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利用糖蛋白捕获和液相色谱-质谱联用技术发现肺癌血清生物标志物。

Lung cancer serum biomarker discovery using glycoprotein capture and liquid chromatography mass spectrometry.

机构信息

University of Pittsburgh Cancer Institute, Pittsburgh, Pennsylvania, USA.

出版信息

J Proteome Res. 2010 Dec 3;9(12):6440-9. doi: 10.1021/pr100696n. Epub 2010 Oct 28.

Abstract

Targeted glycoproteomics represents an attractive approach for conducting peripheral blood based cancer biomarker discovery due to the well-known altered pattern of protein glycosylation in cancer and the reduced complexity of the resultant glycoproteome. Here we report its application to a set of pooled nonsmall cell lung cancer (NSCLC) case sera (9 adenocarcinoma and 6 squamous cell carcinoma pools from 54 patients) and matched controls pools, including 8 clinical control pools with computed tomography detected nodules but being nonmalignant as determined by biopsy from 54 patients, and 8 matched healthy control pools from 106 cancer-free subjects. The goal of the study is to discover biomarkers that may enable improved early detection and diagnosis of lung cancer. Immunoaffinity subtraction was used to first deplete the topmost abundant serum proteins; the remaining serum proteins were then subjected to hydrazide chemistry based glycoprotein capture and enrichment. Hydrazide resin in situ trypsin digestion was used to release nonglycosylated peptides. Formerly N-linked glycosylated peptides were released by peptide-N-glycosidase F (PNGase F) treatment and were subsequently analyzed by liquid chromatography (LC)-tandem mass spectrometry (MS/MS). A MATLAB based in-house tool was developed to facilitate retention time alignment across different LC-MS/MS runs, determination of precursor ion m/z values and elution profiles, and the integration of mass chromatograms based on determined parameters for identified peptides. A total of 38 glycopeptides from 22 different proteins were significantly differentially abundant across the case/control pools (P < 0.01, Student's t test) and their abundances led to a near complete separation of case and control pools based on hierarchical clustering. The differential abundances of three of these candidate proteins were verified by commercially available ELISAs applied in the pools. Strong positive correlations between glycopeptide mass chromatograms and ELISA-measured protein abundance was observed for all of the selected glycoproteins.

摘要

靶向糖蛋白质组学代表了一种有吸引力的方法,可用于进行基于外周血的癌症生物标志物发现,这是由于癌症中蛋白质糖基化模式的改变以及所得糖蛋白质组的复杂性降低。在这里,我们报告了它在一组汇集的非小细胞肺癌(NSCLC)病例血清(来自 54 名患者的 9 个腺癌和 6 个鳞状细胞癌池)和匹配对照池中的应用,包括 8 个具有 CT 检测到的结节的临床对照池,但通过来自 54 名患者的活检确定为非恶性,以及 8 个来自 106 名无癌症患者的匹配健康对照组。该研究的目标是发现可能有助于改善肺癌早期检测和诊断的生物标志物。免疫亲和减法首先用于耗尽最丰富的血清蛋白;剩余的血清蛋白随后进行基于酰肼化学的糖蛋白捕获和富集。酰肼树脂原位胰蛋白酶消化用于释放非糖基化肽。以前通过肽-N-糖苷酶 F(PNGase F)处理释放 N-连接糖肽,然后通过液相色谱(LC)-串联质谱(MS/MS)进行分析。开发了一个基于 MATLAB 的内部工具,以促进不同 LC-MS/MS 运行之间的保留时间对齐,确定前体离子 m/z 值和洗脱曲线,并根据确定的参数整合基于确定的参数的质量色谱图。在病例/对照池中,共有 38 种糖肽来自 22 种不同的蛋白质存在显著差异(P <0.01,学生 t 检验),它们的丰度基于层次聚类导致病例和对照池的几乎完全分离。通过在池中的商业 ELISA 验证了这三种候选蛋白质的差异丰度。观察到所有选定糖蛋白的糖肽质量色谱图和 ELISA 测量的蛋白丰度之间存在强烈的正相关。

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