Huang Zhenzhen, Zhang Shudi, Hang Wei, Chen Yuedong, Zheng Jiaxin, Li Wei, Xing Jinchun, Zhang Jie, Zhu Eryi, Yan Xiaomei
Department of Chemistry, MOE Key Lab of Spectrochemical Analysis & Instrumentation, College of Chemistry and Chemical Engineering, Xiamen University, China.
Department of Chemistry, MOE Key Lab of Spectrochemical Analysis & Instrumentation, College of Chemistry and Chemical Engineering, Xiamen University, China; State Key Laboratory of Marine Environmental Science, Xiamen University, China.
J Pharm Biomed Anal. 2014 Nov;100:175-183. doi: 10.1016/j.jpba.2014.07.028. Epub 2014 Aug 2.
Serum peptidomic approach was applied to investigate the peptidomic signature and discover the clinical biomarkers and biomarker patterns for RCC patients. The holistic orthogonal partial least-squares-discriminant analysis (OPLS-DA) based on qualified profile data successfully classified RCC patients from healthy controls, showing 100% sensitivity and specificity. Following critical criteria, several peptides presenting significant differences in serum level were picked out. The unsupervised hierarchical cluster analysis on those peptides was performed, showing 100% sensitivity and 93.3% specificity for RCC diagnosis regarding the present samples. Besides, receiver-operating characteristic (ROC) analysis was applied on single peptide biomarkers, with four peptides showing excellent predictive power. Among them, IYQLNSKLV and AGISMRSGDSPQD are reported for the first time for cancer detection.
采用血清肽组学方法研究肾细胞癌（RCC）患者的肽组学特征，并发现临床生物标志物和生物标志物模式。基于合格的谱数据进行的整体正交偏最小二乘判别分析（OPLS-DA）成功地将RCC患者与健康对照区分开来，灵敏度和特异性均为100%。按照关键标准，挑选出了几种血清水平存在显著差异的肽。对这些肽进行了无监督层次聚类分析，对于当前样本，RCC诊断的灵敏度为100%，特异性为93.3%。此外，对单个肽生物标志物进行了受试者操作特征（ROC）分析，有四种肽显示出优异的预测能力。其中，IYQLNSKLV和AGISMRSGDSPQD首次被报道可用于癌症检测。
