AIM： To identify a multi serum protein pattern as well as single protein markers using surface-enhanced laser desorption/ionisation time-of-flight mass spectrometry （SELDI-TOF-MS） for detection and differentiation ...AIM： To identify a multi serum protein pattern as well as single protein markers using surface-enhanced laser desorption/ionisation time-of-flight mass spectrometry （SELDI-TOF-MS） for detection and differentiation of liver fibrosis （F1-F2）, liver cirrhosis （F4） and hepatocellular carcinoma （HCC） in patients with chronic hepatitis C virus （HCV）. METHODS： Serum samples of 39 patients with F1/F2 fibrosis, 44 patients with F4 fibrosis, 34 patients with HCC were applied to CM10 arrays and analyzed using the SELDI-TOF ProteinChip System （PBS-Ⅱc; Ciphergen Biosystems） after anion-exchange fractionation. All patients had chronic hepatitis C and histologically confirmed fibrosis stage/HCC. Data were analyzed for protein patterns by multivariate statistical techniques and artificial neural networks. RESULTS： A 4 peptide/protein multimarker panel （7486, 12843, 44293 and 53598 Da） correctly identified HCCs with a sensitivity of 100% and specificity of 85% in a two way-comparison of HCV-cirrhosis versus HCV-HCC training samples （AUROC 0.943）. Sensitivity and specificity for identification of HCC were 68% and 80% for random test samples. Cirrhotic patients could be discriminated against patients with F1 or F2 fibrosis using a 5 peptide/protein multimarker pattern （2873, 6646, 7775, 10525 and 67867 Da） with a specificity of 100% and a sensitivity of 85% in training samples （AUROC 0.976） and a sensitivity and specificity of 80% and 67% for random test samples. Combination of the biomarker classifiers with APR/score and alfa-fetopotein （AFP） improved the diagnostic performance. The 6646 Da marker protein for liver fibrosis was identified as apolipoprotein C-I. CONCLUSION： SELDI-TOF-MS technology combined with protein pattern analysis seems a valuable approach for the identification of liver cirrhosis and hepatocellular carcinoma in patients with chronic hepatitis C. Host probably a combination of different serum markers will help to identify liver cirrhosis and early-stage hepatocellular carcinomas in the future.展开更多
AIM： To detect the new serum biomarkers for colorectal cancer （CRC） by serum protein profiling with surfaceenhanced laser desorption ionisation - time of flight mass spectrometry （SELDI-TOF MS）. METHODS： Two ind...AIM： To detect the new serum biomarkers for colorectal cancer （CRC） by serum protein profiling with surfaceenhanced laser desorption ionisation - time of flight mass spectrometry （SELDI-TOF MS）. METHODS： Two independent serum sample sets were analysed separately with the ProteinChip technology （set A： 40 CRC ＋ 49 healthy controls; set B： 37 CRC ＋ 31 healthy controls）, using chips with a weak cation exchange moiety and buffer pH 5. Discriminative power of differentially expressed proteins was assessed with a classification tree algorithm. Sensitivities and specificities of the generated classification trees were obtained by blindly applying data from set A to the generated trees from set B and vice versa. CRC serum protein profiles were also compared with those from breast, ovarian, prostate, and non-small cell lung cancer. RESULTS： Mass-to-charge ratios （m/z） 3.1×10^3, 3.3× 10^3, 4.5×10^3, 6.6×10^3 and 28×10^3 were used as classitiers in the best-performing classification trees. Tree sensitivities and specificities were between 65% and 90%.Host of these discriminative m/z values were also different in the other tumour types investigated. M/z 3.3× 10^3, main classifier in most trees, was a doubly charged form of the 6.6× 10^3-Da protein. The latter was identified as apolipoprotein C-I. M/z 3.1×10^3 was identified as an N-terminal fragment of albumin, and m/z 28× 10^3 as apolipoprotein A-I. CONCLUSION： SELDI-TOF MS followed by classification tree pattern analysis is a suitable technique for finding new serum markers for CRC. Biomarkers can be identified and reproducibly detected in independent sample sets with high sensitivities and specificities. Although not specific for CRC, these biomarkers have a potential role in disease and treatment monitoring.展开更多
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 ...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 11.6kDa protein was significandy elevated in lung cancer patients, compared with the control groups （P〈0.001）. The level and percentage of 11.6kDa 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, 11.6kDa 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 11.6kDa peak. Further identification showed that 2177Da was a fragment of serum amyloid A （SAA, MW 11.6kDa）. Two of the new peaks, 1550Da and 1611Da, were defined from the same protein by database searching. This result was further confirmed by partial purification of 11.6kDa 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.展开更多
基金Supported by a research grant of the Jurgen Manchot Stiftung
文摘AIM： To identify a multi serum protein pattern as well as single protein markers using surface-enhanced laser desorption/ionisation time-of-flight mass spectrometry （SELDI-TOF-MS） for detection and differentiation of liver fibrosis （F1-F2）, liver cirrhosis （F4） and hepatocellular carcinoma （HCC） in patients with chronic hepatitis C virus （HCV）. METHODS： Serum samples of 39 patients with F1/F2 fibrosis, 44 patients with F4 fibrosis, 34 patients with HCC were applied to CM10 arrays and analyzed using the SELDI-TOF ProteinChip System （PBS-Ⅱc; Ciphergen Biosystems） after anion-exchange fractionation. All patients had chronic hepatitis C and histologically confirmed fibrosis stage/HCC. Data were analyzed for protein patterns by multivariate statistical techniques and artificial neural networks. RESULTS： A 4 peptide/protein multimarker panel （7486, 12843, 44293 and 53598 Da） correctly identified HCCs with a sensitivity of 100% and specificity of 85% in a two way-comparison of HCV-cirrhosis versus HCV-HCC training samples （AUROC 0.943）. Sensitivity and specificity for identification of HCC were 68% and 80% for random test samples. Cirrhotic patients could be discriminated against patients with F1 or F2 fibrosis using a 5 peptide/protein multimarker pattern （2873, 6646, 7775, 10525 and 67867 Da） with a specificity of 100% and a sensitivity of 85% in training samples （AUROC 0.976） and a sensitivity and specificity of 80% and 67% for random test samples. Combination of the biomarker classifiers with APR/score and alfa-fetopotein （AFP） improved the diagnostic performance. The 6646 Da marker protein for liver fibrosis was identified as apolipoprotein C-I. CONCLUSION： SELDI-TOF-MS technology combined with protein pattern analysis seems a valuable approach for the identification of liver cirrhosis and hepatocellular carcinoma in patients with chronic hepatitis C. Host probably a combination of different serum markers will help to identify liver cirrhosis and early-stage hepatocellular carcinomas in the future.
文摘AIM： To detect the new serum biomarkers for colorectal cancer （CRC） by serum protein profiling with surfaceenhanced laser desorption ionisation - time of flight mass spectrometry （SELDI-TOF MS）. METHODS： Two independent serum sample sets were analysed separately with the ProteinChip technology （set A： 40 CRC ＋ 49 healthy controls; set B： 37 CRC ＋ 31 healthy controls）, using chips with a weak cation exchange moiety and buffer pH 5. Discriminative power of differentially expressed proteins was assessed with a classification tree algorithm. Sensitivities and specificities of the generated classification trees were obtained by blindly applying data from set A to the generated trees from set B and vice versa. CRC serum protein profiles were also compared with those from breast, ovarian, prostate, and non-small cell lung cancer. RESULTS： Mass-to-charge ratios （m/z） 3.1×10^3, 3.3× 10^3, 4.5×10^3, 6.6×10^3 and 28×10^3 were used as classitiers in the best-performing classification trees. Tree sensitivities and specificities were between 65% and 90%.Host of these discriminative m/z values were also different in the other tumour types investigated. M/z 3.3× 10^3, main classifier in most trees, was a doubly charged form of the 6.6× 10^3-Da protein. The latter was identified as apolipoprotein C-I. M/z 3.1×10^3 was identified as an N-terminal fragment of albumin, and m/z 28× 10^3 as apolipoprotein A-I. CONCLUSION： SELDI-TOF MS followed by classification tree pattern analysis is a suitable technique for finding new serum markers for CRC. Biomarkers can be identified and reproducibly detected in independent sample sets with high sensitivities and specificities. Although not specific for CRC, these biomarkers have a potential role in disease and treatment monitoring.
基金This work was supported by the National Natural Science Foundation of China (Grant No.30370712)Beijing Key Project (Grant No. 7051002)+1 种基金 Beijing Science Technology Committee Project (No.Y0204002040111)a grant of Majon State Basic Research Program of China (No. 2006CB 910100).
文摘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 11.6kDa protein was significandy elevated in lung cancer patients, compared with the control groups （P〈0.001）. The level and percentage of 11.6kDa 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, 11.6kDa 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 11.6kDa peak. Further identification showed that 2177Da was a fragment of serum amyloid A （SAA, MW 11.6kDa）. Two of the new peaks, 1550Da and 1611Da, were defined from the same protein by database searching. This result was further confirmed by partial purification of 11.6kDa 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.
