蛋白指纹图谱技术对肾透明细胞癌分期和预后判定的临床价值

目的:应用蛋白组指纹图谱技术对不同分期和手术前后的肾透明细胞癌进行血清差异蛋白研究,寻找显著性表达的差异蛋白,以判定肾透明细胞癌的分期和预后。方法:采集肾透明细胞癌血清样本33例,其中术前血清样本24例,男性18例,女性6例,TNM分期:Ⅰa期8例,Ⅰb期7例,Ⅱ期3例,Ⅲ期2例,Ⅳ期4例。肾透明细胞癌术后组:男性5例,女性4例,TNM分期:Ⅰa期3例,Ⅰb期4例,Ⅱ期2例。应用磁珠联合MALDITOF MS技术对肾透明细胞癌血清进行差异蛋白研究,筛选出显著性差异的血清蛋白,并应用遗传算法建立诊断模型。结果:肾透明细胞癌早期组与晚期组之间差异蛋白峰133个,具有显著性差异的蛋白峰2个,分子量为9293.53和4 646.76(P0.05)。利用ClinProTools2.2软件通过遗传算法优化选择,筛选出15个差异蛋白峰建立诊断模型,诊断识别能力为91.67%。肾透明细胞癌术前组与术后组之间差异蛋白峰128个,其中具有显著性差异的蛋白峰10个,分子量分别为3 885.32、7 769.69、2 770.68、5 163.14、9 070.9、3 542.1、5 135.01、4 301.21、4 271.81和7 684.4(P0.05)。通过遗传算法筛选出15个差异蛋白峰建立诊断模型,诊断识别能力为100%。结论:肾透明细胞癌不同分期和手术前后存在显著性差异的血清蛋白,应用遗传算法建立血清差异蛋白的诊断模型,模型识别能力高,有助于肾透明细胞癌的分期和预后的判定。

Clinical value of proteomic fingerprint technique in determining staging and prognosis of renal clear cell carcinoma

Objective： The application of proteomic fingerprint technique in detecting differentially expressed proteins in serum of renal clear cell carcinoma （RCCC） with different stages and before and after operation in order to determine the staging and prognosis of RCCC. Method： Thirty-three eases of serum samples of RCCC were collected, including 24 eases of preoperative RCCC （stage I, eight cases, stage 1 b seven cases, stage II three cases, stage m two cases, stage IV four cases）, and nine cases of postoperative RCCC （stage I, three cases, stage I b four cases, stage II two cases）. Matrix-assisted laser desorption-ionization time of flight mass spectrometry （MALDI-TOF MS） combined with weak cationic exchange （WCX） magnetic beads was applied in detecting differentially expressed proteins in serum of renal clear cell carcinoma to screen out differentially expressed proteins and genetic algorithm was used to establish diagnosis models. Result： One hundred and thirty-three differentially expressed proteins were detected between serums of early group and advanced group, and two proteins were signifi- cantly different （ P d0. 05）. ClinProTools 2.2 software was utilized with genetic algorithm to screen out 15 dif- ferentially expressed proteins to establish a diagnosis model, and the recognition rate was 91.67 %. One hundred and twenty-eight differentially expressed proteins were detected between serums of preoperative group and postop- erative group, and ten proteins were significantly different （ P 〈0.05）. ClinProTools 2.2 software was utilized with genetic algorithm to screen out 15 differentially expressed proteins to establish a diagnosis model, and the recognition rate was 100%. Conclusion： There were significantly expressed proteins in serum of RCCC with different stages and before and after operation. The diagnosis model established by genetic algorithms has high recognition rate, and can help to determine the staging and prognosis of RCCC.