肿瘤研究与临床
腫瘤研究與臨床
종류연구여림상
CANCER RESEARCH AND CLINIC
2014年
9期
601-604,607
,共5页
肺肿瘤%肿瘤标志%光谱法,质量,基质辅助激光解吸电离
肺腫瘤%腫瘤標誌%光譜法,質量,基質輔助激光解吸電離
폐종류%종류표지%광보법,질량,기질보조격광해흡전리
Lung neoplasms%Tumor biomakers%Spectrometry,mass,matrix-assisted laser de sorption-ionization
目的 探讨用表面增强激光解吸电离飞行时间质谱技术(SELDI-TOF-MS)筛查肺癌血清特异性蛋白质的临床意义.方法 应用CM-10蛋白质芯片对肺鳞状细胞癌38例,肺腺癌30例,健康对照组50例的血清样品进行蛋白质指纹图谱测定,其中34例肺癌患者进行术前和术后对比检测,用BioMarker Wizard及BioMarker pattern system分析软件对所得的数据进行处理并建立诊断模型.结果 共检测到167个蛋白质峰,筛选出2个肺癌特异蛋白质峰[质荷比(M/Z)分别为6 010、5 330]建立了诊断模型.模型的原始判别敏感度为100％,特异度为100％.交叉验证敏感度为98％,特异度为96％.肺癌术前和术后有9个差异蛋白质(P＜0.05),转移和无转移肺癌患者血清有8个差异蛋白质(P＜0.05).结论 SELDI技术在肺癌的诊断中具有较高的敏感性和特异性,可能成为一种有效的筛查手段.
目的 探討用錶麵增彊激光解吸電離飛行時間質譜技術(SELDI-TOF-MS)篩查肺癌血清特異性蛋白質的臨床意義.方法 應用CM-10蛋白質芯片對肺鱗狀細胞癌38例,肺腺癌30例,健康對照組50例的血清樣品進行蛋白質指紋圖譜測定,其中34例肺癌患者進行術前和術後對比檢測,用BioMarker Wizard及BioMarker pattern system分析軟件對所得的數據進行處理併建立診斷模型.結果 共檢測到167箇蛋白質峰,篩選齣2箇肺癌特異蛋白質峰[質荷比(M/Z)分彆為6 010、5 330]建立瞭診斷模型.模型的原始判彆敏感度為100％,特異度為100％.交扠驗證敏感度為98％,特異度為96％.肺癌術前和術後有9箇差異蛋白質(P＜0.05),轉移和無轉移肺癌患者血清有8箇差異蛋白質(P＜0.05).結論 SELDI技術在肺癌的診斷中具有較高的敏感性和特異性,可能成為一種有效的篩查手段.
목적 탐토용표면증강격광해흡전리비행시간질보기술(SELDI-TOF-MS)사사폐암혈청특이성단백질적림상의의.방법 응용CM-10단백질심편대폐린상세포암38례,폐선암30례,건강대조조50례적혈청양품진행단백질지문도보측정,기중34례폐암환자진행술전화술후대비검측,용BioMarker Wizard급BioMarker pattern system분석연건대소득적수거진행처리병건립진단모형.결과 공검측도167개단백질봉,사선출2개폐암특이단백질봉[질하비(M/Z)분별위6 010、5 330]건립료진단모형.모형적원시판별민감도위100％,특이도위100％.교차험증민감도위98％,특이도위96％.폐암술전화술후유9개차이단백질(P＜0.05),전이화무전이폐암환자혈청유8개차이단백질(P＜0.05).결론 SELDI기술재폐암적진단중구유교고적민감성화특이성,가능성위일충유효적사사수단.
Objective To investigate the serum proteomic patterns in lung cancer by surface enhanced laser desorption/ionization-time of flight-mass spectrometry (SELDI-TOF-MS) techniques and build diagnostic models in order to evaluate their clinical significance by biomarkers screening in lung cancer.Methods SELDI-TOF-MS and CM-10 protein chip were used to detect the serum proteomic patterns of 38 lung squamous cell carcinoma and 30 lung adenocarcinomas,including the comparation in 34 geminate patient serums before and after surgery.Nagative control setted as a group of 50 normal tissues.BioMarker Wizard and BioMarker pattern system software were used in combination to analyze the data and to develop diagnostic models.Results Two protein peaks from a total of 167 were chosen as a biomarker pattern in the training set.Two protein peaks pattern (mass/charge ratio [m/z] 6 010,5 330) was observed in the model that could be used as to distinguish lung cancer from non-cancerous diseases.It yielded a sensitivity of 98 ％ and a specificity of 96 ％.There were 9 different protein peaks (P ＜ 0.05) between pre-surgery and post-surgery.There were 8 different peaks (P ＜ 0.05) between metastasis and non-metastasis.Conclusion SELDI techniques can be employed as diagnosis in lung cancer patients with relatively high sensitivity and specificity,and also could be used as an effective tool for the screening of lung cancer.
