong Kong Wee, Yong Wei Sean, Sim Yirong, Oey Chung Lie, Lim Sue Zann, Chan Mun Yew Patrick, Ho Teng Swan Juliana, Khoo Boon Kheng James, Wong Su Lin Jill, Thng Choon Hua, Chong Bee Kiang, Tan Ern Yu, Tan Veronique Kiak-Mien, Lee Ann Siew Gek
Cellular and Molecular Research, Humphrey Oei Institute of Cancer Research, National Cancer Centre, Singapore 169610, Singapore.
SingHealth Duke-NUS Oncology Academic Clinical Programme, Duke-NUS Medical School, Singapore 169857, Singapore.
Cancers (Basel). 2019 Nov 26;11(12):1872. doi: 10.3390/cancers11121872.
Although mammography is the gold standard for breast cancer screening, the high rates of false-positive mammograms remain a concern. Thus, there is an unmet clinical need for a non-invasive and reliable test to differentiate between malignant and benign breast lesions in order to avoid subjecting patients with abnormal mammograms to unnecessary follow-up diagnostic procedures. Serum samples from 116 malignant breast lesions and 64 benign breast lesions were comprehensively profiled for 2,083 microRNAs (miRNAs) using next-generation sequencing. Of the 180 samples profiled, three outliers were removed based on the principal component analysis (PCA), and the remaining samples were divided into training ( = 125) and test ( = 52) sets at a 70:30 ratio for further analysis. In the training set, significantly differentially expressed miRNAs (adjusted < 0.01) were identified after correcting for multiple testing using a false discovery rate. Subsequently, a predictive classification model using an eight-miRNA signature and a Bayesian logistic regression algorithm was developed. Based on the receiver operating characteristic (ROC) curve analysis in the test set, the model could achieve an area under the curve (AUC) of 0.9542. Together, this study demonstrates the potential use of circulating miRNAs as an adjunct test to stratify breast lesions in patients with abnormal screening mammograms.
尽管乳腺钼靶检查是乳腺癌筛查的金标准，但乳腺钼靶检查的高假阳性率仍是一个令人担忧的问题。因此，临床上迫切需要一种非侵入性且可靠的检测方法，以区分乳腺恶性病变和良性病变，从而避免对乳腺钼靶检查异常的患者进行不必要的后续诊断程序。使用下一代测序技术对116例乳腺恶性病变和64例乳腺良性病变的血清样本进行了2083种微小RNA（miRNA）的全面分析。在分析的180个样本中，根据主成分分析（PCA）去除了3个异常值，其余样本按70:30的比例分为训练集（n = 125）和测试集（n = 52）进行进一步分析。在训练集中，使用错误发现率对多重检验进行校正后，鉴定出显著差异表达的miRNA（校正后P < 0.01）。随后，开发了一种使用八-miRNA特征和贝叶斯逻辑回归算法的预测分类模型。根据测试集中的受试者工作特征（ROC）曲线分析，该模型的曲线下面积（AUC）可达0.9542。总之，本研究证明了循环miRNA作为辅助检测手段，对乳腺钼靶筛查异常患者的乳腺病变进行分层的潜在用途。
