Improving Detection Accuracy of Lung Cancer Serum Proteomic Profiling via Two-Stage Training Process

Springer Science and Business Media LLC - Tập 9 - Trang 1-10 - 2011
Pei-Sung Hsu1, Yu-Shan Wang2, Su-Chen Huang2, Yi-Hsien Lin3, Chih-Chia Chang2, Yuk-Wah Tsang2, Jiunn-Song Jiang1, Shang-Jyh Kao1, Wu-Ching Uen4, Kwan-Hwa Chi2,5
1Division of Chest Medicine, Shin Kong Wu Ho-Su Memorial Hospital, Taipei, Taiwan
2Division of Radiation Therapy and Oncology, Shin Kong Wu Ho-Su Memorial Hospital, Taipei, Taiwan
3Institute of Traditional Medicine, National Yang-Ming University, Taiwan
4Division of Hematology and Oncology, Shin Kong Wu Ho-Su Memorial Hospital, Taipei, Taiwan
5School of Medicine and Institute of Biomedical Image and Radiation Science, National Yang Ming University, Taipei, Taiwan

Tóm tắt

Surface-Enhanced Laser Desorption/Ionization Time-of-Flight Mass Spectrometry (SELDI-TOF-MS) is a frequently used technique for cancer biomarker research. The specificity of biomarkers detected by SELDI can be influenced by concomitant inflammation. This study aimed to increase detection accuracy using a two-stage analysis process. Sera from 118 lung cancer patients, 72 healthy individuals, and 31 patients with inflammatory disease were randomly divided into training and testing groups by 3:2 ratio. In the training group, the traditional method of using SELDI profile analysis to directly distinguish lung cancer patients from sera was used. The two-stage analysis of distinguishing the healthy people and non-healthy patients (1st-stage) and then differentiating cancer patients from inflammatory disease patients (2nd-stage) to minimize the influence of inflammation was validated in the test group. In the test group, the one-stage method had 87.2% sensitivity, 37.5% specificity, and 64.4% accuracy. The two-stage method had lower sensitivity (> 70.1%) but statistically higher specificity (80%) and accuracy (74.7%). The predominantly expressed protein peak at 11480 Da was the primary splitter regardless of one- or two-stage analysis. This peak was suspected to be SAA (Serum Amyloid A) due to the similar m/z countered around this area. This hypothesis was further tested using an SAA ELISA assay. Inflammatory disease can severely interfere with the detection accuracy of SELDI profiles for lung cancer. Using a two-stage training process will improve the specificity and accuracy of detecting lung cancer.

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