Front Cover: Exploring the expression bar code of SAA variants for gastric cancer detection. Issue 11 (8th June 2017)

• Record Type: Journal Article
• Authors: Wu, Deng‐Chyang; Wang, Kai‐Yi; Wang, Sophie S.W.; Huang, Ching‐Min; Lee, Yi‐Wei; Chen, Michael Isaac; Chuang, Szu‐An; Chen, Shu‐Hua; Lu, Ying‐Wei; Lin, Chun‐Cheng; Lee, Ka‐Wo; Hsu, Wen‐Hung; Wu, Kun‐Pin; Chen, Yu‐Ju
• Is Part Of: Proteomics. Volume 17:Issue 11(2017)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2017-06-08
• Subjects: Proteins -- Separation -- Periodicals; Bioinformatics -- Periodicals; Proteomics -- Periodicals; Genomes -- Periodicals; Molecular genetics -- Periodicals; 572.605
• DOI: 10.1002/pmic.201770090
• Languages: English
• ISSNs: 1615-9853

Abstract

Abstract : DOI:10.1002/pmic.201600356 A nanoprobe‐based affinity mass spectrometry platform to explore the under‐estimated serum protein variant patterns in cancer, and its utility as a new class of biomarker panel for diagnosis, is reported here. Using SAA as a model, the results not only revealed the surprisingly heterogeneous and most comprehensive SAA bar code to date, but also demonstrated its good discrimination for differentiating gastric cancer patients from gastritis and normal groups. This study suggests a new concept of diagnostic platform by protein variant profile. For more details, see the research article by Deng‐Chyang Wu et al., article number 1600356.