Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/14903
Title: Deep proteome profiling of sera from never-smoked lung cancer patients
Authors: Au, JSK
Cho, WCS
Yip, TT
Yip, C
Zhu, H
Leung, WWF 
Tsui, PYB
Kwok, DLP
Kwan, SSM
Cheng, WW
Tzang, LCH
Yang, M
Law, SCK
Keywords: Combinatorial ligands
Lung neoplasm
Proteomic profiling
Issue Date: 2007
Source: Biomedicine and pharmacotherapy, 2007, v. 61, no. 9 SPEC. ISS., p. 570-577 How to cite?
Journal: Biomedicine and Pharmacotherapy 
Abstract: Previous studies on the serum proteome are hampered by the huge dynamic range of concentration of different protein species. The use of Equalizer Beads coupled with a combinatorial library of ligands has been shown to allow access to many low-abundance proteins or polypeptides undetectable by classical analytical methods. This study focused on never-smoked lung cancer, which is considered to be more homogeneous and distinct from smoking-related cases both clinically and biologically. Serum samples obtained from 42 never-smoked lung cancer patients (28 patients with active untreated disease and 14 patients with tumor resected) were compared with those from 30 normal control subjects using the pioneering Equalizer Beads technology followed by subsequent analysis by surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS). Eighty-five biomarkers were significantly different between lung cancer and normal control. The application of classification algorithms based on significant biomarkers achieved good accuracy of 91.7%, 80% and 87.5% in class-prediction with respect to presence or absence of disease, subsequent development of metastasis and length of survival (longer or shorter than median) respectively. Support vector machine (SVM) performed best overall. We have proved the feasibility and convenience of using the Equalizer Beads technology to study the deep proteome of the sera of lung cancer patients in a rapid and high-throughput fashion, and which enables detection of low abundance polypeptides/proteins biomarkers. Coupling with classification algorithms, the technologies will be clinically useful for diagnosis and prediction of prognosis in lung cancer.
URI: http://hdl.handle.net/10397/14903
ISSN: 0753-3322
DOI: 10.1016/j.biopha.2007.08.017
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