通过脂质组学分析和机器学习鉴定食管鳞状细胞癌中的诊断标志物和脂质失调。
Identification of diagnostic markers and lipid dysregulation in oesophageal squamous cell carcinoma through lipidomic analysis and machine learning.
机构信息
Department of Pathology, School of Basic Medical Sciences, Institute of Systems Biomedicine, Peking-Tsinghua Center for Life Sciences, Peking University Health Science Center, Beijing, China.
State Key Laboratory of Molecular Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
出版信息
Br J Cancer. 2021 Aug;125(3):351-357. doi: 10.1038/s41416-021-01395-w. Epub 2021 May 5.
BACKGROUND
Oesophageal cancer (EC) ranks high in both morbidity and mortality. A non-invasive and high-sensitivity diagnostic approach is necessary to improve the prognosis of EC patients.
METHODS
A total of 525 serum samples were subjected to lipidomic analysis. We combined serum lipidomics and machine-learning algorithms to select important metabolite features for the detection of oesophageal squamous cell carcinoma (ESCC), the major subtype of EC in developing countries. A diagnostic model using a panel of selected features was developed and evaluated. Integrative analyses of tissue transcriptome and serum lipidome were conducted to reveal the underlying mechanism of lipid dysregulation.
RESULTS
Our optimised diagnostic model with a panel of 12 lipid biomarkers together with age and gender reaches a sensitivity of 90.7%, 91.3% and 90.7% and an area under receiver-operating characteristic curve of 0.958, 0.966 and 0.818 in detecting ESCC for the training cohort, validation cohort and independent validation cohort, respectively. Integrative analysis revealed matched variation trend of genes encoding key enzymes in lipid metabolism.
CONCLUSIONS
We have identified a panel of 12 lipid biomarkers for diagnostic modelling and potential mechanisms of lipid dysregulation in the serum of ESCC patients. This is a reliable, rapid and non-invasive tumour-diagnostic approach for clinical application.
背景
食管癌（EC）在发病率和死亡率方面都很高。需要一种非侵入性和高灵敏度的诊断方法来改善 EC 患者的预后。
方法
对 525 份血清样本进行脂质组学分析。我们结合血清脂质组学和机器学习算法，选择重要的代谢物特征来检测食管鳞状细胞癌（ESCC），这是发展中国家 EC 的主要亚型。开发并评估了使用选定特征组合的诊断模型。对组织转录组和血清脂质组进行综合分析，揭示脂质失调的潜在机制。
结果
我们优化的诊断模型，使用一组 12 种脂质生物标志物以及年龄和性别，在训练队列、验证队列和独立验证队列中检测 ESCC 的灵敏度分别为 90.7%、91.3%和 90.7%，接受者操作特征曲线下面积分别为 0.958、0.966 和 0.818。综合分析显示，脂质代谢关键酶编码基因的变化趋势相匹配。
结论
我们已经确定了一组 12 种脂质生物标志物用于诊断模型和 ESCC 患者血清中脂质失调的潜在机制。这是一种可靠、快速和非侵入性的肿瘤诊断方法，具有临床应用前景。
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