Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsinchu, Taiwan.
Department of Biological Science and Technology, National Chiao Tung University, Hsinchu, Taiwan.
Sci Rep. 2018 Oct 31;8(1):16138. doi: 10.1038/s41598-018-34604-3.
Breast cancer is a heterogeneous disease and one of the most common cancers among women. Recently, microRNAs (miRNAs) have been used as biomarkers due to their effective role in cancer diagnosis. This study proposes a support vector machine (SVM)-based classifier SVM-BRC to categorize patients with breast cancer into early and advanced stages. SVM-BRC uses an optimal feature selection method, inheritable bi-objective combinatorial genetic algorithm, to identify a miRNA signature which is a small set of informative miRNAs while maximizing prediction accuracy. MiRNA expression profiles of a 386-patient cohort of breast cancer were retrieved from The Cancer Genome Atlas. SVM-BRC identified 34 of 503 miRNAs as a signature and achieved a 10-fold cross-validation mean accuracy, sensitivity, specificity, and Matthews correlation coefficient of 80.38%, 0.79, 0.81, and 0.60, respectively. Functional enrichment of the 10 highest ranked miRNAs was analysed in terms of Kyoto Encyclopedia of Genes and Genomes and Gene Ontology annotations. Kaplan-Meier survival analysis of the highest ranked miRNAs revealed that four miRNAs, hsa-miR-503, hsa-miR-1307, hsa-miR-212 and hsa-miR-592, were significantly associated with the prognosis of patients with breast cancer.
乳腺癌是一种异质性疾病，也是女性中最常见的癌症之一。最近，由于 microRNAs（miRNAs）在癌症诊断中的有效作用，它们已被用作生物标志物。本研究提出了一种基于支持向量机（SVM）的分类器 SVM-BRC，用于将乳腺癌患者分为早期和晚期。SVM-BRC 使用一种最优特征选择方法，可遗传的双目标组合遗传算法，来识别 miRNA 特征，这是一组信息量较小的有意义的 miRNAs，同时最大限度地提高预测准确性。从癌症基因组图谱中检索了 386 名乳腺癌患者的 miRNA 表达谱。SVM-BRC 确定了 34 个 miRNA 作为特征，在 10 倍交叉验证中平均准确率、灵敏度、特异性和 Matthews 相关系数分别为 80.38%、0.79、0.81 和 0.60。根据京都基因与基因组百科全书和基因本体论注释分析了排名前 10 的 miRNA 的功能富集。排名前 10 的 miRNA 的 Kaplan-Meier 生存分析表明，hsa-miR-503、hsa-miR-1307、hsa-miR-212 和 hsa-miR-592 这四个 miRNA 与乳腺癌患者的预后显著相关。
