Affiliations: Key laboratory for Cell Proliferation and Regulation
Biology Ministry of Education, Beijing Normal University, Beijing 100875,
China
Note: [] Corresponding author: Dacheng He, Key laboratory for Cell
Proliferation and Regulation Biology Ministry of Education, Beijing Normal
University, Universities' of Confederated Institute for Proteomics, Beijing
100875, China. Tel.: +86 10 62208439; Fax: +86 10 62209729; E-mail:
dhe@bnu.edu.cn
Abstract: Lung cancer is at present the number one cause of cancer death and
no biomarker is available to detect early lung cancer in serum samples so far.
The objective of this study is to find specific biomarkers for detection of
lung cancer using Surface Enhanced Laser Desorption/Ionization (SELDI)
technology. In this study, serum samples from 30 lung cancer patients and 51
age-and sex-matched healthy were analyzed by SELDI based ProteinChip reader,
PBSII-C. The spectra were generated on WCX2 chips and protein peaks clustering
and classification analyses were performed utilizing Biomarker Wizard and
Biomarker Patterns software packages, respectively. Three protein peaks were
automatically chosen for the system training and the development of a decision
classification tree. The constructed model was then used to test an independent
set of masked serum samples from 15 lung cancer patients and 31 healthy
individuals. The analysis yielded a sensitivity of 93.3%, and a specificity of
96.7%. These results suggest that the serum is a capable resource for detection
of specific lung cancer biomarkers. SELDI technique combined with an artificial
intelligence classification algorithm can both facilitate the discovery of
better biomarkers for lung cancer and provide a useful tool for molecular
diagnosis in future.
