Identification of serum N-glycans signatures in three major gastrointestinal cancers by high-throughput N-glycome profiling.

IF 2.8 3区 医学 Q2 BIOCHEMICAL RESEARCH METHODS
Si Liu, Jianmin Huang, Yuanyuan Liu, Jiajing Lin, Haobo Zhang, Liming Cheng, Weimin Ye, Xin Liu
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引用次数: 0

Abstract

Background: Alternative N-glycosylation of serum proteins has been observed in colorectal cancer (CRC), esophageal squamous cell carcinoma (ESCC) and gastric cancer (GC), while comparative study among those three cancers has not been reported before. We aimed to identify serum N-glycans signatures and introduce a discriminative model across the gastrointestinal cancers.

Methods: The study population was initially screened according to the exclusion criteria process. Serum N-glycans profiling was characterized by a high-throughput assay based on matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS). Diagnostic model was built by random forest, and unsupervised machine learning was performed to illustrate the differentiation between the three major gastrointestinal (GI) cancers.

Results: We have found that three major gastrointestinal cancers strongly associated with significantly decreased mannosylation and mono-galactosylation, as well as increased sialylation of serum glycoproteins. A highly accurate discriminative power (> 0.90) for those gastrointestinal cancers was obtained with serum N-glycome based predictive model. Additionally, serum N-glycome profile exhibited distinct distributions across GI cancers, and several altered N-glycans were hyper-regulated in each specific disease.

Conclusions: Serum N-glycome profile was differentially expressed in three major gastrointestinal cancers, providing a new clinical tool for cancer diagnosis and throwing a light upon the disease-specific molecular signatures.

通过高通量n -糖谱分析鉴定三种主要胃肠道肿瘤的血清n -聚糖特征。
背景:在结直肠癌(CRC)、食管鳞状细胞癌(ESCC)和胃癌(GC)中均观察到血清蛋白n -糖基化的变化,但在这三种癌症之间的比较研究尚未见报道。我们的目的是鉴定血清n -聚糖的特征,并引入一个鉴别胃肠道癌症的模型。方法:根据排除标准流程对研究人群进行初步筛选。采用基于基质辅助激光解吸/电离飞行时间质谱(MALDI-TOF-MS)的高通量分析方法对血清n -聚糖谱进行了表征。通过随机森林建立诊断模型,并进行无监督机器学习来说明三种主要胃肠道(GI)癌症之间的区别。结果:我们发现三种主要胃肠道肿瘤与甘露糖基化和单半乳糖基化显著降低以及血清糖蛋白唾液基化升高密切相关。以血清n -糖为基础的预测模型对胃肠道肿瘤具有高度准确的判别能力(> 0.90)。此外,血清n -糖苷谱在GI癌症中表现出不同的分布,并且在每种特定疾病中,几种改变的n -糖苷被过度调节。结论:血清n -糖蛋白谱在三种主要胃肠道肿瘤中存在差异表达,为癌症诊断提供了新的临床工具,并揭示了疾病特异性分子特征。
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来源期刊
Clinical proteomics
Clinical proteomics BIOCHEMICAL RESEARCH METHODS-
CiteScore
5.80
自引率
2.60%
发文量
37
审稿时长
17 weeks
期刊介绍: Clinical Proteomics encompasses all aspects of translational proteomics. Special emphasis will be placed on the application of proteomic technology to all aspects of clinical research and molecular medicine. The journal is committed to rapid scientific review and timely publication of submitted manuscripts.
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