AIM: The purpose of this study is to develop a proteomic pattern for distinguishing individuals with colorectal cancer from healthy controls and monitoring micrometastasis using SELDI-TOF-MS. METHODS: A training set consisting of 63 patients with colorectal cancer, 20 patients with benign colorectal diseases and 26 healthy volunteers was used to develop a proteomic model that discriminated colorectal cancer effectively. The sensitivity and specificity of this model was validated by an independent test set. To explore serum proteins changed after operation, the protein profiles of 31 postoperative patients were compared with those of preoperative patients. We also analyzed protein profiles of patients with and without metastasis to monitor micrometastasis. RESULTS: Our study yielded a four-peak model (m/z: 3191.5, 3262.9, 3396.3 and 5334.4) that discriminated cancer from non-cancer samples with sensitivity of 90.3% and specificity of 95.7%. This model was validated in the test set with sensitivity of 87.5% and specificity of 93.8% which was significantly better than the combination use of CEA, CA199 and CA242 (sensitivity 62.4%) for early detection of colorectal cancer. Two peaks (m/z: 2753.8 and 4172.4) were found down-regulated in postoperative samples comparing with preoperative samples. We also detected two proteins (m/z: 9184.4 and 9340.9) that can discriminate patients with primary colorectal cancer from metastatic colorectal cancer. CONCLUSIONS: The four-peak model and two peaks (m/z: 2753.8 and 4172.4) detected in this study have the potential for assistance in diagnostics and therapeutic strategies in colorectal cancer and the two proteins (m/z: 9184.4 and 9340.9) were effective biomarkers for monitoring micrometastasis.
AIM: The purpose of this study is to develop a proteomic pattern for distinguishing individuals with colorectal cancer from healthy controls and monitoring micrometastasis using SELDI-TOF-MS. METHODS: A training set consisting of 63 patients with colorectal cancer, 20 patients with benign colorectal diseases and 26 healthy volunteers was used to develop a proteomic model that discriminated colorectal cancer effectively. The sensitivity and specificity of this model was validated by an independent test set. To explore serum proteins changed after operation, the protein profiles of 31 postoperative patients were compared with those of preoperative patients. We also analyzed protein profiles of patients with and without metastasis to monitor micrometastasis. RESULTS: Our study yielded a four-peak model (m/z: 3191.5, 3262.9, 3396.3 and 5334.4) that discriminated cancer from non-cancer samples with sensitivity of 90.3% and specificity of 95.7%. This model was validated in the test set with sensitivity of 87.5% and specificity of 93.8% which was significantly better than the combination use of CEA, CA199 and CA242 (sensitivity 62.4%) for early detection of colorectal cancer. Two peaks (m/z: 2753.8 and 4172.4) were found down-regulated in postoperative samples comparing with preoperative samples. We also detected two proteins (m/z: 9184.4 and 9340.9) that can discriminate patients with primary colorectal cancer from metastatic colorectal cancer. CONCLUSIONS: The four-peak model and two peaks (m/z: 2753.8 and 4172.4) detected in this study have the potential for assistance in diagnostics and therapeutic strategies in colorectal cancer and the two proteins (m/z: 9184.4 and 9340.9) were effective biomarkers for monitoring micrometastasis.
Authors: Christopher C L Liao; Anuja Mehta; Nicholas J Ward; Simon Marsh; Tan Arulampalam; John D Norton Journal: World J Surg Oncol Date: 2010-04-26 Impact factor: 2.754