Husi Holger, Skipworth Richard J E, Cronshaw Andrew, Stephens Nathan A, Wackerhage Henning, Greig Carolyn, Fearon Kenneth C H, Ross James A
Glasgow Cardiovascular Research Centre, University of Glasgow, Glasgow, UK.
School of Clinical Sciences, University of Edinburgh, Edinburgh, UK.
Proteomics Clin Appl. 2015 Jun;9(5-6):586-96. doi: 10.1002/prca.201400111. Epub 2015 May 8.
Cancer of the upper digestive tract (uGI) is a major contributor to cancer-related death worldwide. Due to a rise in occurrence, together with poor survival rates and a lack of diagnostic or prognostic clinical assays, there is a clear need to establish molecular biomarkers.
Initial assessment was performed on urine samples from 60 control and 60 uGI cancer patients using MS to establish a peak pattern or fingerprint model, which was validated by a further set of 59 samples.
We detected 86 cluster peaks by MS above frequency and detection thresholds. Statistical testing and model building resulted in a peak profiling model of five relevant peaks with 88% overall sensitivity and 91% specificity, and overall correctness of 90%. High-resolution MS of 40 samples in the 2-10 kDa range resulted in 646 identified proteins, and pattern matching identified four of the five model peaks within significant parameters, namely programmed cell death 6 interacting protein (PDCD6IP/Alix/AIP1), Rabenosyn-5 (ZFYVE20), protein S100A8, and protein S100A9, of which the first two were validated by Western blotting.
We demonstrate that MS analysis of human urine can identify lead biomarker candidates in uGI cancers, which makes this technique potentially useful in defining and consolidating biomarker patterns for uGI cancer screening.
上消化道(uGI)癌是全球癌症相关死亡的主要原因。由于发病率上升,加上生存率低以及缺乏诊断或预后临床检测方法,显然需要建立分子生物标志物。
使用质谱(MS)对60名对照者和60名uGI癌症患者的尿液样本进行初步评估,以建立峰模式或指纹模型,并通过另外59个样本进行验证。
我们通过MS在频率和检测阈值以上检测到86个聚类峰。统计测试和模型构建产生了一个由五个相关峰组成的峰谱模型,总体灵敏度为88%,特异性为91%,总体正确率为90%。对40个样本在2 - 10 kDa范围内进行高分辨率MS分析,鉴定出646种蛋白质,模式匹配在显著参数范围内鉴定出五个模型峰中的四个,即程序性细胞死亡6相互作用蛋白(PDCD6IP/Alix/AIP1)、拉贝诺辛-5(ZFYVE20)、蛋白质S100A8和蛋白质S100A9,其中前两个通过蛋白质印迹法得到验证。
我们证明,对人尿液进行MS分析可识别uGI癌症中的潜在生物标志物候选物,这使得该技术在定义和巩固uGI癌症筛查的生物标志物模式方面可能具有实用性。