Identification of N-glycan serum markers associated with hepatocellular carcinoma from mass spectrometry data

Zhiqun Tang, Rency S. Varghese, Slavka Bekesova, Christopher A. Loffredo, Mohamed Abdul Hamid, Zuzana Kyselova, Yehia Mechref, Milos V. Novotny, Radoslav Goldman, Habtom W. Ressom

Research output: Contribution to journalArticlepeer-review

60 Scopus citations


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.

Original languageEnglish
Pages (from-to)104-112
Number of pages9
JournalJournal of Proteome Research
Issue number1
StatePublished - Jan 4 2010


  • Biomarker discovery
  • Glycan biomarker
  • Hepatocellular carcinoma
  • Mass spectrometry
  • Recursive feature selection
  • Support vector machine


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