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首页> 外文期刊>Journal of proteome research >Lung cancer serum biomarker discovery using glycoprotein capture and liquid chromatography mass spectrometry
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Lung cancer serum biomarker discovery using glycoprotein capture and liquid chromatography mass spectrometry

机译:利用糖蛋白捕获和液相色谱质谱法发现肺癌血清生物标志物

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摘要

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.
机译:由于众所周知的癌症中蛋白质糖基化模式的改变以及所得糖蛋白组的复杂性降低,靶向糖蛋白组学代表了进行外周血基于癌症的生物标志物发现的一种有吸引力的方法。在这里,我们将其应用于一组合并的非小细胞肺癌(NSCLC)病例血清(9例腺癌和6例来自54例患者的鳞状细胞癌)和匹配的对照库中,包括8个通过计算机断层扫描检测到的结节但非恶性的临床对照库通过对54位患者进行活检确定,并从106位无癌受试者中选出8个匹配的健康对照组。这项研究的目的是发现可以改善肺癌的早期发现和诊断的生物标志物。免疫亲和减法首先用于消耗最丰富的血清蛋白。然后将其余的血清蛋白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的肽。以前,N-连接的糖基化肽是通过肽-N-糖苷酶F(PNGase F)处理释放的,随后通过液相色谱(LC)-串联质谱(MS / MS)进行分析。一个MATLAB吗?基于内部工具的仪器被开发出来,以利于在不同LC-MS / MS运行中进行保留时间的比对,确定前体离子m / z值和洗脱曲线,并根据确定的肽段参数确定质谱图的积分。病例/对照库中来自22种不同蛋白质的38个糖肽显着不同(P <0.01,Student's t检验),其丰度导致基于分级聚类的病例库和对照库几乎完全分离。通过池中应用的市售ELISA验证了这些候选蛋白中三种的差异丰度。对于所有选定的糖蛋白,均观察到糖肽质谱图与ELISA测定的蛋白丰度之间存在强正相关。

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