OBJECTIVES: To detect the serum proteomic patterns by using SELDI-TOF-MS (surface enhanced laser desorption/ ionization-time of flight-mass spectrometry) technology and CM10 ProteinChip in colorectal cancer (CRC) patients, and to evaluate the significance of the proteomic patterns in the tumour staging of colorectal cancer. METHODS: SELDI-TOF-MS and CM10 ProteinChip were used to detect the serum proteomic patterns of 76 patients with colorectal cancer, among them, 10 Stage I, 19 Stage II, 16 Stage III and 31 Stage IV samples. Different stage models were developed and validated by support vector machines, discriminant analysis and time-sequence analysis. RESULTS: The Model I formed by 6 protein peaks (m/z: 2759.58, 2964.66, 2048.01, 4795.90, 4139.77 and 37761.60) could be used to distinguish local CRC patients (Stage I and Stage II) from regional CRC patients (Stage III) with an accuracy of 86.67% (39/45). The Model II formed by 3 protein peaks (m/z: 6885.30, 2058.32 and 8567.75) could be used to distinguish locoregional CRC patients (Stage I, Stage II and Stage III) from systematic CRC patients (Stage IV) with an accuracy of 75.00% (57/76). The Model III could distinguish Stage I from Stage II with an accuracy of 86.21% (25/29). The Model IV could distinguish Stage I from Stage III with accuracy of 84.62% (22/26). The Model V could distinguish Stage II from Stage III with accuracy of 85.71% (30/35). The Model VI could distinguish Stage II from Stage IV with accuracy of 80.00% (40/50). The Model VII could distinguish Stage III from Stage IV with accuracy of 78.72% (37/47). Different stage groups could be distinguished by the two-dimensional scattered spots figure obviously. CONCLUSION: This method showed great success in preoperatively determining the colorectal cancer stage of patients.
OBJECTIVES: To detect the serum proteomic patterns by using SELDI-TOF-MS (surface enhanced laser desorption/ ionization-time of flight-mass spectrometry) technology and CM10 ProteinChip in colorectal cancer (CRC) patients, and to evaluate the significance of the proteomic patterns in the tumour staging of colorectal cancer. METHODS: SELDI-TOF-MS and CM10 ProteinChip were used to detect the serum proteomic patterns of 76 patients with colorectal cancer, among them, 10 Stage I, 19 Stage II, 16 Stage III and 31 Stage IV samples. Different stage models were developed and validated by support vector machines, discriminant analysis and time-sequence analysis. RESULTS: The Model I formed by 6 protein peaks (m/z: 2759.58, 2964.66, 2048.01, 4795.90, 4139.77 and 37761.60) could be used to distinguish local CRCpatients (Stage I and Stage II) from regional CRCpatients (Stage III) with an accuracy of 86.67% (39/45). The Model II formed by 3 protein peaks (m/z: 6885.30, 2058.32 and 8567.75) could be used to distinguish locoregional CRCpatients (Stage I, Stage II and Stage III) from systematic CRCpatients (Stage IV) with an accuracy of 75.00% (57/76). The Model III could distinguish Stage I from Stage II with an accuracy of 86.21% (25/29). The Model IV could distinguish Stage I from Stage III with accuracy of 84.62% (22/26). The Model V could distinguish Stage II from Stage III with accuracy of 85.71% (30/35). The Model VI could distinguish Stage II from Stage IV with accuracy of 80.00% (40/50). The Model VII could distinguish Stage III from Stage IV with accuracy of 78.72% (37/47). Different stage groups could be distinguished by the two-dimensional scattered spots figure obviously. CONCLUSION: This method showed great success in preoperatively determining the colorectal cancer stage of patients.
Authors: Haleem J Issaq; Timothy D Veenstra; Thomas P Conrads; Donna Felschow Journal: Biochem Biophys Res Commun Date: 2002-04-05 Impact factor: 3.575
Authors: O John Semmes; Ziding Feng; Bao-Ling Adam; Lionel L Banez; William L Bigbee; David Campos; Lisa H Cazares; Daniel W Chan; William E Grizzle; Elzbieta Izbicka; Jacob Kagan; Gunjan Malik; Dale McLerran; Judd W Moul; Alan Partin; Premkala Prasanna; Jason Rosenzweig; Lori J Sokoll; Shiv Srivastava; Sudhir Srivastava; Ian Thompson; Manda J Welsh; Nicole White; Marcy Winget; Yutaka Yasui; Zhen Zhang; Liu Zhu Journal: Clin Chem Date: 2005-01 Impact factor: 8.327
Authors: L A Ries; P A Wingo; D S Miller; H L Howe; H K Weir; H M Rosenberg; S W Vernon; K Cronin; B K Edwards Journal: Cancer Date: 2000-05-15 Impact factor: 6.860