A clinical research about predicting the changing of malignant tumor patients serum glucose after chemotherapy by SELDI technology
10.3760/cma.j.issn.1006-9801.2009.10.012
- VernacularTitle:SELDI技术预测恶性肿瘤患者化疗后血糖变化的临床研究
- Author:
Shuqing WEI
;
Qi LI
;
Yi PEI
- Publication Type:Journal Article
- Keywords:
Neoplasms;
Blood glucose;
Spectrometry;
mass;
matrix-assisted laser diesorption-innization
- From:
Cancer Research and Clinic
2009;21(10):683-686
- CountryChina
- Language:Chinese
-
Abstract:
Objective By surface-enhanced laser desorption / ionization time-of-flight mass spectrometry(SELDI-TOF-MS), the serum pmteomic fingerprints related with the changing of malignant tumor patients' serum glucose after chemotherapy was selected and constructed as an predictive model. Methods By SELDI-TOF-MS, the serum of 182 malignant tumor patients who had received chemotherapy were tested, and the pmteomic fingerprints were received. After 2 years follow-up, all the patients were divided into 3 groups: the euglycemia group(136 people), the carbohydrate tolerance abnormality group(27 people), and the diabetes mellitus group (19 people). The proteomic fingerprints were analyzed by Biomarker Wizard Software and the idio-proteomic fingerprint of protective models were constructed by BPS (biomarker pattern software). Results The diagnosis model composed with 2 proteins (M/Z values were 5298 and 9608) could classify the carbohydrate tolerance abnormality group, and the diabetes mellitus group correctly. In the test model, the sensitivity and specificity were 81.48 %(22/27) and 100.00 %(17/17) respectively, the accuracy was 88.64 % (39/44). The diagnosis model composed with 3 proteins (M/Z values were 10324, 2761 and 4084) could classify the diabetes mellitus group and the euglycemia group correctly. In the test model, the sensitivity and specificity were 62.35 %(53/85) and 88.24 %(15/17) respectively, the accuracy was 66.67 %(68/102). The diagnosis model composed with 6 proteins (M/Z values were 5895,6010,6099,3930,5430 and 2495) could classify the diabetes mellitus group and the the carbohydrate tolerance abnormality group correctly. In the test model, the sensitivity and specificity were 77.65 %(66/85) and 96.30 %(26/27) respectively, the accuracy was 82.14 %(92/112). Conclusion SELDI-TOF-MS could be utilized to analyze protein profiling in screening serum glucose changing-related biomarkers and developing diagnostic and predictive patterns, and the developed patterns may be used to predict the changing of serum glucose after chemotherapy in malignant tumor patients.
