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Objective To assess the efficacy of using four kinds of proteins / peptides to distinguish the tuberculosis patients from healthy people. Methods A, B, C and D were used to represent four proteins /peptides with 1 060, 1 944, 2 081 and 3 954 of mass to charge ratio (m / z) in serum, respectively. Levels A, B, C and D in serum of 57 patients with tuberculosis and 30 healthy people were determined by using the surface-enhanced laser desorption-ionization time of flight mass spectrometry (SELDI-TOF-MS). Then the differences of levels of f A, B, C and D were anlyzed between tuberculosis patients and healthy people. The efficacy of distinguishing tuberculosis patient from healthy people were evaluated by using diagnostic test evaluation method. Results (1) The levels of A, B, C and D were 1 ± 11, 1 597 ± 3 102, 460 ± 765 and 1 208 ± 1 003 in tuberculosis patients, while they were 123 ± 201, 47 ± 98, 36 ± 93 and 397 ± 355 in healthy people. (2) The area under the receiver operator characteristic (ROC) curve was 0.644, 0.848, 0.735 and 0.810 respectively. The serum levels of A, B, C and D could be used to distinguish tuberculosis patient from healthy people and the cut-off values of A, B, C and D were ≤166, ≥318, ≥48 and ≥728, respectively. Conclusions B, C and D have better performances to distinguish tuberculosis patients from healthy people , which may be regarded as new promising candidate markers for diagnosis of tuberculosis.
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Objective To screen and identify the serum biomarker of anovulatory dysfunctional uterine bleeding (ADUB) , to determine the expression of biomarker protein in menses of ADUB pa-tients, and to investigate the relation between ADUB and the biomarker proteins. Methods Subjects included 128 ADUB patients and 93 age-matched controls( normal women ). Their serum and super-natant of mense were collected and stored for use at -80℃. The differential proteins in the serum of the 2 groups were detected by CM 10 and analyzed by Biomarker WizardTM3.2 software. Then, the differential proteins were identified by Trieine-SDS-PAGE gel separation, spectrometry identifica-tion, and immunoprecipitation. The expression of the protein identified above in the menses was test-ed by ELISA, RT-PCR, and Western blotting. SPSS 14.1 was applied for statistical analysis and chart drawing. Results Five differential protein peaks were screened and their peak values were 11.80, 13.59, 13.79, 13.85, and 14.20 km/z, respectively. The intensity of protein peak ( 11.80 km/z ) which was identified as serum amyploid protein A ( SAA ) of ADUB was significantly higher than that of the controls (P<0.05). While the intensity of protein peak (13.59 km/z) which was identified as vascular endothelial growth factor (VEGF) of ADUB was obviously lower than that of the controls (P<0.05). The intensity of protein peak 13.08, 13.85, and 14.20 was not different between the cases and controls. SAA expressed highly in the menses of ADUB but low in that of the controls. Conversely, VEGF expressed highly in the menses of the control but low in that of the ADUB. Conclusion Two biomarkers which might be related with ADUB have been correctly screened and identified as SAA and VEGF. It needs further study whether the increased expression of SAA and reduced expression of VEGF are the cause or result of ADUB.
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Objective To study the difference of proteomic spectra in serum of patients with colorectal cancer(CRC) in order to build a proteomic pattern and find a method for early diagnosis of CRC.Methods We screened for potential tumor biomarkers of serum samples from 48 CRC patients and 34 healthy subjects by using CM10(Ciphergen Company,USA) and the technology of Surface enhanced laser desorption/ionization time-of-flight mass spectrometry(SELDI-TOF-MS).Using Ciphergen Protein Chipsoftware 5.1,a proteomic pattern was constructed.The constructed pattern was then tested by an independent set of masked serum samples from 33 colorectal cancer patients and 34 healthy subjects.Results(1) The contents of 27 proteins in the CRC to healthy subjects groups were significantly different(P
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Objective To search for the biomarker used to determine gastric cancer by the application of protein mass spectrometry analysis. Methods The relative contents of serum proteins were detected of 38 patients with gastric cancer and 82 healthy people by IMAC3 (CipherGen Inc.) chip and proteinchip. Results At the M/Z values range from 1 723Da to 14 048Da, 18 kind of protein contents are obviously different between the two groups. In the learning mode, all the 120 testers were correctly distinguished, both the sensitivity and specificity reached to 100%. While in the test mode, 31 patients and 81 control people were correctly distinguished, the accuracy were 81 6%(31/38) and 98 8%(81/82), respectively. Conclusion Gastric cancer can be quickly and exactly diagnosed by this method with high sensitivity and specificity. That will be widely used in clinical application
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Objective To establish a serum protein fingerprinting technique with a pattern matching algorithm to distinguish Dukes A stage from colorectal cancer of Dukes B,C,D stage. Methods Serum samples were collected from both reserved group and test group, each of the groups comprised 10 patients of colorectal cancer in Dukes A stage and 68 patients in Dukes B,C,D stage. The sera of the reserved group was combined with the surface of the IMAC3 proteinchip. Then the data of SELDI TOF MS was read and analyzed by BioMarker Wizard software and BioMarker Pattern software to get a classificatory pedigree tree, which is a standard configuration that can distinguish the sera of colorectal cancer patients of Dukes A from colorectal cancer patients of Dukes B,C,D, and the standard was confirmed by double blind test in the test group. Results At the M/Z values of 8 320 Da, 8 604Da, 8 867Da and 15 872Da, the protein contents in reserved group are obviously different between the two classes of patients. The accuracy of classification was 97 4%(76/78), corresponding sensitivity was 100%(10/10) and corresponding specificity was 97 1%(66/68). By double blind examination in test group, the corresponding accuracy was 100%(10/10), the corresponding sensitivity was 94 1%(64/68) and the corresponding specificity was 94 1%(64/68). Conclusion Colorectal cancer of Dukes A can be quickly and exactly diagnosed by this method with high sensitivity and specificity. That will be widely used in clinical application
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Objective To approach the significance of serum protein fingerprinting on the identification of different courses of acute myocardial infarction. Methods For 153 patients of acute myocardial infarction, including 45 cases of just admitted patients, 12 cases of 3h admitted patients, 22 cases of 6h admitted patients, 24 cases of 9h admitted patients, 24 cases of 12h admitted patients, 16 cases of 24h admitted patients and 10 cases of 48h admitted patients. The relative contents of serum proteins were detected by IMAC3 chip and proteinchip reader (CipherGen Inc., VS). Results On the M/Z values ranged from 4KDa to 12KDa, ten protein contents were obviously different between the different courses of acute myocardial infarction. All the patients in different courses were diagnosed correctly, the accuracy was 100%(153/153). Both sensitivity and specificity were also 100% in learning mode. Conclusion Different courses of acute myocardial infarction patients can be quickly and correctly diagnosed by this method with high sensitivity and specificity. That will be widely used in clinical application
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Objective To approach the feasibility of identifying the specific biomarker of ovarian cancer by SELDI TOF mass spectrometry. Methods The relative contents of serum protein of both 24 the patients with ovarian cancer and 56 cases of healthy people were tested by IMAC3 chip and proteinchip reader (CipherGen Inc., VS). Results On the M/Z values ranged from 4000Da to 10000Da, there were six kinds of protein contents in the serum of the were obviously different between the two groups. Among them the serum protein of M/Z value 4472Da may be regarded as a specific biomarker of ovarian cancer. In the learning mode, all the 24 patients and 56 control people were diagnosed and distinguished out correctly, while in the test mode, 23 patients were correctly diagnosed and 56 control people were distinguished out, the total accuracy was 98 75%(79/80), and the sensitivity and specificity were 95 8%(23/24) and 100%(56/56), respectively. Conclusion Ovarian cancer can be quickly and correctly diagnosed by this method with high sensitivity and specificity. That will be widely used in clinical application