Department of Chemistry, University of California, Davis, CA, USA.
Proteomics. 2012 Aug;12(15-16):2523-38. doi: 10.1002/pmic.201100273.
Human serum glycomics is a promising method for finding cancer biomarkers but often lacks the tools for streamlined data analysis. The Glycolyzer software incorporates a suite of analytic tools capable of identifying informative glycan peaks out of raw mass spectrometry data. As a demonstration of its utility, the program was used to identify putative biomarkers for epithelial ovarian cancer from a human serum sample set. A randomized, blocked, and blinded experimental design was used on a discovery set consisting of 46 cases and 48 controls. Retrosynthetic glycan libraries were used for data analysis and several significant candidate glycan biomarkers were discovered via hypothesis testing. The significant glycans were attributed to a glycan family based on glycan composition relationships and incorporated into a linear classifier motif test. The motif test was then applied to the discovery set to evaluate the disease state discrimination performance. The test provided strongly predictive results based on receiver operator characteristic curve analysis. The area under the receiver operator characteristic curve was 0.93. Using the Glycolyzer software, we were able to identify a set of glycan biomarkers that highly discriminate between cases and controls, and are ready to be formally validated in subsequent studies.
人血清糖组学是一种很有前途的寻找癌症生物标志物的方法，但通常缺乏简化数据分析的工具。Glycolyzer 软件集成了一套分析工具，能够从原始质谱数据中识别出有意义的聚糖峰。作为其效用的一个演示，该程序被用于从一组人类血清样本中识别上皮性卵巢癌的潜在生物标志物。在一个由 46 个病例和 48 个对照组成的发现集上使用了随机、分块和盲法实验设计。通过假设检验，使用回溯合成聚糖文库进行数据分析，发现了几个具有显著意义的候选聚糖生物标志物。根据聚糖组成关系，将显著聚糖归因于聚糖家族，并将其纳入线性分类器基序测试中。然后将该基序测试应用于发现集，以评估疾病状态的区分性能。该测试基于接受者操作特征曲线分析提供了强有力的预测结果。接受者操作特征曲线下的面积为 0.93。使用 Glycolyzer 软件，我们能够识别出一组聚糖生物标志物，这些标志物能够高度区分病例和对照组，并且已经准备好在后续研究中进行正式验证。
