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基于液相色谱-质谱联用的组织代谢组学分析揭示了肺癌的主要代谢途径改变和潜在的生物标志物。

Liquid Chromatography-Mass Spectrometry-Based Tissue Metabolic Profiling Reveals Major Metabolic Pathway Alterations and Potential Biomarkers of Lung Cancer.

机构信息

CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China.

University of Chinese Academy of Sciences, Beijing 100049, China.

出版信息

J Proteome Res. 2020 Sep 4;19(9):3750-3760. doi: 10.1021/acs.jproteome.0c00285. Epub 2020 Jul 31.

DOI:10.1021/acs.jproteome.0c00285
PMID:32693607
Abstract

Unclarified molecular mechanism and lack of practical diagnosis biomarkers hinder the effective treatment of non-small-cell lung cancer. Herein, we performed liquid chromatography-mass spectrometry-based nontargeted metabolomics analysis in 131 patients with their lung tissue pairs to study the metabolic characteristics and disordered metabolic pathways in lung tumor. A total of 339 metabolites were identified in metabolic profiling. Also, 241 differential metabolites were found between lung carcinoma tissues (LCTs) and paired distal noncancerous tissues; amino acids, purine metabolites, fatty acids, phospholipids, and most of lysophospholipids significantly increased in LCTs, while 3-phosphoglyceric acid, phosphoenolpyruvate, 6-phosphogluconate, and citrate decreased. Additionally, pathway enrichment analysis revealed that energy, purine, amino acid, lipid, and glutathione metabolism are markedly disturbed in lung cancer (LCa). Using binary logistic regression, we further defined candidate biomarkers for different subtypes of lung tumor. Xanthine and PC 35:2 were selected as combinational biomarkers for distinguishing benign from malignant lung tumors with a 0.886 area under curve (AUC) value, and creatine, myoinositol and LPE 16:0 were defined as combinational biomarkers for discriminating adenocarcinoma from squamous cell lung carcinoma with a 0.934 AUC value. Overall, metabolic characterization and pathway disturbance demonstrated apparent metabolic reprogramming in LCa. The defined candidate metabolite marker panels are useful for subtyping of lung tumors to assist clinical diagnosis.

摘要

不明的分子机制和缺乏实用的诊断生物标志物阻碍了非小细胞肺癌的有效治疗。在此,我们对 131 对患者的肺组织进行了基于液相色谱-质谱的非靶向代谢组学分析,以研究肺癌肿瘤中的代谢特征和紊乱的代谢途径。在代谢图谱中鉴定出了 339 种代谢物。此外,在肺癌组织(LCT)和配对的远端非癌组织之间发现了 241 种差异代谢物;LCT 中氨基酸、嘌呤代谢物、脂肪酸、磷脂和大多数溶血磷脂显著增加,而 3-磷酸甘油酸、磷酸烯醇丙酮酸、6-磷酸葡萄糖酸和柠檬酸则减少。此外,通路富集分析显示,能量、嘌呤、氨基酸、脂质和谷胱甘肽代谢在肺癌(LCa)中明显受到干扰。通过二元逻辑回归,我们进一步定义了不同类型肺癌的候选生物标志物。黄嘌呤和 PC 35:2 被选为区分良性和恶性肺肿瘤的组合生物标志物,曲线下面积(AUC)值为 0.886,肌氨酸、肌醇和 LPE 16:0 被定义为区分腺癌和鳞状细胞肺癌的组合生物标志物,AUC 值为 0.934。总的来说,代谢特征和通路紊乱表明 LCa 中存在明显的代谢重编程。定义的候选代谢标志物组合可用于辅助临床诊断的肺肿瘤分型。

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