Biomarker discovery and identification from non-small cell lung cancer sera.

Currently, serum biomarkers might usually be thought not to be used for early detection of lung cancer by some researchers. In this study, we used a highly optimized ClinProt-matrix-assisted laser desorption/ionization time-of flight mass spectrometer (MALDI-TOF-MS) to screen non-small cell lung carcinoma (NSCLC) markers in serum. A training set of spectra derived from 45 NSCLC patients, 24 patients with benign lung diseases (BLDs) and 21 healthy individuals, was used to develop a proteomic pattern that discriminated cancer from non-cancer effectively. A test set, including 74 cases (29 NSCLC patients and 45 controls), was used to validate this pattern. After cross-validation, the classifier showed sensitivity and specificity, 86.20% and 80.00%, respectively. Remarkably, 100% of early stage serum samples could be correctly classified as lung cancer. Furthermore, the differential peptides of 1865Da and 4209Da were identified as element of component 3 and eukaryotic peptide chain release factor GTP-binding subunit ERF, respectively. The patterns we described and peptides we identified may have clinical utility as surrogate markers for detection and classification of NSCLC.

Study of differential proteins in lung adenocarcinoma using laser capture microdissection combined with liquid chip-mass spectrometry technology.

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 2.895 × 10(6) and 1.584 × 10(6) 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 t test, the expression of two protein peaks at 7521.5 M/Z and 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.

Detection of lung adenocarcinoma using magnetic beads based matrix-assisted laser desorption/ionization time-of-flight mass spectrometry serum protein profiling.

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.

[Application of liquid chip-mass spectrometry technology for screening serum biomarker proteins in lung squamous cell carcinoma].

OBJECTIVE: To screen the serum biomarker proteins of lung squamous cell carcinoma (SCCs) by liquid chip-mass spectrometry technology. METHODS: All serum samples, including 34 SCCs, 46 benign lung diseases (BLDs) and 44 healthy individuals, were analyzed by CLINPROT system in order to study the serum protein expression profiles. Then the discriminatory proteins were detected by FlexAnalysis 3.0 software. Biomarkers were identified by liquid chromatography-tandem mass spectrometry (LCMS/MS). RESULTS: Comparing the differential serum expression proteins between SCCs and healthy individuals, and SCCs and BLDs respectively. Ninety-six differential protein peaks [mass-to-charge ration (M/Z) between 800 and 10 000] were found between SCCs and healthy individuals. In these protein peaks, the expression of protein peaks at 4054.13 M/Z and 4267.46 M/Z had the largest difference between them. The two protein peaks could accurately separate SCCs from healthy individuals by the frame of axes. Similarly, 99 differential protein peaks were automatically detected between SCCs and BLDs. In these protein peaks, the expression of protein peaks at 5065.27 M/Z and 4054.02 M/Z had the largest difference between them. The two protein peaks could accurately separate SCCs from BLDs by the frame of axes. Identified by LC-MS/MS, 1778 M/Z and 1865 M/Z might be assayed jointly and corresponded to complements C3 fragment or C3f precursor. CONCLUSIONS: Differential protein expressions existed between SCCs versus healthy individuals and SCCs versus BLD patients. It is feasible to screen the diagnostic serum biomarkers of SCC with a high sensitivity and specificity by using CLINPROT system.

[The value of (18)F-FDG PET/CT in differential diagnosis of benign and malignant pulmonary lesions: a Meta-analysis.].

OBJECTIVE: Using Meta analysis to evaluate the value of (18)F-FDG PET/CT ((18)fluorine-fluorodeoxyglucose Positron emission tomography/computed tomography) in differentiating between benign and malignant pulmonary lesions. METHODS: Relevant documentations from PubMed and other 5 databases from 1980 to 2008 were searched, and the eligible literatures according to the inclusive criteria were selected. The statistical information and quality of science were assessed and classified. The data were analyzed using Meta-Disc1.4 software. The diagnostic value of PET/CT in distinguishing benign from malignant pulmonary lesions was evaluated by the pooled sensitivity, specificity, the likelihood ratio (LR) and summary receiver operating characteristic curve (SROC curve) statistical indicators. RESULTS: Seven literatures were collected including 5 in English and 2 in Chinese, and 795 cases were included in the study. Heterogeneity test showed that the homogeneity of the study was good. By using deterministic models to analyze the data, the value of the weighted sensitivity was 95% (93% - 97%), the specificity was 77% (71% - 82%), the positive likelihood ratio was 4.12, negative likelihood ratio was 0.08, and the SROC area under the curve (area under curve, AUC) was 94%. CONCLUSION: PET/CT is of high diagnostic value in differentiation between benign and malignant lung lesions, but large sized, multicenter, prospective studies are needed to assess its clinical value more accurately.

[Use of laser capture microdissection and surface-enhanced laser desorption ionization time-of-flight mass spectrometry to screen differential proteins in lung adenocarcinoma and lung squamous carcinoma].

OBJECTIVE: To screen biomarkers for classification in lung adenocarcinoma and lung squamous carcinoma by using laser capture microdissection (LCM) and surface-enhanced laser desorption ionization time-of-flight mass spectrometry (SELDI-TOF-MS) and support vector machine (SVM). METHODS: Six specimens of lung adenocarcinoma tissues and seven specimens of lung squamous carcinoma tissues obtained during operation were made into frozen sections and stained by improved H-E solution. About 1.2 x 10(5) of homogeneous adenocarcinoma cells and 1.4 x 10(5) of homogeneous lung squamous carcinoma cells were collected using LCM. Then SELDI profiles based on PBS II(+)SELDI-TOF-MS (IMAC protein chip) and the data were analyzed by support vector machine (SVM). RESULTS: Eighty seven differential protein peaks were found and top ten of them were identified as candidate biomarkers. The expression levels of 6 proteins among them with the molecular weights of 3333, 3592, 3848, 5036, 5191, and 5211 respectively in the lung squamous cancer tissues were weaker than those in the adenocarcinoma tissues, and the expression levels of 4 proteins with the molecular weights of 2505, 4004, 4847, and 11 412 in the lung squamous carcinoma tissues were stronger than those in the adenocarcinoma tissues. The expression of the protein with the molecular weight of 4847 in the squamous cancer was significantly stronger than that in the adenocarcinoma (p + 0.032). A discriminatory pattern consisting of 3 proteins with the molecular weights of 4847, 11 412, and 3592 was established with a sensitivity of 100% and a specificity of 100% respectively in separating adenocarcinoma from squamous carcinoma. CONCLUSION: There is a difference in protein component between adenocarcinoma and squamous carcinoma. LCM combined with SELDI-TOF-MS help screen a biomarker pattern to distinguish lung adenocarcinoma from lung squamous carcinoma with high sensitivity and specificity.