Liu Si, Huang Jianmin, Liu Yuanyuan, Lin Jiajing, Zhang Haobo, Cheng Liming, Ye Weimin, Liu Xin
Department of Epidemiology and Health Statistics, School of Public Health, Fujian Medical University, Fuzhou, 350122, China.
The Key Laboratory for Biomedical Photonics of MOE at Wuhan National Laboratory for Optoelectronics-Hubei Bioinformatics & Molecular Imaging Key Laboratory, Systems Biology Theme, Department of Biomedical Engineering, College of Life Science and Technology, Huazhong University of Science and Technology, No. 1037 Luoyudong Road, Hongshan District, Wuhan, 430074, China.
Clin Proteomics. 2024 Nov 28;21(1):64. doi: 10.1186/s12014-024-09516-2.
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.
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.
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.
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.
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的异常N-糖基化，而此前尚未有这三种癌症之间的比较研究报道。我们旨在识别血清N-聚糖特征，并引入一种针对胃肠道癌症的判别模型。
根据排除标准流程对研究人群进行初步筛选。血清N-聚糖谱通过基于基质辅助激光解吸/电离飞行时间质谱（MALDI-TOF-MS）的高通量检测进行表征。通过随机森林构建诊断模型，并进行无监督机器学习以阐明三种主要胃肠道（GI）癌症之间的差异。
我们发现三种主要胃肠道癌症与血清糖蛋白的甘露糖基化和单半乳糖基化显著降低以及唾液酸化增加密切相关。基于血清N-糖组的预测模型对这些胃肠道癌症具有高度准确的判别能力（>0.90）。此外，血清N-糖组谱在胃肠道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-聚糖被上调。
血清N-糖组谱在三种主要胃肠道癌症中差异表达，为癌症诊断提供了一种新的临床工具，并揭示了疾病特异性分子特征。
