Preoperative molecular staging of colorectal cancers by CM10 ProteinChip and SELDI-TOF-MS analysis / 中华肿瘤杂志

<p><b>OBJECTIVE</b>To detect the serum proteomic patterns by using SELDI-TOF-MS and CM10 ProteinChip techniques in colorectal cancer (CRC) patients, and to evaluate the significance of the proteomic patterns in colorectal cancer staging.</p><p><b>METHODS</b>A total of 76 serum samples were obtained from CRC patients at different clinical stages, including Dukes A (n = 10), Dukes B (n = 19), Dukes C (n = 16) and Dukes D (n = 31). Different stage models were developed and validated by bioinformatics methods of support vector machines, discriminant analysis and time-sequence analysis.</p><p><b>RESULTS</b>The model I formed by six proteins of peaks at m/z 2759.6, 2964.7, 2048.0, 4795.9, 4139.8 and 37 761.6 could do the best as potential biomarkers to distinguish local CRC patients (Dukes A and Dukes B) from regional CRC patients (Dukes C ) with an accuracy of 86.7%. The model II formed by 3 proteins of peaks at m/z 6885.3, 2058.3 and 8567.8 could do the best to distinguish locoregional CRC patients (Dukes A, B and C) from systematic CRC patients (Dukes D) with an accuracy of 75.0%. The mode III could distinguish Dukes A from Dukes B with an accuracy of 86.2% (25/29). The model IV could distinguish Dukes A from Dukes C with an accuracy of 84.6% (22/26). The model V could distinguish Dukes B from Dukes C with an accuracy of 85.7% (30/35). The model VI could distinguish Dukes B from Dukes D with an accuracy of 80.0% (40/50). The model VII could distinguish Dukes C from Dukes D with an accuracy of 78.7% (37/47). Different stage groups could be distinguished by the two-dimensional scattered spots figure obviously.</p><p><b>CONCLUSION</b>Our findings indicate that this method can well be used in preoperative staging of colorectal cancers and the screened tumor markers may serve for guidance of integrating treatment of colorectal cancers.</p>

Preoperatively molecular staging with CM10 ProteinChip and SELDI-TOF-MS for colorectal cancer patients / 浙江大学学报（英文版）（B辑：生物医学和生物技术）

<p><b>OBJECTIVES</b>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.</p><p><b>METHODS</b>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.</p><p><b>RESULTS</b>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.</p><p><b>CONCLUSION</b>This method showed great success in preoperatively determining the colorectal cancer stage of patients.</p>

The optimal combination of serum tumor markers with bioinformatics in diagnosis of colorectal carcinoma / 浙江大学学报·医学版

<p><b>OBJECTIVE</b>To identify the optimal combination of serum tumor markers with bioinformatics in diagnosis of colorectal cancer.</p><p><b>METHODS</b>The serum levels of CEA, AFP, NSE, CA199, CA242, CA724, CA211 and TPA were detected in 128 patients with colorectal carcinoma and 113 health subjects. The serum tumor markers were evaluated with the area under curves. The optimal combination of serum tumor markers was selected and the diagnostic model with artificial neural network was established.</p><p><b>RESULTS</b>CEA, CA199, CA242, CA211, CA724 were selected for the optimal combination and the artificial neural network was built. The model was evaluated by a 5-cross validation approach. The model had a specificity of 95%, sensitivity of 83% and positive predictive value of 95% in diagnosis of colorectal carcinoma.</p><p><b>CONCLUSION</b>The combination of optimal serum tumor markers has a high sensitivity and specificity in diagnosis of colorectal carcinoma.</p>