全面分析血清糖磷脂,发现肺癌诊断生物标志物,并揭示不同肺癌亚型中糖磷脂网络的变化。

IF 2.7 3区 化学 Q2 CHEMISTRY, ANALYTICAL
Ting Hu, Feifei Han, Zhuoling An
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引用次数: 0

摘要

糖磷脂是由低聚糖链与神经酰胺骨架共价连接而成的糖脂复合物,在肺癌的发生和转移中发挥着重要作用。本研究开发了一种超高效液相色谱-质谱联用(UHPLC-HRMS)方法,用于糖磷脂的全面分析,并构建了一个用于数据解读的内部库。研究人员对 31 名健康对照组(HCs)和 92 名不同病理亚型的肺癌患者的血清糖磷脂进行了分析。根据这种新方法,在人类血清中检测到了 1700 多种糖磷脂。在肺癌患者和健康对照组之间共发现了 567 种不同的糖磷脂(调整后 P < 0.05,折叠变化 > 2)。糖磷脂可作为诊断肺癌的潜在生物标记物,其灵敏度远高于传统的血清肿瘤标记物。鳞状细胞癌(Squa)中大多数糖磷脂的水平明显低于小细胞肺癌(SCLC)和腺癌(Aden)。在三种不同亚型的肺癌中,SCLC 患者的 Cer1P 丰度最高,这被认为与 SCLC 的高恶性度和高转移性有关。该研究建立了一个人工神经网络(ANN)模型,用于区分三种不同亚型的肺癌,准确率高于 93%。除了为肺癌的诊断和肺癌亚型的鉴别提供生物标志物和统计模型外,这项研究还揭示了糖磷脂网络在不同亚型肺癌中的变异,从而为研究肺癌的发病机制和探索治疗靶点提供了新的视角。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Comprehensive profiling of serum glycosphingolipids to discover the diagnostic biomarkers of lung cancer and uncover the variation of glycosphingolipid networks in different lung cancer subtypes.

Glycosphingolipids are glycolipid complexes formed by an oligosaccharide chain covalently linked to a ceramide backbone and play important roles in the occurrence and metastasis of lung cancer. In this study, an UHPLC-HRMS method was developed for the comprehensive profiling of glycosphingolipids, with an in-house library constructed for data interpretation. Serum glycosphingolipids were profiled in 31 healthy controls (HCs) and 92 lung cancer patients with different pathologic subtypes. Over 1700 glycosphingolipids were detected in human serum based on the novel method. A total of 567 differential glycosphingolipids (adjusted P < 0.05, and fold change > 2) were found between lung cancer patients and HCs. Glycosphingolipids can be used as potential biomarkers for lung cancer diagnosis, with sensitivity much higher than that of traditional serum tumor markers. The levels of most glycosphingolipids in squamous cell carcinoma (Squa) were significantly lower than those in small cell lung cancer (SCLC) and adenocarcinoma (Aden). The highest Cer1P abundance in SCLC patients among the three different subtypes of lung cancer was thought to be related to the high malignancy and metastasis of SCLC. An artificial neural network (ANN) model was constructed for the discrimination of the three different subtypes of lung cancer, with accuracy higher than 93%. Beyond providing biomarkers and statistical models for the diagnosis of lung cancer and discrimination of lung cancer subtypes, this study uncovered the variation of glycosphingolipid networks in different subtypes of lung cancer and thereby provided a novel insight to study the pathogenesis of lung cancer and explore therapeutic targets.

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来源期刊
Analytical Methods
Analytical Methods CHEMISTRY, ANALYTICAL-FOOD SCIENCE & TECHNOLOGY
CiteScore
5.10
自引率
3.20%
发文量
569
审稿时长
1.8 months
期刊介绍: Early applied demonstrations of new analytical methods with clear societal impact
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