Xiao Xueyuan, Liu Danhui, Tang Ying, Guo Fuzheng, Xia Liang, Liu Jin, He Dacheng
Key Laboratory for Cell Proliferation and Regulation Biology Ministry of Education, Beijing Normal University, Beijing 100875, China.
Dis Markers. 2003;19(1):33-9. doi: 10.1155/2003/278152.
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.
肺癌目前是癌症死亡的首要原因，到目前为止，尚无生物标志物可用于检测血清样本中的早期肺癌。本研究的目的是利用表面增强激光解吸/电离（SELDI）技术寻找用于检测肺癌的特异性生物标志物。在本研究中，使用基于SELDI的蛋白质芯片阅读器PBSII-C对30例肺癌患者和51例年龄及性别匹配的健康人的血清样本进行了分析。在WCX2芯片上生成光谱，并分别利用Biomarker Wizard和Biomarker Patterns软件包进行蛋白质峰聚类和分类分析。自动选择三个蛋白质峰用于系统训练和决策分类树的开发。然后使用构建的模型对来自15例肺癌患者和31例健康个体的一组独立的盲法血清样本进行测试。分析得出的灵敏度为93.3%，特异性为96.7%。这些结果表明，血清是检测特异性肺癌生物标志物的有效资源。SELDI技术与人工智能分类算法相结合，既有助于发现更好的肺癌生物标志物，也为未来的分子诊断提供了有用的工具。
