Liu Si, Tu Chang, Zhang Haobo, Huang Hanhui, Liu Yuanyuan, Wang Yi, Cheng Liming, Liu Bi-Feng, Ning Kang, Liu Xin
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, Wuhan, 430074, China.
Department of Epidemiology and Health Statistics, School of Public Health, Fujian Medical University, Fuzhou, China.
J Ovarian Res. 2024 Jan 27;17(1):26. doi: 10.1186/s13048-024-01350-2.
Ovarian cancer (OC) is one of the most common gynecological tumors with high morbidity and mortality. Altered serum N-glycome has been observed in many diseases, while the association between serum protein N-glycosylation and OC progression remains unclear, particularly for the onset of carcinogenesis from benign neoplasms to cancer.
Herein, a mass spectrometry based high-throughput technique was applied to characterize serum N-glycome profile in individuals with healthy controls, benign neoplasms and different stages of OC. To elucidate the alterations of glycan features in OC progression, an orthogonal strategy with lectin-based ELISA was performed.
It was observed that the initiation and development of OC was associated with increased high-mannosylationand agalactosylation, concurrently with decreased total sialylation of serum, each of which gained at least moderately accurate merits. The most important individual N-glycans in each glycan group was H7N2, H3N5 and H5N4S2F1, respectively. Notably, serum N-glycome could be used to accurately discriminate OC patients from benign cohorts, with a comparable or even higher diagnostic score compared to CA125 and HE4. Furthermore, bioinformatics analysis based discriminative model verified the diagnostic performance of serum N-glycome for OC in two independent sets.
These findings demonstrated the great potential of serum N-glycome for OC diagnosis and precancerous lesion prediction, paving a new way for OC screening and monitoring.
卵巢癌(OC)是最常见的妇科肿瘤之一,发病率和死亡率都很高。在许多疾病中都观察到血清N-聚糖组发生改变,而血清蛋白N-糖基化与OC进展之间的关联仍不清楚,特别是从良性肿瘤到癌症的致癌过程起始阶段。
本文采用基于质谱的高通量技术来表征健康对照、良性肿瘤患者以及不同阶段OC患者的血清N-聚糖组图谱。为阐明OC进展过程中聚糖特征的变化,采用了基于凝集素的酶联免疫吸附测定(ELISA)的正交策略。
观察到OC的发生和发展与高甘露糖基化和去半乳糖基化增加有关,同时血清总唾液酸化减少,每一项都至少具有适度准确的特征。每个聚糖组中最重要的单个N-聚糖分别是H7N2、H3N5和H5N4S2F1。值得注意的是,血清N-聚糖组可用于准确区分OC患者和良性队列,与CA125和HE4相比,诊断评分相当甚至更高。此外,基于生物信息学分析的判别模型在两个独立数据集上验证了血清N-聚糖组对OC的诊断性能。
这些发现证明了血清N-聚糖组在OC诊断和癌前病变预测方面具有巨大潜力,为OC筛查和监测开辟了一条新途径。