Zhang Fan, Deng Youping, Wang Mu, Cui Li, Drabier Renee
Department of Academic and Institutional Resources and Technology, University of North Texas Health Science Center, Fort Worth, TX, USA. ; Department of Forensic and Investigative Genetics, University of North Texas Health Science Center, Fort Worth, TX, USA.
Department of Internal Medicine, Rush University Medical Center, Chicago, IL, USA.
Cancer Inform. 2015 Feb 9;13(Suppl 5):101-8. doi: 10.4137/CIN.S14069. eCollection 2014.
Genes do not function alone but through complex biological pathways. Pathway-based biomarkers may be a reliable diagnostic tool for early detection of breast cancer due to the fact that breast cancer is not a single homogeneous disease. We applied Integrated Pathway Analysis Database (IPAD) and Gene Set Enrichment Analysis (GSEA) approaches to the study of pathway-based biomarker discovery problem in breast cancer proteomics. Our strategy for identifying and analyzing pathway-based biomarkers are threefold. Firstly, we performed pathway analysis with IPAD to build the gene set database. Secondly, we ran GSEA to identify 16 pathway-based biomarkers. Lastly, we built a Support Vector Machine model with three-way data split and fivefold cross-validation to validate the biomarkers. The approach-unraveling the intricate pathways, networks, and functional contexts in which genes or proteins function-is essential to the understanding molecular mechanisms of pathway-based biomarkers in breast cancer.
基因并非单独发挥作用，而是通过复杂的生物途径起作用。由于乳腺癌并非单一的同质疾病，基于途径的生物标志物可能是早期检测乳腺癌的可靠诊断工具。我们将综合途径分析数据库（IPAD）和基因集富集分析（GSEA）方法应用于乳腺癌蛋白质组学中基于途径的生物标志物发现问题的研究。我们识别和分析基于途径的生物标志物的策略有三个方面。首先，我们使用IPAD进行途径分析以构建基因集数据库。其次，我们运行GSEA来识别16个基于途径的生物标志物。最后，我们构建了一个支持向量机模型，采用三分法数据划分和五重交叉验证来验证这些生物标志物。这种揭示基因或蛋白质发挥作用的复杂途径、网络和功能背景的方法，对于理解乳腺癌中基于途径的生物标志物的分子机制至关重要。
