基于蛋白质芯片的慢性肾衰竭舌苔透明上清液中蛋白质的研究

Cheng Yawei , He Lei , Liao Ping , Hu Heng , Jin Yaming , Li Fufeng , Wang Wenjing , Wang Yiqin , Hao Yiming
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摘要

本研究旨在筛选与慢性肾功能衰竭(CRF)相关的舌苔蛋白标志物,通过比较CRF患者与正常人舌苔透明上清液蛋白谱表达的差异,建立预测模型,探讨舌苔透明上清液蛋白在慢性肾功能衰竭(CRF)诊断中的意义。研究对象为67例慢性口疮患者和38例正常人的舌苔清液。采用SELDI-TOF-MS技术根据CRF筛选舌苔蛋白标记物。建立了预测模型,并通过生物信息学分析进行了验证。采用SELDI-TOF-MS技术对67例CRF患者和38例正常对照组的舌膜样本进行检测。在1000 ~ 20000 e/m范围内检测到242个蛋白峰。总体而言,13个不同的质谱峰进行了生物信息学分析,并具有统计学意义(P <0.01)。M/Z1092.68和M/Z1508.26等7个明显的质谱峰在CRF组中高表达。M/Z13302.5和M/Z14330.7等6个明显的质谱峰在CRF组中呈低表达。采用模糊分组算法对CRF组与正常对照组进行模糊分组分析和主成分分析(PCA)。结果显示出歧视,但部分重叠。采用生物信息学方法,利用M/Z1049.61、M/Z1076.94、M/Z15295.7等7个不同质谱峰的生物标记物,对CRF的预测模型进行分析和建立。该预测模型可用于CRF组与正常对照组之间的样本分类(预测模型的灵敏度为61.4%;特异性为57.3%;预测正确率为64.4%)。综上所述,利用SELDI-TOF-MS技术初步筛选出了CRF舌苔蛋白标记物。所建立的预测模型为CRF诊断研究提供了客观依据。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Study on Proteinum in Clear Supernatant Liquid of Tongue Coating of Chronic Renal Failure Based on Protein Chip

This study aimed at screening proteinum markers of tongue coating related to chronic renal failure (CRF) and at establishing the predictive model by comparing differences of protein spectrum expression in clear supernatant liquid of tongue coating between CRF patients and normal controls in order to explore the significance of proteinum in clear supernatant liquid in the diagnosis of CRF. Clear supernatant liquid of tongue-coating samples collected from 67 CRF patients and 38 normal controls was used in the study. The proteinum markers of tongue coating were selected according to CRF with the SELDI-TOF-MS technique. The predictive model was established and verified by bioinformatics analysis. The tongue-coating samples of the 67 CRF patients and 38 normal samples in the control group were determined by the SELDI-TOF-MS technique. All 242 proteinum peaks were detected at 1000–20000 e/m. Overall, 13 distinct mass spectrum peaks were analyzed by bioinformatics and displayed statistical significance (P < 0.01). Seven distinct mass spectrum peaks, such as M/Z1092.68 and M/Z1508.26, show high expression in the CRF group. Six distinct mass spectrum peaks, such as M/Z13302.5 and M/Z14330.7, show low expression in the CRF group. Fuzzy-grouping algorithm was used in the fuzzy-grouping analysis and principal component analysis (PCA) between the CRF group and normal control group. The result showed discrimination, but was partly overlapping. The predictive model of CRF was analyzed and established by bioinformatics with biological markers that comprise seven distinct mass spectrum peaks, such as M/Z1049.61, M/Z1076.94, M/Z15295.7, and so on. The predictive model can be used in the sample classification between the CRF group and normal control group (The sensitivity of the predictive model is 61.4%; the specificity is 57.3%; and the predictive exactitude rate is 64.4%). It can be concluded that using the SELDI-TOF-MS technique, the proteinum markers of tongue coating of CRF have been preliminarily screened. The established predictive model provides objective evidence for the study on CRF diagnosis.

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