Zhang Fan, Deng Youping, Drabier Renee
Department of Academic and Institutional Resources and Technology, University of North Texas Health Science Center, Fort Worth, 76107, USA ; Department of Forensic and Investigative Genetics, University of North Texas Health Science Center, Fort Worth, 76107, USA.
Department of Internal Medicine Kidston House, Rush University Medical Center, 630 S. Hermitage Avenue, Room 408, Chicago, IL 60612, USA.
Biomed Res Int. 2013;2013:781618. doi: 10.1155/2013/781618. Epub 2013 Nov 26.
Detecting breast cancer at early stages can be challenging. Traditional mammography and tissue microarray that have been studied for early breast cancer detection and prediction have many drawbacks. Therefore, there is a need for more reliable diagnostic tools for early detection of breast cancer due to a number of factors and challenges. In the paper, we presented a five-marker panel approach based on SVM for early detection of breast cancer in peripheral blood and show how to use SVM to model the classification and prediction problem of early detection of breast cancer in peripheral blood. We found that the five-marker panel can improve the prediction performance (area under curve) in the testing data set from 0.5826 to 0.7879. Further pathway analysis showed that the top four five-marker panels are associated with signaling, steroid hormones, metabolism, immune system, and hemostasis, which are consistent with previous findings. Our prediction model can serve as a general model for multibiomarker panel discovery in early detection of other cancers.
早期检测乳腺癌具有挑战性。用于早期乳腺癌检测和预测的传统乳房X线摄影术和组织微阵列存在许多缺点。因此，由于多种因素和挑战，需要更可靠的诊断工具来早期检测乳腺癌。在本文中，我们提出了一种基于支持向量机的五标记物组合方法用于外周血中乳腺癌的早期检测，并展示了如何使用支持向量机对外周血中乳腺癌早期检测的分类和预测问题进行建模。我们发现，五标记物组合可将测试数据集中的预测性能（曲线下面积）从0.5826提高到0.7879。进一步的通路分析表明，排名前四的五标记物组合与信号传导、类固醇激素、代谢、免疫系统和止血相关，这与之前的研究结果一致。我们的预测模型可作为在其他癌症早期检测中发现多生物标志物组合的通用模型。
