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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(P<0.05)。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(P<0.05)。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。
OBJECTIVE: To study differentially expressed proteins in renal cell carcinoma of different stages and before and after surgery by protein fingerprinting, and to identify differentially expressed proteins to determine the staging and prognosis of renal clear cell carcinoma. Methods: 33 cases of renal clear cell carcinoma serum samples were collected, of which 24 cases of preoperative serum samples, 18 males and 6 females, TNM staging: Ⅰ a in 8 cases, Ⅰ b in 7 cases, Ⅱ in 3 cases, Ⅲ in 2 cases , 4 cases of stage Ⅳ. Renal clear cell carcinoma postoperative group: 5 males and 4 females, TNM staging: Ⅰ a 3 cases, Ⅰ b 4 cases, Ⅱ 2 cases. Using magnetic beads combined with MALDITOF MS technology to differentiate renal clear cell carcinoma serum proteins were screened for significant differences in serum proteins and genetic algorithms to establish a diagnostic model. Results There were 133 differentially expressed protein peaks in early stage and late stage of renal clear cell carcinoma. There were 2 peaks with significant difference, with molecular weights of 9293.53 and 4 646.76 (P <0.05). Using ClinProTools2.2 software to optimize selection by genetic algorithm, 15 differential protein peaks were screened out to establish a diagnostic model, the diagnostic recognition ability was 91.67%. There were 128 differential protein peaks between the preoperative group and postoperative group in renal clear cell carcinoma, including 10 protein peaks with significant differences, with molecular weights of 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 and 7 684.4 (P <0.05). Fifteen differential protein peaks were screened by genetic algorithm to establish a diagnostic model with a diagnostic ability of 100%. Conclusions: Serum proteins with significant difference in different stages of renal clear cell carcinoma and before and after surgery are used to establish the diagnostic model of serum differential proteins by using genetic algorithm. The model has high recognition ability and is helpful to determine the stage and prognosis of renal clear cell carcinoma.