CAJ | 학술논문

目的应用蛋白组学对肾透明细胞癌进行尿液差异蛋白研究,寻找显著性表达的差异蛋白,以提高肾透明细胞癌的检出率. 方法 2010年3-5月收治肾透明细胞癌患者11例,男10例,女1例.年龄46～ 78岁,平均63岁.所有患者手术或穿刺病理均确诊为肾透明细胞癌,临床分期:Ⅰa期3例,Ⅰb期6例,Ⅱ期1例,ⅣV期1例.正常对照组10例,均为男性,年龄25 ～ 32岁,平均29岁.应用磁珠联合基质辅助激光解吸附电离飞行时间质谱(MALDI-TOF MS)技术对两组的尿液进行差异蛋白研究,筛选出肾透明细胞癌的尿液差异蛋白,并应用遗传算法建立诊断模型. 结果筛选出差异蛋白峰160个,具有显著性差异的蛋白峰1个,相对分子质量为2221.71(P=0.0304).利用ClinProTools 2.2软件通过遗传算法优化选择,筛选出符合条件的13个差异蛋白峰建立诊断模型,交叉验证的特异性和敏感性均为100％. 结论应用磁珠联合MALDI-TOF MS技术和遗传算法建立的肾透明细胞癌尿液差异蛋白的诊断模型特异性和敏感性极高,有助于提高肾透明细胞癌的检出率.
목적 응용단백조학대신투명세포암진행뇨액차이단백연구,심조현저성표체적차이단백,이제고신투명세포암적검출솔. 방법 2010년3-5월수치신투명세포암환자11례,남10례,녀1례.년령46～ 78세,평균63세.소유환자수술혹천자병리균학진위신투명세포암,림상분기:Ⅰa기3례,Ⅰb기6례,Ⅱ기1례,ⅣV기1례.정상대조조10례,균위남성,년령25 ～ 32세,평균29세.응용자주연합기질보조격광해흡부전리비행시간질보(MALDI-TOF MS)기술대량조적뇨액진행차이단백연구,사선출신투명세포암적뇨액차이단백,병응용유전산법건립진단모형. 결과 사선출차이단백봉160개,구유현저성차이적단백봉1개,상대분자질량위2221.71(P=0.0304).이용ClinProTools 2.2연건통과유전산법우화선택,사선출부합조건적13개차이단백봉건립진단모형,교차험증적특이성화민감성균위100％. 결론 응용자주연합MALDI-TOF MS기술화유전산법건립적신투명세포암뇨액차이단백적진단모형특이성화민감성겁고,유조우제고신투명세포암적검출솔.
Objective To study the application of proteomics in detecting differentially expressed proteins in renal clear cell carcinoma (RCCC) patient's urine in order to improve the diagnosis rate of RCCC.Methods From Mar.2010 to May.2010,the urine samples of 11 RCCC cases were collected,including 10 males and 1 female with average age of 63 (46-78) years.All patients were finally diagnosed as RCCC by post-operative or biopsy pathology.The normal control urine samples were collected from 10 males with average age of 29 (25-32) years.WCX beads combined with matrix assisted laser desorption ionization time of flighl mass spectrometry (MALDI-TOF MS) technique was applied in detecting differentially expressed proteins in RCCC patient's urine to find out differentially expressed proteins.And genetic algorithm was utilized to establish a diagnosis model.Results 160 differentially expressed proteins in RCCC patient 's urine were detected,and 1 was in significant difference,P=0.0304.ClinProTools 2.2 software was utilized with genetic algorithm to find out 13 differentially expressed proteins to establish a diagnosis model,and the sensitivity and specificity rate was 100％ after cross validation.Conclusions The diagnosis model established by genetic algorithms has high sensitivity and specificity rate,and can improve the diagnosis of RCCC.