[Identification and clinical evaluation of lung cancer serum biomarker L-lactate dehydrogenase B].

OBJECTIVE: To identify potential biomarkers related with lung cancer metastasis. METHODS: Conditional media proteins collected from a primary non-small cell lung cancer cell line (NSCLC) NCI-H226 and its brain metastatic subline H226Br were analyzed by SDS-PAGE and MALDI-TOF-MS. Twelve biomarkers were identified, of which LDHB was significantly up-regulated in H226Br cell and was further validated using ELISA in sera including 105 lung cancer samples, 41 pneumonia and pulmonary tuberculosis samples and 65 healthy samples. RESULTS: The levels of LDHB were specifically elevated in NSCLC sera [A value 0.485 (0 - 1.415)] compared with pneumonia and pulmonary tuberculosis [A value 0.187 (0 - 0.609), P < 0.01] and healthy group [A value, 0.159 (0 - 0.524), P < 0.01] and were progressively increased with the clinical stage. At the cutoff point 0.260 (A value) on the ROC curve, the sensitivity, specificity and total accuracy of LDHB were 81%, 70% and 76% respectively. CONCLUSION: These findings demonstrated that secretome could open up a possibility to identify novel biomarkers related with cancer occurrence and metastasis.

Protein profile of human lung squamous carcinoma cell line NCI-H226.

OBJECTIVE: To construct a database of human lung squamous carcinoma cell line NCI-H226 and to facilitate discovery of novel subtypes markers of lung cancer. METHOD: Proteomic technique was used to analyze human lung squamous carcinoma cell line NCI-H226. The proteins of the NCI-H226 cells were separated by two-dimensional gel electrophoresis and identified by mass spectrometry. RESULTS: The results showed that a good reproducibility of the 2-D gel pattern was attained. The position deviation of matched spots among three 2-D gels was 1.95 +/- 0.53 mm in the isoelectric focusing direction, and 1.73 +/- 0.45 mm in the sodium dodecyl sulfate-polyacrylamide gel electrophoresis direction. One hundred and twenty-seven proteins, including enzymes, signal transduction proteins, structure proteins, transport proteins, etc. were characterized, of which, 29 identified proteins in NCI-H226 cells were reported for the first time to be involved in lung cancer carcinogenesis. CONCLUSION: The information obtained from this study could provide some valuable clues for further study on the carcinogenetic mechanism of different types of lung cancer, and may help us to discover some potential subtype-specific biomarkers of lung cancer.

Serum amyloid A protein: a potential biomarker correlated with clinical stage of lung cancer.

OBJECTIVE: To identify serum diagnosis or progression biomarkers in patients with lung cancer using protein chip profiling analysis. METHOD: Profiling analysis was performed on 450 sera collected from 213 patients with lung cancer, 19 with pneumonia, 16 with pulmonary tuberculosis, 65 with laryngeal carcinoma, 55 with laryngopharyngeal carcinoma patients, and 82 normal individuals. A new strategy was developed to identify the biomarkers on chip by trypsin pre-digestion. RESULTS: Profiling analysis demonstrated that an 11.6 kDa protein was significantly elevated in lung cancer patients, compared with the control groups (P < 0.001). The level and percentage of 11.6 kDa protein progressively increased with the clinical stages I-IV and were also higher in patients with squamous cell carcinoma than in other subtypes. This biomarker could be decreased after operation or chemotherapy. On the other hand, 11.6 kDa protein was also increased in 50% benign diseases of lung and 13% of other cancer controls. After trypsin pre-digestion, a set of new peptide biomarkers was noticed to appear only in the samples containing a 11.6 kDa peak. Further identification showed that 2177 Da was a fragment of serum amyloid A (SAA, MW 11.6 kDa). Two of the new peaks, 1550 Da and 1611 Da, were defined from the same protein by database searching. This result was further confirmed by partial purification of 11.6 kDa protein and MS analysis. CONCLUSION: SAA is a useful biomarker to monitor the progression of lung cancer and can directly identify some biomarkers on chip.

[Application of serum surface-enhanced laser desorption/ionization proteomic patterns in distinguishing non-small cell lung cancer patients from healthy people].

OBJECTIVE: To explore the application of serum surface-enhanced laser desorption/ionization (SELDI) marker patterns in distinguishing non-small cell lung cancer patients from healthy people by protein chip technology. METHODS: One hundred and sixty-three serum samples (123 patients with lung cancer and 40 healthy persons), were randomly divided into a training set [94 cases, 53 non-small cell lung cancer (NSCLC), 21 small cell lung cancer and 20 healthy persons] and a blinded test set (69 cases), were included for analysis by surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS). Five protein peaks at 11,493, 6,429, 8,245, 5,336 and 2,536 were automatically chosen for the system training and the development of a decision classification tree model (marker pattern). The accuracy of the model was tested with the blinded test set (an independent set of masked serum samples from 49 patients with NSCLC and 20 healthy persons). RESULTS: The model differentiated the patients with NSCLC from the healthy people with a sensitivity of 95.9% (71/74) and a specificity of 90.0% (18/20) in the training set and a sensitivity of 83.7%, and a specificity of 80.0% in the blinded set respectively. CONCLUSION: SELDI-TOF-MS technique can correctly distinguish NSCLC patients from healthy people, and it has the potential for the development of a screening test for the detection of NSCLC.

Application of surface-enhanced laser desorption/ionization time-of-flight-based serum proteomic array technique for the early diagnosis of prostate cancer.

AIM: To identify the serum biomarkers of prostate cancer (PCa) by protein chip and bioinformatics. METHODS: Serum samples from 83 PCa patients and 95 healthy men were taken from a mass screening in Changchun, China. Protein profiling was carried out using surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF MS). The data of spectra were analyzed using two bioinformatics tools. RESULTS: Eighteen serum differential proteins were identified in the PCa group compared with the control group (P < 0.01). There were four proteins at the higher serum level and 14 proteins at the lower serum level in the PCa group. A decision tree classification algorithm that used an eight-protein mass pattern was developed to correctly classify the samples. A sensitivity of 92.0% and a specificity of 96.7% for the study group were obtained by comparing the PCa and control groups. CONCLUSION: We identified new serum biomarkers of PCa. SELDI-TOF MS coupled with a decision tree classification algorithm will provide a highly accurate and innovative approach for the early diagnosis of PCa.

Application of serum SELDI proteomic patterns in diagnosis of lung cancer.

BACKGROUND: Currently, no satisfactory biomarkers are available to screen for lung cancer. Surface-Enhanced Laser Desorption/ionization Time-of-Flight Mass Spectrometry ProteinChip system (SELDI-TOF-MS) is one of the currently used techniques to identify biomarkers for cancers. The aim of this study is to explore the application of serum SELDI proteomic patterns to distinguish lung cancer patients from healthy individuals. METHODS: A total of 208 serum samples, including 158 lung cancer patients and 50 healthy individuals, were randomly divided into a training set (including 11 sera from patients with stages I/II lung cancer, 63 from patients with stages III/IV lung cancer and 20 from healthy controls) and a blinded test set (including 43 sera from patients with stages I/II lung cancer, 41 from patients with stages III/IV lung cancer and 30 from healthy controls). All samples were analyzed by SELDI technology. The spectra were generated on weak cation exchange (WCX2) chips, and protein peaks clustering and classification analyses were made using Ciphergen Biomarker Wizard and Biomarker Pattern software, respectively. We additionally determined Cyfra21-1 and NSE in the 208 serum samples included in this study using an electrochemiluminescent immunoassay. RESULTS: Five protein peaks at 11493, 6429, 8245, 5335 and 2538 Da were automatically chosen as a biomarker pattern in the training set. When the SELDI marker pattern was tested with the blinded test set, it yielded a sensitivity of 86.9%, a specificity of 80.0% and a positive predictive value of 92.4%. The sensitivities provided by Cyfra21-1 and NSE used individually or in combination were significantly lower than that of the SELDI marker pattern (P < 0.005 or 0.05, respectively). Based on the results of the test set, we found that the SELDI marker pattern showed a sensitivity of 91.4% in the detection of non-small cell lung cancers (NSCLC), which was significantly higher than that in the detection of small cell lung cancers (P < 0.05); The pattern also had a sensitivity of 79.1% in the detection of lung cancers in stages I/II. CONCLUSION: These results suggest that serum SELDI protein profiling can distinguish lung cancer patients, especially NSCLC patients, from normal subjects with relatively high sensitivity and specificity, and the SELDI-TOF-MS is a potential tool for the screening of lung cancer.

[Proteomic analysis of prostate cancer using surface enhanced laser desorption/ionization mass spectrometry].

OBJECTIVE: To identify the serum biomarkers of prostate cancer by using protein chip and bioinformatics. METHODS: Eighty three prostate cancer (PCA) patients and ninety five healthy people from mass screen in Changchun were detected by surface-enhanced laser desorption/ionization mass spectrometry (SELDI-MS). The data of spectra were analyzed by bioinformatics tools-Biomarker Wizard and Biomarker Pattern. RESULTS: Compared with the spectra of healthy people, there were 18 potential markers detected in the spectra of the PCA patients, the protein expression was high in 4 of which and low in the 10 of which. The softwares Biomarkerwizard and Biomarker Pattern automatically, under given conditions, selected 8 biomarker proteins to be used to establish a five layer decision tree differentiate to diagnose PCA and differentiate PCA from healthy people with a specificity of 92.632% and a sensitivity of 96.386%. CONCLUSION: New serum biomarkers of PCA have been identified, and this SELDI mass spectrometry coupled with decision tree classification algorithm will provide a highly accurate and innovative approach for the early diagnosis of PCA.

A preliminary analysis of non-small cell lung cancer biomarkers in serum.

OBJECTIVE: To identify potential serum biomarkers that could be used to discriminate lung cancers from normal. METHODS: Proteomic spectra of twenty-eight serum samples from patients with non-small cell lung cancer and twelve from normal individuals were generated by SELDI (Surfaced Enhanced Laser Desorption/Ionization) Mass Spectrometry. Anion-exchange columns were used to fractionate the sera into 6 designated pH groups. Two different types of protein chip arrays, IMAC-Cu and WCX2, were employed. Samples were examined in PBSII Protein Chip Reader (Ciphergen Biosystem Inc) and the discriminatory profiling between cancer and normal samples was analyzed with Biomarker Pattern software. RESULTS: Five distinct potential lung cancer biomarkers with higher sensitivity and specificity were found, with four common biomarkers in both IMAC-Cu and WCX2 chip; the remaining biomarker occurred only in WCX2 chip. Two biomarkers were up-regulated while three biomarkers were down-regulated in the serum samples from patients with non-small cell lung cancer. The sensitivities provided by the individual biomarkers were 75%-96.43% and specificities were 75%-100%. CONCLUSIONS: The preliminary results suggest that serum is a capable resource for detecting specific non-small cell lung cancer biomarkers. SELDI mass spectrometry is a useful tool for the detection and identification of new potential biomarker of non-small cell lung cancer in serum.