Background Recently, due to the rapid development of proteomic techniques, great advance has been made in many scientific fields. We aimed to use magnetic beads （liquid chip） based matrix-assisted laser desorption/i...Background Recently, due to the rapid development of proteomic techniques, great advance has been made in many scientific fields. We aimed to use magnetic beads （liquid chip） based matrix-assisted laser desorption/ionization time-of-flight mass spectrometry （MALDI-TOF-MS） technology to screen distinctive biomarkers for lung adenocarcinoma （adCA）, and to establish the diagnostic protein profiles. Methods Using weak cation exchange magnetic beads （MB-WCX） to isolate and purify low molecular weight proteins from sera of 35 lung adCA, 46 benign lung diseases （BLDs） and 44 healthy individuals. The resulting spectra gained by anchor chip-MALDI-TOF-MS were analyzed by ClinProTools and a pattern recognition genetic algorithm （GA）. Results In the working mass range of 800-10 000 Da, 99 distinctive peaks were resolved in lung adCA versus BLDs, while 101 peaks were resolved in lung adCA versus healthy persons. The profile gained by GA that could distinguish adCA from BLDs was comprised of 4053.88, 4209.57 and 3883.33 Da with sensitivity of 80%, specificity of 93%, while that could separate adCA from healthy control was comprised of 2951.83 Da and 4209.73 Da with sensitivity of 94%, specificity of 95%. The sensitivity provided by carcinoembryonic antigen （CEA） in this experiment was significantly lower than our discriminatory profiles （P 〈0.005）. We further identified a eukaryotic peptide chain release factor GTP-binding subunit （eRF3b） （4209 Da） and a complement C3f （1865 Da） that may serve as candidate biomarkers for lung adCA. Conclusion Magnetic beads based MALDI-TOF-MS technology can rapidly and effectively screen distinctive proteins/polypeptides from sera of lung adCA patients and controls, which has potential value for establishing a new diagnostic method for lung adCA.展开更多
Background In recent years the proportion of lung adenocarcinoma （adCA） which occurs in lung cancer patients has increased. Using laser capture microdissection （LCM） combined with liquid chip-mass spectrometry tec...Background In recent years the proportion of lung adenocarcinoma （adCA） which occurs in lung cancer patients has increased. Using laser capture microdissection （LCM） combined with liquid chip-mass spectrometry technology, we aimed to screen lung cancer biomarkers by studying the proteins in the tissues of adCA. Methods We used LCM and magnetic bead based weak cation exchange （MB-WCX） to separate and purify the homogeneous adCA cells and normal cells from six cases of fresh adCA and matched normal lung tissues. The proteins were analyzed and identified by matrix assisted laser desorption/ionization time-of-fight mass spectrometry （MALDI-OF-MS）. We screened for the best pattern using a radial basic function neural network algorithm. Results About 2.895x10s and 1.584x10s cells were satisfactorily obtained by LCM from six cases of fresh lung adCA and matched normal lung tissues, respectively. The homogeneities of cell population were estimated to be over 95% as determined by microscopic visualization. Comparing the differentially expressed proteins between the lung adCA and the matched normal lung group, 221 and 239 protein peaks, respectively, were found in the mass-to-charge ration （M/Z） between 800 Da and 10 000 Da. According to ttest, the expression of two protein peaks at 7521.5 M/Zand 5079.3 M/Z had the largest difference between tissues. They were more weakly expressed in the lung adCA compared to the matched normal group. The two protein peaks could accurately separate the lung adCA from the matched normal lung group by the sample distribution chart. A discriminatory pattern which can separate the lung adCA from the matched normal lung tissue consisting of three proteins at 3358.1 M/Z, 5079.3 M/Z and 7521.5 M/Z was established by a radial basic function neural network algorithm with a sensitivity of 100% and a specificity of 100%. Conclusions Differential proteins in lung adCA were screened using LCM combined with liquid chip-mass spectrometry technology, and a biomarker model was established. It is possible that this technology is going to become a powerful tool in screening and early diagnosis of lung adCA.展开更多
基金This work was supported by grants from the National Natural Science Foundation of China （No. 30570795） and Program for New Century Excellent Talents in University （No. NECT-06-0845） and the Program in Science and Technology of Xi＇an, Shaanxi Province （No. S F08009（1））.Acknowledgement： We are grateful to HU Xiao-hui for the technical guidance.
文摘Background Recently, due to the rapid development of proteomic techniques, great advance has been made in many scientific fields. We aimed to use magnetic beads （liquid chip） based matrix-assisted laser desorption/ionization time-of-flight mass spectrometry （MALDI-TOF-MS） technology to screen distinctive biomarkers for lung adenocarcinoma （adCA）, and to establish the diagnostic protein profiles. Methods Using weak cation exchange magnetic beads （MB-WCX） to isolate and purify low molecular weight proteins from sera of 35 lung adCA, 46 benign lung diseases （BLDs） and 44 healthy individuals. The resulting spectra gained by anchor chip-MALDI-TOF-MS were analyzed by ClinProTools and a pattern recognition genetic algorithm （GA）. Results In the working mass range of 800-10 000 Da, 99 distinctive peaks were resolved in lung adCA versus BLDs, while 101 peaks were resolved in lung adCA versus healthy persons. The profile gained by GA that could distinguish adCA from BLDs was comprised of 4053.88, 4209.57 and 3883.33 Da with sensitivity of 80%, specificity of 93%, while that could separate adCA from healthy control was comprised of 2951.83 Da and 4209.73 Da with sensitivity of 94%, specificity of 95%. The sensitivity provided by carcinoembryonic antigen （CEA） in this experiment was significantly lower than our discriminatory profiles （P 〈0.005）. We further identified a eukaryotic peptide chain release factor GTP-binding subunit （eRF3b） （4209 Da） and a complement C3f （1865 Da） that may serve as candidate biomarkers for lung adCA. Conclusion Magnetic beads based MALDI-TOF-MS technology can rapidly and effectively screen distinctive proteins/polypeptides from sera of lung adCA patients and controls, which has potential value for establishing a new diagnostic method for lung adCA.
基金This work was supported by grants from the National Natural Science Foundation of China （No. 30570795） and Program for New Century Excellent Talents in University （No. NECT-06-0845） and the Program in Science and Technology of Xi＇an, Shaanxi Province （No. SF08009（1））.
文摘Background In recent years the proportion of lung adenocarcinoma （adCA） which occurs in lung cancer patients has increased. Using laser capture microdissection （LCM） combined with liquid chip-mass spectrometry technology, we aimed to screen lung cancer biomarkers by studying the proteins in the tissues of adCA. Methods We used LCM and magnetic bead based weak cation exchange （MB-WCX） to separate and purify the homogeneous adCA cells and normal cells from six cases of fresh adCA and matched normal lung tissues. The proteins were analyzed and identified by matrix assisted laser desorption/ionization time-of-fight mass spectrometry （MALDI-OF-MS）. We screened for the best pattern using a radial basic function neural network algorithm. Results About 2.895x10s and 1.584x10s cells were satisfactorily obtained by LCM from six cases of fresh lung adCA and matched normal lung tissues, respectively. The homogeneities of cell population were estimated to be over 95% as determined by microscopic visualization. Comparing the differentially expressed proteins between the lung adCA and the matched normal lung group, 221 and 239 protein peaks, respectively, were found in the mass-to-charge ration （M/Z） between 800 Da and 10 000 Da. According to ttest, the expression of two protein peaks at 7521.5 M/Zand 5079.3 M/Z had the largest difference between tissues. They were more weakly expressed in the lung adCA compared to the matched normal group. The two protein peaks could accurately separate the lung adCA from the matched normal lung group by the sample distribution chart. A discriminatory pattern which can separate the lung adCA from the matched normal lung tissue consisting of three proteins at 3358.1 M/Z, 5079.3 M/Z and 7521.5 M/Z was established by a radial basic function neural network algorithm with a sensitivity of 100% and a specificity of 100%. Conclusions Differential proteins in lung adCA were screened using LCM combined with liquid chip-mass spectrometry technology, and a biomarker model was established. It is possible that this technology is going to become a powerful tool in screening and early diagnosis of lung adCA.
