肿瘤研究与临床
腫瘤研究與臨床
종류연구여림상
CANCER RESEARCH AND CLINIC
2009年
10期
683-686
,共4页
肿瘤%血糖%光谱法%质量%基质辅助激光解吸电离
腫瘤%血糖%光譜法%質量%基質輔助激光解吸電離
종류%혈당%광보법%질량%기질보조격광해흡전리
Neoplasms%Blood glucose%Spectrometry%mass%matrix-assisted laser diesorption-innization
目的 应用弱阳离子芯片结合表面增强飞行时问质谱(SELDI-TOF-MS)技术筛选与恶性肿瘤化疗后血糖变化情况、相关的血清蛋白质组指纹并建立预测模型.方法 应用SELDI-TOF-MS、CM10蛋白质芯片技术检测182例恶性肿瘤患者化疗前血清样本的蛋白质谱,经过2年随访按化疗后的血糖情况分为血糖正常组(136例)、糖耐量异常组(27例)和糖尿病组(19例),利用Biomarker Wizard 软件回顾性地分析比较各组间化疗前的血清蛋白质指纹图潜,Biomarker Pattern软件建市预测模型.结果 M/Z为5298和9608的两个蛋白质组成的诊断模型可将患者在化疗前准确分为糖尿病组与糖耐量异常组,灵敏度和特异度分别为81.481%(22/27)和100.00%(17/17),准确度为88.64%(39/44);M/Z为10324、2761和4084的3个蛋向质组成的诊断模型可将糖尿病组与血糖正常组准确分组,灵敏度和特异度分别为62.35%(53/85)和88.24%(15/17),准确度为66.67%(68/102);M/Z为5895、6010、6099、3930、5430和2495的6个蛋白质组成的诊断模型可将糖耐最异常组与血糖正常组准确分组,灵敏度和特异度分别为77.65%(66/85)和96.30%(26/27),准确度为82.14%(92/112).结论 SELDI-TOF-MS技术筛选出恶性肿瘤化疗后3组血糖情况的化疗前的蛋白质指纹,建立血糖正常组、糖耐量异常组和糖尿病组的诊断模型,用于肿瘤患者化疗后血糖变化的早期预测.
目的 應用弱暘離子芯片結閤錶麵增彊飛行時問質譜(SELDI-TOF-MS)技術篩選與噁性腫瘤化療後血糖變化情況、相關的血清蛋白質組指紋併建立預測模型.方法 應用SELDI-TOF-MS、CM10蛋白質芯片技術檢測182例噁性腫瘤患者化療前血清樣本的蛋白質譜,經過2年隨訪按化療後的血糖情況分為血糖正常組(136例)、糖耐量異常組(27例)和糖尿病組(19例),利用Biomarker Wizard 軟件迴顧性地分析比較各組間化療前的血清蛋白質指紋圖潛,Biomarker Pattern軟件建市預測模型.結果 M/Z為5298和9608的兩箇蛋白質組成的診斷模型可將患者在化療前準確分為糖尿病組與糖耐量異常組,靈敏度和特異度分彆為81.481%(22/27)和100.00%(17/17),準確度為88.64%(39/44);M/Z為10324、2761和4084的3箇蛋嚮質組成的診斷模型可將糖尿病組與血糖正常組準確分組,靈敏度和特異度分彆為62.35%(53/85)和88.24%(15/17),準確度為66.67%(68/102);M/Z為5895、6010、6099、3930、5430和2495的6箇蛋白質組成的診斷模型可將糖耐最異常組與血糖正常組準確分組,靈敏度和特異度分彆為77.65%(66/85)和96.30%(26/27),準確度為82.14%(92/112).結論 SELDI-TOF-MS技術篩選齣噁性腫瘤化療後3組血糖情況的化療前的蛋白質指紋,建立血糖正常組、糖耐量異常組和糖尿病組的診斷模型,用于腫瘤患者化療後血糖變化的早期預測.
목적 응용약양리자심편결합표면증강비행시문질보(SELDI-TOF-MS)기술사선여악성종류화료후혈당변화정황、상관적혈청단백질조지문병건립예측모형.방법 응용SELDI-TOF-MS、CM10단백질심편기술검측182례악성종류환자화료전혈청양본적단백질보,경과2년수방안화료후적혈당정황분위혈당정상조(136례)、당내량이상조(27례)화당뇨병조(19례),이용Biomarker Wizard 연건회고성지분석비교각조간화료전적혈청단백질지문도잠,Biomarker Pattern연건건시예측모형.결과 M/Z위5298화9608적량개단백질조성적진단모형가장환자재화료전준학분위당뇨병조여당내량이상조,령민도화특이도분별위81.481%(22/27)화100.00%(17/17),준학도위88.64%(39/44);M/Z위10324、2761화4084적3개단향질조성적진단모형가장당뇨병조여혈당정상조준학분조,령민도화특이도분별위62.35%(53/85)화88.24%(15/17),준학도위66.67%(68/102);M/Z위5895、6010、6099、3930、5430화2495적6개단백질조성적진단모형가장당내최이상조여혈당정상조준학분조,령민도화특이도분별위77.65%(66/85)화96.30%(26/27),준학도위82.14%(92/112).결론 SELDI-TOF-MS기술사선출악성종류화료후3조혈당정황적화료전적단백질지문,건립혈당정상조、당내량이상조화당뇨병조적진단모형,용우종류환자화료후혈당변화적조기예측.
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.
