A circulating extracellular vesicles-based novel screening tool for colorectal cancer revealed by shotgun and data-independent acquisition mass spectrometry
A circulating extracellular vesicles-based novel screening tool for colorectal cancer revealed by shotgun and data-independent acquisition mass spectrometry
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通过鸟枪法和数据独立的采集质谱法揭示了一种基于循环细胞外囊泡的结直肠癌新型筛查工具
DOI：
10.1080/20013078.2020.1750202
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发表时间：
2020-01-01
影响因子：
16
通讯作者：
Zheng, Shu
中科院分区：
文献类型：
--
作者：
Zheng, Xi;Xu, Kailun;Zheng, Shu
摘要背景：结直肠癌（CRC）的早期筛查对于改善其预后至关重要。由于低侵入性和高再现性，液体活检越来越多地被考虑用于诊断癌症。此外，表达肿瘤特异性蛋白的循环细胞外囊泡（crEV，从血浆中分离的细胞外囊泡）是各种癌症的潜在生物标志物。在这里，我们提出了一种基于数据独立采集（DIA）-质谱（MS）的液体活检诊断方法。研究方法：通过与蔗糖密度梯度离心偶联的过夜超离心，从人CRC细胞系的培养上清液和不同肿瘤阶段的CRC患者的血浆中分离细胞外囊泡（EV）。使用基于串联质量标签（TMT）的鸟枪蛋白质组学和磷酸化蛋白质组学对肿瘤特异性EV蛋白进行优先排序。使用蛋白质印迹法（WB）在第二个独立队列和小鼠荷瘤模型中验证结果。在第三个队列中通过DIA-MS进一步验证候选生物标志物。最后，加速DIA-MS方法，以允许在另一个CRC患者和健康对照的独立队列中高通量检测EV生物标志物。结果如下：crEV中高水平的总纤维连接蛋白1（FN 1）和磷酸化纤维连接蛋白1（FN 1）、结合珠蛋白（HP）、S100A9和纤维蛋白原α链（FGA）与癌症进展显著相关。FGA是最主要的生物标志物候选者。对人CRC细胞系和小鼠模型的分析表明，FGA + crEV可能由CRC细胞释放。此外，快速DIA-MS和平行反应监测（PRM）-MS均证实FGA + crEV可以区分结肠腺瘤（受试者操作特征（ROC）曲线中的曲线下面积（AUC）为0.949）和CRC患者（ROC的AUC为1.000）与健康个体。性能优于传统的肿瘤生物标志物。结论：DIA-MS检测FGA + crEVs是一种潜在的快速、无创的筛查工具，可用于早期结直肠癌的诊断。缩略语：FGA：纤维蛋白原α链;儿童权利委员会：结肠直肠癌; crEV：循环细胞外囊泡; EV：细胞外囊泡; MS：质谱法;白平衡：蛋白质印迹法; ROC：接收机工作特性; PRM：并行反应监测; GPC 1：磷脂酰肌醇蛋白聚糖-1;- 你好基因本体论;透射电镜：透射电子显微镜; FN1：纤连蛋白1; HP：结合珠蛋白; TMT：串联质量标记LC-MS/MS：液相色谱-串联质谱法; DIA：数据独立采集; DDA：数据依赖采集; CiRT：通用内部保留时间标准品; AGC：自动增益控制; AUC：曲线下面积。
ABSTRACT Background: Early screening for colorectal cancer (CRC) is essential to improve its prognosis. Liquid biopsies are increasingly being considered for diagnosing cancer due to low invasiveness and high reproducibility. In addition, circulating extracellular vesicles (crEVs, extracellular vesicles isolated from plasma) expressing tumour-specific proteins are potential biomarkers for various cancers. Here, we present a data-independent acquisition (DIA)-mass spectrometry (MS)-based diagnostic method for liquid biopsies. Methods: Extracellular vesicles (EVs) were isolated from culture supernatants of human CRC cell lines, and plasma of patients with CRC at different tumour stages, by overnight ultracentrifugation coupled with sucrose density gradient centrifugation. Tumour-specific EV proteins were prioritized using Tandem Mass Tag (TMT)-based shotgun proteomics and phosphoproteomics. The results were verified in a second independent cohort and a mouse tumour-bearing model using Western blotting (WB). The candidate biomarkers were further validated in a third cohort by DIA-MS. Finally, the DIA-MS methodology was accelerated to permit high-throughput detection of EV biomarkers in another independent cohort of patients with CRC and healthy controls. Results: High levels of total and phosphorylated fibronectin 1 (FN1) in crEVs, haptoglobin (HP), S100A9 and fibrinogen α chain (FGA) were significantly associated with cancer progression. FGA was the most dominant biomarker candidate. Analysis of the human CRC cell lines and the mouse model indicated that FGA+ crEVs were likely released by CRC cells. Furthermore, fast DIA-MS and parallel reaction monitoring (PRM)-MS both confirmed that FGA+ crEVs could distinguish colon adenoma with an area of curve (AUC) in the receiver operating characteristic (ROC) curve of 0.949 and patients with CRC (AUC of ROC is 1.000) from healthy individuals. The performance outperformed conventional tumour biomarkers. The DIA-MS quantification of FGA+ crEVs among three groups agreed with that from PRM-MS. Conclusion: DIA-MS detection of FGA+ crEVs is a potential rapid and non-invasive screening tool to identify early stage CRC. Abbreviations: FGA: fibrinogen α chain; CRC: colorectal cancer; crEVs: circulating extracellular vesicles; EV: extracellular vesicles;MS: mass spectrometry; WB: Western blotting; ROC: receiver operating characteristic; PRM: Parallel Reaction Monitoring; GPC1: Glypican-1; GO: Gene ontology; TEM: transmission electron microscopy; FN1: Fibronectin 1; HP: haptoglobin; TMT: Tandem Mass Tag; LC-MS/MS: liquid chromatography coupled to tandem mass spectrometry; DIA: data-independent acquisition; DDA: data-dependent acquisition; CiRT: Common internal Retention Time standards;AGC: Automatic gain control; AUC: area under curve.