Development of Proteomic Patterns for Detecting Lung Cancer

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.