期刊
JOURNAL OF PROTEOME RESEARCH
卷 9, 期 1, 页码 104-112出版社
AMER CHEMICAL SOC
DOI: 10.1021/pr900397n
关键词
hepatocellular carcinoma; glycan biomarker; biomarker discovery; mass spectrometry; support vector machine; recursive feature selection
资金
- National Science Foundation [IIS-0812246]
- National Cancer Institute (NCI) [R21CA130837, R03CA119313]
- NCI Early Detection Research Network Associate Membership Grant
- Prevent Cancer Foundation Grant
- NATIONAL CANCER INSTITUTE [R21CA130837, R03CA119313] Funding Source: NIH RePORTER
Glycocylation represents the most complex and widespread post-translational modifications in human proteins. The variation of glycosylation is closely related to oncogenic transformation. Therefore, profiling of glycans detached from proteins is a promising strategy to identify biomarkers for cancer detection. This study identified candidate glycan biomarkers associated with hepatocellular carcinoma by mass spectrometry. Specifically, mass spectrometry data were analyzed with a peak selection procedure which incorporates multiple random sampling strategies with recursive feature selection based on support vector machines. Ten peak sets were obtained from different combinations of samples. Seven peaks were shared by each of the 10 peaksets, in which 7-12 peaks were selected, indicating 58-100% of peaks were shared by the 10 peaksets. Support vector machines and hierarchical clustering method were used to evaluate the performance of the peaksets. The predictive performance of the seven peaks was further evaluated by using 19 newly generated MALDI-TOF spectra. Glycan structures for four glycans of the seven peaks were determined. Literature search indicated that the structures of the four glycans could be found in some cancer-related glycoproteins. The method of this study is significant in deriving consistent, accurate, and biological significant glycan marker candidates for hepatocellular carcinoma diagnosis.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据