全面的血清脂质组学分析揭示了癌症的潜在生物标志物:一项病例对照研究。

IF 4.6 Q2 MATERIALS SCIENCE, BIOMATERIALS ACS Applied Bio Materials Pub Date : 2023-01-01 DOI:10.3233/CBM-220462
Bing Cao, Siyu Yang, Lailai Yan, Nan Li
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引用次数: 0

摘要

背景:癌症是世界范围内最常见的女性恶性肿瘤。在中国人群中进行癌症脂质组学研究的证据相对有限。目的:我们目前的研究旨在识别中国人群中能够区分患有和不患有恶性癌症的成年人的外周脂质,并探索与癌症乳腺癌相关的潜在脂质代谢途径。方法:采用Ultimate 3000 UHPLC系统和Q-Exactive HF-MS平台,对71例女性癌症患者和92例年龄匹配(±2岁)的健康女性的血清进行脂质组学研究。数据被上传到专门的在线软件MetabioAnalyst 5.0并由其进行处理。对潜在的生物标志物筛选进行了单变量和多变量分析。获得已鉴定的不同脂质的受试者工作特性(ROC)曲线下面积(AUCs),以评估其分类能力。结果:通过应用以下标准,共鉴定出47种显著不同的脂质:错误发现率调整后的P<0.05,投影中的变量重要性为1.0,倍数变化为2.0或0.5。其中,13种脂质被鉴定为曲线下面积(AUC)大于0.7的诊断生物标志物。多变量ROC曲线表明,2-47种脂质可以获得大于0.8的AUC。结论:使用基于非靶向LC-MS的代谢谱分析方法,我们的研究提供了初步证据,证明OxPC、PC、SM和TAG的广泛失调参与了癌症的病理过程。我们为进一步研究脂质改变在乳腺癌症病因学中的作用提供了线索。
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Comprehensive serum lipidomic analyses reveal potential biomarkers for malignant breast cancer: A case-control study.

Background: Breast cancer is the most worldwide commonly found malignancy among women. The evidence for lipidomic studies of breast cancer in the Chinese population is relatively limited.

Objective: Our current study aimed to identify peripheral lipids capable of distinguishing adults with and without malignant breast cancer in a Chinese population and to explore the potential lipid metabolism pathways implicated in breast cancer.

Methods: Lipidomics was performed with an Ultimate 3000 UHPLC system coupled with a Q-Exactive HF MS platform by using the serum of 71 female patients with malignant breast cancer and 92 age-matched (± 2 years) healthy women. The data were uploaded to and processed by the specialized online software Metaboanalyst 5.0. Both univariate and multivariate analyses were carried out for potential biomarker screening. Areas under the receiver-operating characteristic (ROC) curves (AUCs) of identified differential lipids were obtained for evaluating their classification capacity.

Results: A total of 47 significantly different lipids were identified by applying the following criteria: false discovery rate-adjusted P < 0.05, variable importance in projection ⩾ 1.0, and fold change ⩾ 2.0 or ⩽ 0.5. Among them, 13 lipids were identified as diagnostic biomarkers with the area under curve (AUC) greater than 0.7. Multivariate ROC curves indicated that AUCs greater than 0.8 could be achieved with 2-47 lipids.

Conclusions: Using an untargeted LC-MS-based metabolic profiling approach, our study provides preliminary evidence that extensive dysregulations of OxPCs, PCs, SMs and TAGs were involved in the pathological processes of breast cancer. We provided clues for furtherly investigating the role of lipid alterations in the pathoetiology of breast cancer.

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来源期刊
ACS Applied Bio Materials
ACS Applied Bio Materials Chemistry-Chemistry (all)
CiteScore
9.40
自引率
2.10%
发文量
464
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